fix: head+tail truncation for execute_code stdout (inspired by openclaw context-pruning)

Previously, _drain() only captured the first MAX_STDOUT_BYTES (50KB) of
stdout, silently dropping all tail output. Scripts that print() their
final results at the end would have those results lost.

Now uses a two-buffer approach: 40% head + 60% tail (rolling window).
This matches the pattern already used in terminal_tool.py (line 1042-1051)
but gives the tail more space since execute_code scripts typically
print() their final results at the end.

Inspired by openclaw's softTrim context-pruning (headChars/tailChars).

.plans/openai-api-server.md

@@ -1,291 +0,0 @@
-# OpenAI-Compatible API Server for Hermes Agent
-
-## Motivation
-
-Every major chat frontend (Open WebUI 126k★, LobeChat 73k★, LibreChat 34k★,
-AnythingLLM 56k★, NextChat 87k★, ChatBox 39k★, Jan 26k★, HF Chat-UI 8k★,
-big-AGI 7k★) connects to backends via the OpenAI-compatible REST API with
-SSE streaming. By exposing this endpoint, hermes-agent becomes instantly
-usable as a backend for all of them — no custom adapters needed.
-
-## What It Enables
-
-```
-┌──────────────────┐
-│  Open WebUI      │──┐
-│  LobeChat        │  │    POST /v1/chat/completions
-│  LibreChat       │  ├──► Authorization: Bearer <key>     ┌─────────────────┐
-│  AnythingLLM     │  │    {"messages": [...]}             │  hermes-agent   │
-│  NextChat        │  │                                    │  gateway        │
-│  Any OAI client  │──┘    ◄── SSE streaming response      │  (API server)   │
-└──────────────────┘                                        └─────────────────┘
-```
-
-A user would:
-1. Set `API_SERVER_ENABLED=true` in `~/.hermes/.env`
-2. Run `hermes gateway` (API server starts alongside Telegram/Discord/etc.)
-3. Point Open WebUI (or any frontend) at `http://localhost:8642/v1`
-4. Chat with hermes-agent through any OpenAI-compatible UI
-
-## Endpoints
-
-| Method | Path | Purpose |
-|--------|------|---------|
-| POST | `/v1/chat/completions` | Chat with the agent (streaming + non-streaming) |
-| GET | `/v1/models` | List available "models" (returns hermes-agent as a model) |
-| GET | `/health` | Health check |
-
-## Architecture
-
-### Option A: Gateway Platform Adapter (recommended)
-
-Create `gateway/platforms/api_server.py` as a new platform adapter that
-extends `BasePlatformAdapter`. This is the cleanest approach because:
-
-- Reuses all gateway infrastructure (session management, auth, context building)
-- Runs in the same async loop as other adapters
-- Gets message handling, interrupt support, and session persistence for free
-- Follows the established pattern (like Telegram, Discord, etc.)
-- Uses `aiohttp.web` (already a dependency) for the HTTP server
-
-The adapter would start an `aiohttp.web.Application` server in `connect()`
-and route incoming HTTP requests through the standard `handle_message()` pipeline.
-
-### Option B: Standalone Component
-
-A separate HTTP server class in `gateway/api_server.py` that creates its own
-AIAgent instances directly. Simpler but duplicates session/auth logic.
-
-**Recommendation: Option A** — fits the existing architecture, less code to
-maintain, gets all gateway features for free.
-
-## Request/Response Format
-
-### Chat Completions (non-streaming)
-
-```
-POST /v1/chat/completions
-Authorization: Bearer hermes-api-key-here
-Content-Type: application/json
-
-{
-  "model": "hermes-agent",
-  "messages": [
-    {"role": "system", "content": "You are a helpful assistant."},
-    {"role": "user", "content": "What files are in the current directory?"}
-  ],
-  "stream": false,
-  "temperature": 0.7
-}
-```
-
-Response:
-```json
-{
-  "id": "chatcmpl-abc123",
-  "object": "chat.completion",
-  "created": 1710000000,
-  "model": "hermes-agent",
-  "choices": [{
-    "index": 0,
-    "message": {
-      "role": "assistant",
-      "content": "Here are the files in the current directory:\n..."
-    },
-    "finish_reason": "stop"
-  }],
-  "usage": {
-    "prompt_tokens": 50,
-    "completion_tokens": 200,
-    "total_tokens": 250
-  }
-}
-```
-
-### Chat Completions (streaming)
-
-Same request with `"stream": true`. Response is SSE:
-
-```
-data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"role":"assistant"},"finish_reason":null}]}
-
-data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"Here "},"finish_reason":null}]}
-
-data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"are "},"finish_reason":null}]}
-
-data: {"id":"chatcmpl-abc123","object":"chat.completion.chunk","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}
-
-data: [DONE]
-```
-
-### Models List
-
-```
-GET /v1/models
-Authorization: Bearer hermes-api-key-here
-```
-
-Response:
-```json
-{
-  "object": "list",
-  "data": [{
-    "id": "hermes-agent",
-    "object": "model",
-    "created": 1710000000,
-    "owned_by": "hermes-agent"
-  }]
-}
-```
-
-## Key Design Decisions
-
-### 1. Session Management
-
-The OpenAI API is stateless — each request includes the full conversation.
-But hermes-agent sessions have persistent state (memory, skills, tool context).
-
-**Approach: Hybrid**
-- Default: Stateless. Each request is independent. The `messages` array IS
-  the conversation. No session persistence between requests.
-- Opt-in persistent sessions via `X-Session-ID` header. When provided, the
-  server maintains session state across requests (conversation history,
-  memory context, tool state). This enables richer agent behavior.
-- The session ID also enables interrupt support — a subsequent request with
-  the same session ID while one is running triggers an interrupt.
-
-### 2. Streaming
-
-The agent's `run_conversation()` is synchronous and returns the full response.
-For real SSE streaming, we need to emit chunks as they're generated.
-
-**Phase 1 (MVP):** Run agent in a thread, return the complete response as
-a single SSE chunk + `[DONE]`. This works with all frontends — they just see
-a fast single-chunk response. Not true streaming but functional.
-
-**Phase 2:** Add a response callback to AIAgent that emits text chunks as the
-LLM generates them. The API server captures these via a queue and streams them
-as SSE events. This gives real token-by-token streaming.
-
-**Phase 3:** Stream tool execution progress too — emit tool call/result events
-as the agent works, giving frontends visibility into what the agent is doing.
-
-### 3. Tool Transparency
-
-Two modes:
-- **Opaque (default):** Frontends see only the final response. Tool calls
-  happen server-side and are invisible. Best for general-purpose UIs.
-- **Transparent (opt-in via header):** Tool calls are emitted as OpenAI-format
-  tool_call/tool_result messages in the stream. Useful for agent-aware frontends.
-
-### 4. Authentication
-
-- Bearer token via `Authorization: Bearer <key>` header
-- Token configured via `API_SERVER_KEY` env var
-- Optional: allow unauthenticated local-only access (127.0.0.1 bind)
-- Follows the same pattern as other platform adapters
-
-### 5. Model Mapping
-
-Frontends send `"model": "hermes-agent"` (or whatever). The actual LLM model
-used is configured server-side in config.yaml. The API server maps any
-requested model name to the configured hermes-agent model.
-
-Optionally, allow model passthrough: if the frontend sends
-`"model": "anthropic/claude-sonnet-4"`, the agent uses that model. Controlled
-by a config flag.
-
-## Configuration
-
-```yaml
-# In config.yaml
-api_server:
-  enabled: true
-  port: 8642
-  host: "127.0.0.1"        # localhost only by default
-  key: "your-secret-key"   # or via API_SERVER_KEY env var
-  allow_model_override: false  # let clients choose the model
-  max_concurrent: 5         # max simultaneous requests
-```
-
-Environment variables:
-```bash
-API_SERVER_ENABLED=true
-API_SERVER_PORT=8642
-API_SERVER_HOST=127.0.0.1
-API_SERVER_KEY=your-secret-key
-```
-
-## Implementation Plan
-
-### Phase 1: MVP (non-streaming) — PR
-
-1. `gateway/platforms/api_server.py` — new adapter
-   - aiohttp.web server with endpoints:
-     - `POST /v1/chat/completions` — Chat Completions API (universal compat)
-     - `POST /v1/responses` — Responses API (server-side state, tool preservation)
-     - `GET /v1/models` — list available models
-     - `GET /health` — health check
-   - Bearer token auth middleware
-   - Non-streaming responses (run agent, return full result)
-   - Chat Completions: stateless, messages array is the conversation
-   - Responses API: server-side conversation storage via previous_response_id
-     - Store full internal conversation (including tool calls) keyed by response ID
-     - On subsequent requests, reconstruct full context from stored chain
-   - Frontend system prompt layered on top of hermes-agent's core prompt
-
-2. `gateway/config.py` — add `Platform.API_SERVER` enum + config
-
-3. `gateway/run.py` — register adapter in `_create_adapter()`
-
-4. Tests in `tests/gateway/test_api_server.py`
-
-### Phase 2: SSE Streaming
-
-1. Add response streaming to both endpoints
-   - Chat Completions: `choices[0].delta.content` SSE format
-   - Responses API: semantic events (response.output_text.delta, etc.)
-   - Run agent in thread, collect output via callback queue
-   - Handle client disconnect (cancel agent)
-
-2. Add `stream_callback` parameter to `AIAgent.run_conversation()`
-
-### Phase 3: Enhanced Features
-
-1. Tool call transparency mode (opt-in)
-2. Model passthrough/override
-3. Concurrent request limiting
-4. Usage tracking / rate limiting
-5. CORS headers for browser-based frontends
-6. GET /v1/responses/{id} — retrieve stored response
-7. DELETE /v1/responses/{id} — delete stored response
-
-## Files Changed
-
-| File | Change |
-|------|--------|
-| `gateway/platforms/api_server.py` | NEW — main adapter (~300 lines) |
-| `gateway/config.py` | Add Platform.API_SERVER + config (~20 lines) |
-| `gateway/run.py` | Register adapter in _create_adapter() (~10 lines) |
-| `tests/gateway/test_api_server.py` | NEW — tests (~200 lines) |
-| `cli-config.yaml.example` | Add api_server section |
-| `README.md` | Mention API server in platform list |
-
-## Compatibility Matrix
-
-Once implemented, hermes-agent works as a drop-in backend for:
-
-| Frontend | Stars | How to Connect |
-|----------|-------|---------------|
-| Open WebUI | 126k | Settings → Connections → Add OpenAI API, URL: `http://localhost:8642/v1` |
-| NextChat | 87k | BASE_URL env var |
-| LobeChat | 73k | Custom provider endpoint |
-| AnythingLLM | 56k | LLM Provider → Generic OpenAI |
-| Oobabooga | 42k | Already a backend, not a frontend |
-| ChatBox | 39k | API Host setting |
-| LibreChat | 34k | librechat.yaml custom endpoint |
-| Chatbot UI | 29k | Custom API endpoint |
-| Jan | 26k | Remote model config |
-| AionUI | 18k | Custom API endpoint |
-| HF Chat-UI | 8k | OPENAI_BASE_URL env var |
-| big-AGI | 7k | Custom endpoint |

.plans/streaming-support.md

@@ -1,705 +0,0 @@
-# Streaming LLM Response Support for Hermes Agent
-
-## Overview
-
-Add token-by-token streaming of LLM responses across all platforms. When enabled,
-users see the response typing out live instead of waiting for the full generation.
-Streaming is opt-in via config, defaults to off, and all existing non-streaming
-code paths remain intact as the default.
-
-## Design Principles
-
-1. **Feature-flagged**: `streaming.enabled: true` in config.yaml. Off by default.
-   When off, all existing code paths are unchanged — zero risk to current behavior.
-2. **Callback-based**: A simple `stream_callback(text_delta: str)` function injected
-   into AIAgent. The agent doesn't know or care what the consumer does with tokens.
-3. **Graceful degradation**: If the provider doesn't support streaming, or streaming
-   fails for any reason, silently fall back to the non-streaming path.
-4. **Platform-agnostic core**: The streaming mechanism in AIAgent works the same
-   regardless of whether the consumer is CLI, Telegram, Discord, or the API server.
-
----
-
-## Architecture
-
-```
-                              stream_callback(delta)
-                                    │
-  ┌─────────────┐    ┌─────────────▼──────────────┐
-  │  LLM API    │    │      queue.Queue()          │
-  │  (stream)   │───►│  thread-safe bridge between │
-  │             │    │  agent thread & consumer    │
-  └─────────────┘    └─────────────┬──────────────┘
-                                   │
-                    ┌──────────────┼──────────────┐
-                    │              │              │
-              ┌─────▼─────┐ ┌─────▼─────┐ ┌─────▼─────┐
-              │    CLI     │ │  Gateway  │ │ API Server│
-              │ print to   │ │ edit msg  │ │ SSE event │
-              │ terminal   │ │ on Tg/Dc  │ │ to client │
-              └───────────┘ └───────────┘ └───────────┘
-```
-
-The agent runs in a thread. The callback puts tokens into a thread-safe queue.
-Each consumer reads the queue in its own context (async task, main thread, etc.).
-
----
-
-## Configuration
-
-### config.yaml
-
-```yaml
-streaming:
-  enabled: false          # Master switch. Default off.
-  # Per-platform overrides (optional):
-  # cli: true             # Override for CLI only
-  # telegram: true        # Override for Telegram only
-  # discord: false        # Keep Discord non-streaming
-  # api_server: true      # Override for API server
-```
-
-### Environment variables
-
-```
-HERMES_STREAMING_ENABLED=true    # Master switch via env
-```
-
-### How the flag is read
-
-- **CLI**: `load_cli_config()` reads `streaming.enabled`, sets env var. AIAgent
-  checks at init time.
-- **Gateway**: `_run_agent()` reads config, decides whether to pass
-  `stream_callback` to the AIAgent constructor.
-- **API server**: For Chat Completions `stream=true` requests, always uses streaming
-  regardless of config (the client is explicitly requesting it). For non-stream
-  requests, uses config.
-
-### Precedence
-
-1. API server: client's `stream` field overrides everything
-2. Per-platform config override (e.g., `streaming.telegram: true`)
-3. Master `streaming.enabled` flag
-4. Default: off
-
----
-
-## Implementation Plan
-
-### Phase 1: Core streaming infrastructure in AIAgent
-
-**File: run_agent.py**
-
-#### 1a. Add stream_callback parameter to __init__ (~5 lines)
-
-```python
-def __init__(self, ..., stream_callback: callable = None, ...):
-    self.stream_callback = stream_callback
-```
-
-No other init changes. The callback is optional — when None, everything
-works exactly as before.
-
-#### 1b. Add _run_streaming_chat_completion() method (~65 lines)
-
-New method for Chat Completions API streaming:
-
-```python
-def _run_streaming_chat_completion(self, api_kwargs: dict):
-    """Stream a chat completion, emitting text tokens via stream_callback.
-    
-    Returns a fake response object compatible with the non-streaming code path.
-    Falls back to non-streaming on any error.
-    """
-    stream_kwargs = dict(api_kwargs)
-    stream_kwargs["stream"] = True
-    stream_kwargs["stream_options"] = {"include_usage": True}
-    
-    accumulated_content = []
-    accumulated_tool_calls = {}  # index -> {id, name, arguments}
-    final_usage = None
-    
-    try:
-        stream = self.client.chat.completions.create(**stream_kwargs)
-        
-        for chunk in stream:
-            if not chunk.choices:
-                # Usage-only chunk (final)
-                if chunk.usage:
-                    final_usage = chunk.usage
-                continue
-            
-            delta = chunk.choices[0].delta
-            
-            # Text content — emit via callback
-            if delta.content:
-                accumulated_content.append(delta.content)
-                if self.stream_callback:
-                    try:
-                        self.stream_callback(delta.content)
-                    except Exception:
-                        pass
-            
-            # Tool call deltas — accumulate silently
-            if delta.tool_calls:
-                for tc_delta in delta.tool_calls:
-                    idx = tc_delta.index
-                    if idx not in accumulated_tool_calls:
-                        accumulated_tool_calls[idx] = {
-                            "id": tc_delta.id or "",
-                            "name": "", "arguments": ""
-                        }
-                    if tc_delta.function:
-                        if tc_delta.function.name:
-                            accumulated_tool_calls[idx]["name"] = tc_delta.function.name
-                        if tc_delta.function.arguments:
-                            accumulated_tool_calls[idx]["arguments"] += tc_delta.function.arguments
-        
-        # Build fake response compatible with existing code
-        tool_calls = []
-        for idx in sorted(accumulated_tool_calls):
-            tc = accumulated_tool_calls[idx]
-            if tc["name"]:
-                tool_calls.append(SimpleNamespace(
-                    id=tc["id"], type="function",
-                    function=SimpleNamespace(name=tc["name"], arguments=tc["arguments"]),
-                ))
-        
-        return SimpleNamespace(
-            choices=[SimpleNamespace(
-                message=SimpleNamespace(
-                    content="".join(accumulated_content) or "",
-                    tool_calls=tool_calls or None,
-                    role="assistant",
-                ),
-                finish_reason="tool_calls" if tool_calls else "stop",
-            )],
-            usage=final_usage,
-            model=self.model,
-        )
-    
-    except Exception as e:
-        logger.debug("Streaming failed, falling back to non-streaming: %s", e)
-        return self.client.chat.completions.create(**api_kwargs)
-```
-
-#### 1c. Modify _run_codex_stream() for Responses API (~10 lines)
-
-The method already iterates the stream. Add callback emission:
-
-```python
-def _run_codex_stream(self, api_kwargs: dict):
-    with self.client.responses.stream(**api_kwargs) as stream:
-        for event in stream:
-            # Emit text deltas if streaming callback is set
-            if self.stream_callback and hasattr(event, 'type'):
-                if event.type == 'response.output_text.delta':
-                    try:
-                        self.stream_callback(event.delta)
-                    except Exception:
-                        pass
-        return stream.get_final_response()
-```
-
-#### 1d. Modify _interruptible_api_call() (~5 lines)
-
-Add the streaming branch:
-
-```python
-def _call():
-    try:
-        if self.api_mode == "codex_responses":
-            result["response"] = self._run_codex_stream(api_kwargs)
-        elif self.stream_callback is not None:
-            result["response"] = self._run_streaming_chat_completion(api_kwargs)
-        else:
-            result["response"] = self.client.chat.completions.create(**api_kwargs)
-    except Exception as e:
-        result["error"] = e
-```
-
-#### 1e. Signal end-of-stream to consumers (~5 lines)
-
-After the API call returns, signal the callback that streaming is done
-so consumers can finalize (remove cursor, close SSE, etc.):
-
-```python
-# In run_conversation(), after _interruptible_api_call returns:
-if self.stream_callback:
-    try:
-        self.stream_callback(None)  # None = end of stream signal
-    except Exception:
-        pass
-```
-
-Consumers check: `if delta is None: finalize()`
-
-**Tests for Phase 1:** (~150 lines)
-- Test _run_streaming_chat_completion with mocked stream
-- Test fallback to non-streaming on error
-- Test tool_call accumulation during streaming
-- Test stream_callback receives correct deltas
-- Test None signal at end of stream
-- Test streaming disabled when callback is None
-
----
-
-### Phase 2: Gateway consumers (Telegram, Discord, etc.)
-
-**File: gateway/run.py**
-
-#### 2a. Read streaming config (~15 lines)
-
-In `_run_agent()`, before creating the AIAgent:
-
-```python
-# Read streaming config
-_streaming_enabled = False
-try:
-    # Check per-platform override first
-    platform_key = source.platform.value if source.platform else ""
-    _stream_cfg = {}  # loaded from config.yaml streaming section
-    if _stream_cfg.get(platform_key) is not None:
-        _streaming_enabled = bool(_stream_cfg[platform_key])
-    else:
-        _streaming_enabled = bool(_stream_cfg.get("enabled", False))
-except Exception:
-    pass
-# Env var override
-if os.getenv("HERMES_STREAMING_ENABLED", "").lower() in ("true", "1", "yes"):
-    _streaming_enabled = True
-```
-
-#### 2b. Set up queue + callback (~15 lines)
-
-```python
-_stream_q = None
-_stream_done = None
-_stream_msg_id = [None]  # mutable ref for the async task
-
-if _streaming_enabled:
-    import queue as _q
-    _stream_q = _q.Queue()
-    _stream_done = threading.Event()
-    
-    def _on_token(delta):
-        if delta is None:
-            _stream_done.set()
-        else:
-            _stream_q.put(delta)
-```
-
-Pass `stream_callback=_on_token` to the AIAgent constructor.
-
-#### 2c. Telegram/Discord stream preview task (~50 lines)
-
-```python
-async def stream_preview():
-    """Progressively edit a message with streaming tokens."""
-    if not _stream_q:
-        return
-    adapter = self.adapters.get(source.platform)
-    if not adapter:
-        return
-    
-    accumulated = []
-    token_count = 0
-    last_edit = 0.0
-    MIN_TOKENS = 20          # Don't show until enough context
-    EDIT_INTERVAL = 1.5      # Respect Telegram rate limits
-    
-    try:
-        while not _stream_done.is_set():
-            try:
-                chunk = _stream_q.get(timeout=0.1)
-                accumulated.append(chunk)
-                token_count += 1
-            except queue.Empty:
-                continue
-            
-            now = time.monotonic()
-            if token_count >= MIN_TOKENS and (now - last_edit) >= EDIT_INTERVAL:
-                preview = "".join(accumulated) + " ▌"
-                if _stream_msg_id[0] is None:
-                    r = await adapter.send(
-                        chat_id=source.chat_id,
-                        content=preview,
-                        metadata=_thread_metadata,
-                    )
-                    if r.success and r.message_id:
-                        _stream_msg_id[0] = r.message_id
-                else:
-                    await adapter.edit_message(
-                        chat_id=source.chat_id,
-                        message_id=_stream_msg_id[0],
-                        content=preview,
-                    )
-                last_edit = now
-        
-        # Drain remaining tokens
-        while not _stream_q.empty():
-            accumulated.append(_stream_q.get_nowait())
-        
-        # Final edit — remove cursor, show complete text
-        if _stream_msg_id[0] and accumulated:
-            await adapter.edit_message(
-                chat_id=source.chat_id,
-                message_id=_stream_msg_id[0],
-                content="".join(accumulated),
-            )
-    
-    except asyncio.CancelledError:
-        # Clean up on cancel
-        if _stream_msg_id[0] and accumulated:
-            try:
-                await adapter.edit_message(
-                    chat_id=source.chat_id,
-                    message_id=_stream_msg_id[0],
-                    content="".join(accumulated),
-                )
-            except Exception:
-                pass
-    except Exception as e:
-        logger.debug("stream_preview error: %s", e)
-```
-
-#### 2d. Skip final send if already streamed (~10 lines)
-
-In `_process_message_background()` (base.py), after getting the response,
-if streaming was active and `_stream_msg_id[0]` is set, the final response
-was already delivered via progressive edits. Skip the normal `self.send()`
-call to avoid duplicating the message.
-
-This is the most delicate integration point — we need to communicate from
-the gateway's `_run_agent` back to the base adapter's response sender that
-the response was already delivered. Options:
-
-- **Option A**: Return a special marker in the result dict:
-  `result["_streamed_msg_id"] = _stream_msg_id[0]`
-  The base adapter checks this and skips `send()`.
-  
-- **Option B**: Edit the already-sent message with the final response
-  (which may differ slightly from accumulated tokens due to think-block
-  stripping, etc.) and don't send a new one.
-
-- **Option C**: The stream preview task handles the FULL final response
-  (including any post-processing), and the handler returns None to skip
-  the normal send path.
-
-Recommended: **Option A** — cleanest separation. The result dict already
-carries metadata; adding one more field is low-risk.
-
-**Platform-specific considerations:**
-
-| Platform | Edit support | Rate limits | Streaming approach |
-|----------|-------------|-------------|-------------------|
-| Telegram | ✅ edit_message_text | ~20 edits/min | Edit every 1.5s |
-| Discord | ✅ message.edit | 5 edits/5s per message | Edit every 1.2s |
-| Slack | ✅ chat.update | Tier 3 (~50/min) | Edit every 1.5s |
-| WhatsApp | ❌ no edit support | N/A | Skip streaming, use normal path |
-| HomeAssistant | ❌ no edit | N/A | Skip streaming |
-| API Server | ✅ SSE native | No limit | Real SSE events |
-
-WhatsApp and HomeAssistant fall back to non-streaming automatically because
-they don't support message editing.
-
-**Tests for Phase 2:** (~100 lines)
-- Test stream_preview sends/edits correctly
-- Test skip-final-send when streaming delivered
-- Test WhatsApp/HA graceful fallback
-- Test streaming disabled per-platform config
-- Test thread_id metadata forwarded in stream messages
-
----
-
-### Phase 3: CLI streaming
-
-**File: cli.py**
-
-#### 3a. Set up callback in the CLI chat loop (~20 lines)
-
-In `_chat_once()` or wherever the agent is invoked:
-
-```python
-if streaming_enabled:
-    _stream_q = queue.Queue()
-    _stream_done = threading.Event()
-    
-    def _cli_stream_callback(delta):
-        if delta is None:
-            _stream_done.set()
-        else:
-            _stream_q.put(delta)
-    
-    agent.stream_callback = _cli_stream_callback
-```
-
-#### 3b. Token display thread/task (~30 lines)
-
-Start a thread that reads the queue and prints tokens:
-
-```python
-def _stream_display():
-    """Print tokens to terminal as they arrive."""
-    first_token = True
-    while not _stream_done.is_set():
-        try:
-            delta = _stream_q.get(timeout=0.1)
-        except queue.Empty:
-            continue
-        if first_token:
-            # Print response box top border
-            _cprint(f"\n{top}")
-            first_token = False
-        sys.stdout.write(delta)
-        sys.stdout.flush()
-    # Drain remaining
-    while not _stream_q.empty():
-        sys.stdout.write(_stream_q.get_nowait())
-    sys.stdout.flush()
-    # Print bottom border
-    _cprint(f"\n\n{bot}")
-```
-
-**Integration challenge: prompt_toolkit**
-
-The CLI uses prompt_toolkit which controls the terminal. Writing directly
-to stdout while prompt_toolkit is active can cause display corruption.
-The existing KawaiiSpinner already solves this by using prompt_toolkit's
-`patch_stdout` context. The streaming display would need to do the same.
-
-Alternative: use `_cprint()` for each token chunk (routes through
-prompt_toolkit's renderer). But this might be slow for individual tokens.
-
-Recommended approach: accumulate tokens in small batches (e.g., every 50ms)
-and `_cprint()` the batch. This balances display responsiveness with
-prompt_toolkit compatibility.
-
-**Tests for Phase 3:** (~50 lines)
-- Test CLI streaming callback setup
-- Test response box borders with streaming
-- Test fallback when streaming disabled
-
----
-
-### Phase 4: API Server real streaming
-
-**File: gateway/platforms/api_server.py**
-
-Replace the pseudo-streaming `_write_sse_chat_completion()` with real
-token-by-token SSE when the agent supports it.
-
-#### 4a. Wire streaming callback for stream=true requests (~20 lines)
-
-```python
-if stream:
-    _stream_q = queue.Queue()
-    
-    def _api_stream_callback(delta):
-        _stream_q.put(delta)  # None = done
-    
-    # Pass callback to _run_agent
-    result, usage = await self._run_agent(
-        ..., stream_callback=_api_stream_callback,
-    )
-```
-
-#### 4b. Real SSE writer (~40 lines)
-
-```python
-async def _write_real_sse(self, request, completion_id, model, stream_q):
-    response = web.StreamResponse(
-        headers={"Content-Type": "text/event-stream", "Cache-Control": "no-cache"},
-    )
-    await response.prepare(request)
-    
-    # Role chunk
-    await response.write(...)
-    
-    # Stream content chunks as they arrive
-    while True:
-        try:
-            delta = await asyncio.get_event_loop().run_in_executor(
-                None, lambda: stream_q.get(timeout=0.1)
-            )
-        except queue.Empty:
-            continue
-        
-        if delta is None:  # End of stream
-            break
-        
-        chunk = {"id": completion_id, "object": "chat.completion.chunk", ...
-                 "choices": [{"delta": {"content": delta}, ...}]}
-        await response.write(f"data: {json.dumps(chunk)}\n\n".encode())
-    
-    # Finish + [DONE]
-    await response.write(...)
-    await response.write(b"data: [DONE]\n\n")
-    return response
-```
-
-**Challenge: concurrent execution**
-
-The agent runs in a thread executor. SSE writing happens in the async event
-loop. The queue bridges them. But `_run_agent()` currently awaits the full
-result before returning. For real streaming, we need to start the agent in
-the background and stream tokens while it runs:
-
-```python
-# Start agent in background
-agent_task = asyncio.create_task(self._run_agent_async(...))
-
-# Stream tokens while agent runs
-await self._write_real_sse(request, ..., stream_q)
-
-# Agent is done by now (stream_q received None)
-result, usage = await agent_task
-```
-
-This requires splitting `_run_agent` into an async version that doesn't
-block waiting for the result, or running it in a separate task.
-
-**Responses API SSE format:**
-
-For `/v1/responses` with `stream=true`, the SSE events are different:
-
-```
-event: response.output_text.delta
-data: {"type":"response.output_text.delta","delta":"Hello"}
-
-event: response.completed  
-data: {"type":"response.completed","response":{...}}
-```
-
-This needs a separate SSE writer that emits Responses API format events.
-
-**Tests for Phase 4:** (~80 lines)
-- Test real SSE streaming with mocked agent
-- Test SSE event format (Chat Completions vs Responses)
-- Test client disconnect during streaming
-- Test fallback to pseudo-streaming when callback not available
-
----
-
-## Integration Issues & Edge Cases
-
-### 1. Tool calls during streaming
-
-When the model returns tool calls instead of text, no text tokens are emitted.
-The stream_callback is simply never called with text. After tools execute, the
-next API call may produce the final text response — streaming picks up again.
-
-The stream preview task needs to handle this: if no tokens arrive during a
-tool-call round, don't send/edit any message. The tool progress messages
-continue working as before.
-
-### 2. Duplicate messages
-
-The biggest risk: the agent sends the final response normally (via the
-existing send path) AND the stream preview already showed it. The user
-sees the response twice.
-
-Prevention: when streaming is active and tokens were delivered, the final
-response send must be suppressed. The `result["_streamed_msg_id"]` marker
-tells the base adapter to skip its normal send.
-
-### 3. Response post-processing
-
-The final response may differ from the accumulated streamed tokens:
-- Think block stripping (`<think>...</think>` removed)
-- Trailing whitespace cleanup
-- Tool result media tag appending
-
-The stream preview shows raw tokens. The final edit should use the
-post-processed version. This means the final edit (removing the cursor)
-should use the post-processed `final_response`, not just the accumulated
-stream text.
-
-### 4. Context compression during streaming
-
-If the agent triggers context compression mid-conversation, the streaming
-tokens from BEFORE compression are from a different context than those
-after. This isn't a problem in practice — compression happens between
-API calls, not during streaming.
-
-### 5. Interrupt during streaming
-
-User sends a new message while streaming → interrupt. The stream is killed
-(HTTP connection closed), accumulated tokens are shown as-is (no cursor),
-and the interrupt message is processed normally. This is already handled by
-`_interruptible_api_call` closing the client.
-
-### 6. Multi-model / fallback
-
-If the primary model fails and the agent falls back to a different model,
-streaming state resets. The fallback call may or may not support streaming.
-The graceful fallback in `_run_streaming_chat_completion` handles this.
-
-### 7. Rate limiting on edits
-
-Telegram: ~20 edits/minute (~1 every 3 seconds to be safe)
-Discord: 5 edits per 5 seconds per message
-Slack: ~50 API calls/minute
-
-The 1.5s edit interval is conservative enough for all platforms. If we get
-429 rate limit errors on edits, just skip that edit cycle and try next time.
-
----
-
-## Files Changed Summary
-
-| File | Phase | Changes |
-|------|-------|---------|
-| `run_agent.py` | 1 | +stream_callback param, +_run_streaming_chat_completion(), modify _run_codex_stream(), modify _interruptible_api_call() |
-| `gateway/run.py` | 2 | +streaming config reader, +queue/callback setup, +stream_preview task, +skip-final-send logic |
-| `gateway/platforms/base.py` | 2 | +check for _streamed_msg_id in response handler |
-| `cli.py` | 3 | +streaming setup, +token display, +response box integration |
-| `gateway/platforms/api_server.py` | 4 | +real SSE writer, +streaming callback wiring |
-| `hermes_cli/config.py` | 1 | +streaming config defaults |
-| `cli-config.yaml.example` | 1 | +streaming section |
-| `tests/test_streaming.py` | 1-4 | NEW — ~380 lines of tests |
-
-**Total new code**: ~500 lines across all phases
-**Total test code**: ~380 lines
-
----
-
-## Rollout Plan
-
-1. **Phase 1** (core): Merge to main. Streaming disabled by default.
-   Zero impact on existing behavior. Can be tested with env var.
-
-2. **Phase 2** (gateway): Merge to main. Test on Telegram manually.
-   Enable per-platform: `streaming.telegram: true` in config.
-
-3. **Phase 3** (CLI): Merge to main. Test in terminal.
-   Enable: `streaming.cli: true` or `streaming.enabled: true`.
-
-4. **Phase 4** (API server): Merge to main. Test with Open WebUI.
-   Auto-enabled when client sends `stream: true`.
-
-Each phase is independently mergeable and testable. Streaming stays
-off by default throughout. Once all phases are stable, consider
-changing the default to enabled.
-
----
-
-## Config Reference (final state)
-
-```yaml
-# config.yaml
-streaming:
-  enabled: false          # Master switch (default: off)
-  cli: true               # Per-platform override
-  telegram: true
-  discord: true
-  slack: true
-  api_server: true        # API server always streams when client requests it
-  edit_interval: 1.5      # Seconds between message edits (default: 1.5)
-  min_tokens: 20          # Tokens before first display (default: 20)
-```
-
-```bash
-# Environment variable override
-HERMES_STREAMING_ENABLED=true
-```

AGENTS.md

@@ -31,8 +31,7 @@ hermes-agent/
 │   ├── config.py         # DEFAULT_CONFIG, OPTIONAL_ENV_VARS, migration
 │   ├── commands.py       # Slash command definitions + SlashCommandCompleter
 │   ├── callbacks.py      # Terminal callbacks (clarify, sudo, approval)
-│   ├── setup.py          # Interactive setup wizard
-│   └── skin_engine.py    # Skin/theme engine — CLI visual customization
+│   └── setup.py          # Interactive setup wizard
 ├── tools/                # Tool implementations (one file per tool)
 │   ├── registry.py       # Central tool registry (schemas, handlers, dispatch)
 │   ├── approval.py       # Dangerous command detection
@@ -122,7 +121,6 @@ Messages follow OpenAI format: `{"role": "system/user/assistant/tool", ...}`. Re
 - **Rich** for banner/panels, **prompt_toolkit** for input with autocomplete
 - **KawaiiSpinner** (`agent/display.py`) — animated faces during API calls, `┊` activity feed for tool results
 - `load_cli_config()` in cli.py merges hardcoded defaults + user config YAML
-- **Skin engine** (`hermes_cli/skin_engine.py`) — data-driven CLI theming; initialized from `display.skin` config key at startup; skins customize banner colors, spinner faces/verbs/wings, tool prefix, response box, branding text
 - `process_command()` is a method on `HermesCLI` (not in commands.py)
 - Skill slash commands: `agent/skill_commands.py` scans `~/.hermes/skills/`, injects as **user message** (not system prompt) to preserve prompt caching
 
@@ -197,94 +195,6 @@ The registry handles schema collection, dispatch, availability checking, and err
 
 ---
 
-## Skin/Theme System
-
-The skin engine (`hermes_cli/skin_engine.py`) provides data-driven CLI visual customization. Skins are **pure data** — no code changes needed to add a new skin.
-
-### Architecture
-
-```
-hermes_cli/skin_engine.py    # SkinConfig dataclass, built-in skins, YAML loader
-~/.hermes/skins/*.yaml       # User-installed custom skins (drop-in)
-```
-
-- `init_skin_from_config()` — called at CLI startup, reads `display.skin` from config
-- `get_active_skin()` — returns cached `SkinConfig` for the current skin
-- `set_active_skin(name)` — switches skin at runtime (used by `/skin` command)
-- `load_skin(name)` — loads from user skins first, then built-ins, then falls back to default
-- Missing skin values inherit from the `default` skin automatically
-
-### What skins customize
-
-| Element | Skin Key | Used By |
-|---------|----------|---------|
-| Banner panel border | `colors.banner_border` | `banner.py` |
-| Banner panel title | `colors.banner_title` | `banner.py` |
-| Banner section headers | `colors.banner_accent` | `banner.py` |
-| Banner dim text | `colors.banner_dim` | `banner.py` |
-| Banner body text | `colors.banner_text` | `banner.py` |
-| Response box border | `colors.response_border` | `cli.py` |
-| Spinner faces (waiting) | `spinner.waiting_faces` | `display.py` |
-| Spinner faces (thinking) | `spinner.thinking_faces` | `display.py` |
-| Spinner verbs | `spinner.thinking_verbs` | `display.py` |
-| Spinner wings (optional) | `spinner.wings` | `display.py` |
-| Tool output prefix | `tool_prefix` | `display.py` |
-| Agent name | `branding.agent_name` | `banner.py`, `cli.py` |
-| Welcome message | `branding.welcome` | `cli.py` |
-| Response box label | `branding.response_label` | `cli.py` |
-| Prompt symbol | `branding.prompt_symbol` | `cli.py` |
-
-### Built-in skins
-
-- `default` — Classic Hermes gold/kawaii (the current look)
-- `ares` — Crimson/bronze war-god theme with custom spinner wings
-- `mono` — Clean grayscale monochrome
-- `slate` — Cool blue developer-focused theme
-
-### Adding a built-in skin
-
-Add to `_BUILTIN_SKINS` dict in `hermes_cli/skin_engine.py`:
-
-```python
-"mytheme": {
-    "name": "mytheme",
-    "description": "Short description",
-    "colors": { ... },
-    "spinner": { ... },
-    "branding": { ... },
-    "tool_prefix": "┊",
-},
-```
-
-### User skins (YAML)
-
-Users create `~/.hermes/skins/<name>.yaml`:
-
-```yaml
-name: cyberpunk
-description: Neon-soaked terminal theme
-
-colors:
-  banner_border: "#FF00FF"
-  banner_title: "#00FFFF"
-  banner_accent: "#FF1493"
-
-spinner:
-  thinking_verbs: ["jacking in", "decrypting", "uploading"]
-  wings:
-    - ["⟨⚡", "⚡⟩"]
-
-branding:
-  agent_name: "Cyber Agent"
-  response_label: " ⚡ Cyber "
-
-tool_prefix: "▏"
-```
-
-Activate with `/skin cyberpunk` or `display.skin: cyberpunk` in config.yaml.
-
----
-
 ## Important Policies
 
 ### Prompt Caching Must Not Break
@@ -300,17 +210,6 @@ Cache-breaking forces dramatically higher costs. The ONLY time we alter context
 - **CLI**: Uses current directory (`.` → `os.getcwd()`)
 - **Messaging**: Uses `MESSAGING_CWD` env var (default: home directory)
 
-### Background Process Notifications (Gateway)
-
-When `terminal(background=true, check_interval=...)` is used, the gateway runs a watcher that
-pushes status updates to the user's chat. Control verbosity with `display.background_process_notifications`
-in config.yaml (or `HERMES_BACKGROUND_NOTIFICATIONS` env var):
-
-- `all` — running-output updates + final message (default)
-- `result` — only the final completion message
-- `error` — only the final message when exit code != 0
-- `off` — no watcher messages at all
-
 ---
 
 ## Known Pitfalls

CONTRIBUTING.md

@@ -139,8 +139,7 @@ hermes-agent/
 │   ├── commands.py               # Slash command definitions + autocomplete
 │   ├── callbacks.py              # Interactive callbacks (clarify, sudo, approval)
 │   ├── doctor.py                 # Diagnostics
-│   ├── skills_hub.py             # Skills Hub CLI + /skills slash command
-│   └── skin_engine.py            # Skin/theme engine — data-driven CLI visual customization
+│   └── skills_hub.py             # Skills Hub CLI + /skills slash command
 │
 ├── tools/                    # Tool implementations (self-registering)
 │   ├── registry.py               # Central tool registry (schemas, handlers, dispatch)
@@ -376,56 +375,6 @@ If the field is omitted or empty, the skill loads on all platforms (backward com
 
 ---
 
-## Adding a Skin / Theme
-
-Hermes uses a data-driven skin system — no code changes needed to add a new skin.
-
-**Option A: User skin (YAML file)**
-
-Create `~/.hermes/skins/<name>.yaml`:
-
-```yaml
-name: mytheme
-description: Short description of the theme
-
-colors:
-  banner_border: "#HEX"     # Panel border color
-  banner_title: "#HEX"      # Panel title color
-  banner_accent: "#HEX"     # Section header color
-  banner_dim: "#HEX"        # Muted/dim text color
-  banner_text: "#HEX"       # Body text color
-  response_border: "#HEX"   # Response box border
-
-spinner:
-  waiting_faces: ["(⚔)", "(⛨)"]
-  thinking_faces: ["(⚔)", "(⌁)"]
-  thinking_verbs: ["forging", "plotting"]
-  wings:                     # Optional left/right decorations
-    - ["⟪⚔", "⚔⟫"]
-
-branding:
-  agent_name: "My Agent"
-  welcome: "Welcome message"
-  response_label: " ⚔ Agent "
-  prompt_symbol: "⚔ ❯ "
-
-tool_prefix: "╎"             # Tool output line prefix
-```
-
-All fields are optional — missing values inherit from the default skin.
-
-**Option B: Built-in skin**
-
-Add to `_BUILTIN_SKINS` dict in `hermes_cli/skin_engine.py`. Use the same schema as above but as a Python dict. Built-in skins ship with the package and are always available.
-
-**Activating:**
-- CLI: `/skin mytheme` or set `display.skin: mytheme` in config.yaml
-- Config: `display: { skin: mytheme }`
-
-See `hermes_cli/skin_engine.py` for the full schema and existing skins as examples.
-
----
-
 ## Cross-Platform Compatibility
 
 Hermes runs on Linux, macOS, and Windows. When writing code that touches the OS:

agent/auxiliary_client.py

@@ -560,16 +560,12 @@ def get_vision_auxiliary_client() -> Tuple[Optional[OpenAI], Optional[str]]:
     forced = _get_auxiliary_provider("vision")
     if forced != "auto":
         return _resolve_forced_provider(forced)
-    # Auto: try providers known to support multimodal first, then fall
-    # back to the user's custom endpoint.  Many local models (Qwen-VL,
-    # LLaVA, Pixtral, etc.) support vision — skipping them entirely
-    # caused silent failures for local-only users.
-    for try_fn in (_try_openrouter, _try_nous, _try_codex,
-                   _try_custom_endpoint):
+    # Auto: only multimodal-capable providers
+    for try_fn in (_try_openrouter, _try_nous, _try_codex):
         client, model = try_fn()
         if client is not None:
             return client, model
-    logger.debug("Auxiliary vision client: none available")
+    logger.debug("Auxiliary vision client: none available (auto only tries OpenRouter/Nous/Codex)")
     return None, None
 
 

agent/display.py

@@ -5,8 +5,8 @@ Used by AIAgent._execute_tool_calls for CLI feedback.
 """
 
 import json
-import logging
 import os
+import random
 import sys
 import threading
 import time
@@ -15,49 +15,6 @@ import time
 _RED = "\033[31m"
 _RESET = "\033[0m"
 
-logger = logging.getLogger(__name__)
-
-
-# =========================================================================
-# Skin-aware helpers (lazy import to avoid circular deps)
-# =========================================================================
-
-def _get_skin():
-    """Get the active skin config, or None if not available."""
-    try:
-        from hermes_cli.skin_engine import get_active_skin
-        return get_active_skin()
-    except Exception:
-        return None
-
-
-def get_skin_faces(key: str, default: list) -> list:
-    """Get spinner face list from active skin, falling back to default."""
-    skin = _get_skin()
-    if skin:
-        faces = skin.get_spinner_list(key)
-        if faces:
-            return faces
-    return default
-
-
-def get_skin_verbs() -> list:
-    """Get thinking verbs from active skin."""
-    skin = _get_skin()
-    if skin:
-        verbs = skin.get_spinner_list("thinking_verbs")
-        if verbs:
-            return verbs
-    return KawaiiSpinner.THINKING_VERBS
-
-
-def get_skin_tool_prefix() -> str:
-    """Get tool output prefix character from active skin."""
-    skin = _get_skin()
-    if skin:
-        return skin.tool_prefix
-    return "┊"
-
 
 # =========================================================================
 # Tool preview (one-line summary of a tool call's primary argument)
@@ -65,8 +22,6 @@ def get_skin_tool_prefix() -> str:
 
 def build_tool_preview(tool_name: str, args: dict, max_len: int = 40) -> str:
     """Build a short preview of a tool call's primary argument for display."""
-    if not args:
-        return None
     primary_args = {
         "terminal": "command", "web_search": "query", "web_extract": "urls",
         "read_file": "path", "write_file": "path", "patch": "path",
@@ -208,7 +163,6 @@ class KawaiiSpinner:
         self.frame_idx = 0
         self.start_time = None
         self.last_line_len = 0
-        self._last_flush_time = 0.0  # Rate-limit flushes for patch_stdout compat
         # Capture stdout NOW, before any redirect_stdout(devnull) from
         # child agents can replace sys.stdout with a black hole.
         self._out = sys.stdout
@@ -223,34 +177,15 @@ class KawaiiSpinner:
             pass
 
     def _animate(self):
-        # Cache skin wings at start (avoid per-frame imports)
-        skin = _get_skin()
-        wings = skin.get_spinner_wings() if skin else []
-
         while self.running:
             if os.getenv("HERMES_SPINNER_PAUSE"):
                 time.sleep(0.1)
                 continue
             frame = self.spinner_frames[self.frame_idx % len(self.spinner_frames)]
             elapsed = time.time() - self.start_time
-            if wings:
-                left, right = wings[self.frame_idx % len(wings)]
-                line = f"  {left} {frame} {self.message} {right} ({elapsed:.1f}s)"
-            else:
-                line = f"  {frame} {self.message} ({elapsed:.1f}s)"
+            line = f"  {frame} {self.message} ({elapsed:.1f}s)"
             pad = max(self.last_line_len - len(line), 0)
-            # Rate-limit flush() calls to avoid spinner spam under
-            # prompt_toolkit's patch_stdout.  Each flush() pushes a queue
-            # item that may trigger a separate run_in_terminal() call; if
-            # items are processed one-at-a-time the \r overwrite is lost
-            # and every frame appears on its own line.  By flushing at
-            # most every 0.4s we guarantee multiple \r-frames are batched
-            # into a single write, so the terminal collapses them correctly.
-            now = time.time()
-            should_flush = (now - self._last_flush_time) >= 0.4
-            self._write(f"\r{line}{' ' * pad}", end='', flush=should_flush)
-            if should_flush:
-                self._last_flush_time = now
+            self._write(f"\r{line}{' ' * pad}", end='', flush=True)
             self.last_line_len = len(line)
             self.frame_idx += 1
             time.sleep(0.12)
@@ -365,7 +300,7 @@ def _detect_tool_failure(tool_name: str, result: str | None) -> tuple[bool, str]
             if exit_code is not None and exit_code != 0:
                 return True, f" [exit {exit_code}]"
         except (json.JSONDecodeError, TypeError, AttributeError):
-            logger.debug("Could not parse terminal result as JSON for exit code check")
+            pass
         return False, ""
 
     # Memory-specific: distinguish "full" from real errors
@@ -375,7 +310,7 @@ def _detect_tool_failure(tool_name: str, result: str | None) -> tuple[bool, str]
             if data.get("success") is False and "exceed the limit" in data.get("error", ""):
                 return True, " [full]"
         except (json.JSONDecodeError, TypeError, AttributeError):
-            logger.debug("Could not parse memory result as JSON for capacity check")
+            pass
 
     # Generic heuristic for non-terminal tools
     lower = result[:500].lower()
@@ -397,7 +332,6 @@ def get_cute_tool_message(
     """
     dur = f"{duration:.1f}s"
     is_failure, failure_suffix = _detect_tool_failure(tool_name, result)
-    skin_prefix = get_skin_tool_prefix()
 
     def _trunc(s, n=40):
         s = str(s)
@@ -408,9 +342,7 @@ def get_cute_tool_message(
         return ("..." + p[-(n-3):]) if len(p) > n else p
 
     def _wrap(line: str) -> str:
-        """Apply skin tool prefix and failure suffix."""
-        if skin_prefix != "┊":
-            line = line.replace("┊", skin_prefix, 1)
+        """Append failure suffix when the tool failed."""
         if not is_failure:
             return line
         return f"{line}{failure_suffix}"

agent/prompt_builder.py

@@ -159,8 +159,8 @@ def _read_skill_description(skill_file: Path, max_chars: int = 60) -> str:
             if len(desc) > max_chars:
                 desc = desc[:max_chars - 3] + "..."
             return desc
-    except Exception as e:
-        logger.debug("Failed to read skill description from %s: %s", skill_file, e)
+    except Exception:
+        pass
     return ""
 
 
@@ -179,12 +179,10 @@ def _skill_is_platform_compatible(skill_file: Path) -> bool:
         return True  # Err on the side of showing the skill
 
 
-def build_skills_system_prompt(agent_skills_dir: Optional[Path] = None) -> str:
+def build_skills_system_prompt() -> str:
     """Build a compact skill index for the system prompt.
 
     Scans ~/.hermes/skills/ for SKILL.md files grouped by category.
-    When agent_skills_dir is provided and exists, also scans it for SKILL.md
-    files. Agent skills take priority (listed first in each category).
     Includes per-skill descriptions from frontmatter so the model can
     match skills by meaning, not just name.
     Filters out skills incompatible with the current OS platform.
@@ -192,46 +190,34 @@ def build_skills_system_prompt(agent_skills_dir: Optional[Path] = None) -> str:
     hermes_home = Path(os.getenv("HERMES_HOME", Path.home() / ".hermes"))
     skills_dir = hermes_home / "skills"
 
+    if not skills_dir.exists():
+        return ""
+
     # Collect skills with descriptions, grouped by category
     # Each entry: (skill_name, description)
-    # Supports sub-categories: skills/mlops/training/axolotl/SKILL.md
-    # → category "mlops/training", skill "axolotl"
     skills_by_category: dict[str, list[tuple[str, str]]] = {}
-
-    def _scan_skills_dir(scan_dir: Path):
-        """Scan a directory for SKILL.md files and add them to skills_by_category."""
-        if not scan_dir.exists():
-            return
-        for skill_file in scan_dir.rglob("SKILL.md"):
-            if not _skill_is_platform_compatible(skill_file):
-                continue
-            rel_path = skill_file.relative_to(scan_dir)
-            parts = rel_path.parts
-            if len(parts) >= 2:
-                skill_name = parts[-2]
-                category = "/".join(parts[:-2]) if len(parts) > 2 else parts[0]
-            else:
-                category = "general"
-                skill_name = skill_file.parent.name
-            desc = _read_skill_description(skill_file)
-            skills_by_category.setdefault(category, []).append((skill_name, desc))
-
-    # Agent skills first (so they appear first / take priority)
-    if agent_skills_dir is not None:
-        _scan_skills_dir(agent_skills_dir)
-
-    # Then global skills
-    _scan_skills_dir(skills_dir)
+    for skill_file in skills_dir.rglob("SKILL.md"):
+        # Skip skills incompatible with the current OS platform
+        if not _skill_is_platform_compatible(skill_file):
+            continue
+        rel_path = skill_file.relative_to(skills_dir)
+        parts = rel_path.parts
+        if len(parts) >= 2:
+            category = parts[0]
+            skill_name = parts[-2]
+        else:
+            category = "general"
+            skill_name = skill_file.parent.name
+        desc = _read_skill_description(skill_file)
+        skills_by_category.setdefault(category, []).append((skill_name, desc))
 
     if not skills_by_category:
         return ""
 
     # Read category-level descriptions from DESCRIPTION.md
-    # Checks both the exact category path and parent directories
     category_descriptions = {}
     for category in skills_by_category:
-        cat_path = Path(category)
-        desc_file = skills_dir / cat_path / "DESCRIPTION.md"
+        desc_file = skills_dir / category / "DESCRIPTION.md"
         if desc_file.exists():
             try:
                 content = desc_file.read_text(encoding="utf-8")
@@ -289,11 +275,11 @@ def _truncate_content(content: str, filename: str, max_chars: int = CONTEXT_FILE
     return head + marker + tail
 
 
-def build_context_files_prompt(cwd: Optional[str] = None, agent_workspace: Optional[Path] = None) -> str:
+def build_context_files_prompt(cwd: Optional[str] = None) -> str:
     """Discover and load context files for the system prompt.
 
     Discovery: AGENTS.md (recursive), .cursorrules / .cursor/rules/*.mdc,
-    SOUL.md (agent_workspace then cwd then ~/.hermes/ fallback). Each capped at 20,000 chars.
+    SOUL.md (cwd then ~/.hermes/ fallback). Each capped at 20,000 chars.
     """
     if cwd is None:
         cwd = os.getcwd()
@@ -361,20 +347,13 @@ def build_context_files_prompt(cwd: Optional[str] = None, agent_workspace: Optio
         cursorrules_content = _truncate_content(cursorrules_content, ".cursorrules")
         sections.append(cursorrules_content)
 
-    # SOUL.md (agent_workspace first, then cwd, then ~/.hermes/ fallback)
+    # SOUL.md (cwd first, then ~/.hermes/ fallback)
     soul_path = None
-    if agent_workspace is not None:
-        for name in ["SOUL.md", "soul.md"]:
-            candidate = agent_workspace / name
-            if candidate.exists():
-                soul_path = candidate
-                break
-    if not soul_path:
-        for name in ["SOUL.md", "soul.md"]:
-            candidate = cwd_path / name
-            if candidate.exists():
-                soul_path = candidate
-                break
+    for name in ["SOUL.md", "soul.md"]:
+        candidate = cwd_path / name
+        if candidate.exists():
+            soul_path = candidate
+            break
     if not soul_path:
         global_soul = Path.home() / ".hermes" / "SOUL.md"
         if global_soul.exists():

agent/redact.py

@@ -10,6 +10,7 @@ the first 6 and last 4 characters for debuggability.
 import logging
 import os
 import re
+from typing import Optional
 
 logger = logging.getLogger(__name__)
 
@@ -59,8 +60,7 @@ _AUTH_HEADER_RE = re.compile(
     re.IGNORECASE,
 )
 
-# Telegram bot tokens: bot<digits>:<token> or <digits>:<token>,
-# where token part is restricted to [-A-Za-z0-9_] and length >= 30
+# Telegram bot tokens: bot<digits>:<token> or <digits>:<alphanum>
 _TELEGRAM_RE = re.compile(
     r"(bot)?(\d{8,}):([-A-Za-z0-9_]{30,})",
 )

batch_runner.py

@@ -606,7 +606,7 @@ class BatchRunner:
         # Create batches
         self.batches = self._create_batches()
         
-        print("📊 Batch Runner Initialized")
+        print(f"📊 Batch Runner Initialized")
         print(f"   Dataset: {self.dataset_file} ({len(self.dataset)} prompts)")
         print(f"   Batch size: {self.batch_size}")
         print(f"   Total batches: {len(self.batches)}")
@@ -826,7 +826,7 @@ class BatchRunner:
             print("=" * 70)
             print(f"   Original dataset size:     {len(self.dataset):,} prompts")
             print(f"   Already completed:         {len(skipped_indices):,} prompts")
-            print("   ─────────────────────────────────────────")
+            print(f"   ─────────────────────────────────────────")
             print(f"   🎯 RESUMING WITH:          {len(filtered_entries):,} prompts")
             print(f"   New batches created:       {len(batches_to_process)}")
             print("=" * 70 + "\n")
@@ -888,7 +888,7 @@ class BatchRunner:
             ]
             
             print(f"✅ Created {len(tasks)} batch tasks")
-            print("🚀 Starting parallel batch processing...\n")
+            print(f"🚀 Starting parallel batch processing...\n")
             
             # Use rich Progress for better visual tracking with persistent bottom bar
             # redirect_stdout/stderr lets rich manage all output so progress bar stays clean
@@ -1057,7 +1057,7 @@ class BatchRunner:
         print(f"✅ Total trajectories in merged file: {total_entries - filtered_entries}")
         print(f"✅ Total batch files merged: {batch_files_found}")
         print(f"⏱️  Total duration: {round(time.time() - start_time, 2)}s")
-        print("\n📈 Tool Usage Statistics:")
+        print(f"\n📈 Tool Usage Statistics:")
         print("-" * 70)
         
         if total_tool_stats:
@@ -1084,7 +1084,7 @@ class BatchRunner:
         # Print reasoning coverage stats
         total_discarded = sum(r.get("discarded_no_reasoning", 0) for r in results)
         
-        print("\n🧠 Reasoning Coverage:")
+        print(f"\n🧠 Reasoning Coverage:")
         print("-" * 70)
         total_turns = total_reasoning_stats["total_assistant_turns"]
         with_reasoning = total_reasoning_stats["turns_with_reasoning"]
@@ -1101,8 +1101,8 @@ class BatchRunner:
             print(f"   🚫 Samples discarded (zero reasoning): {total_discarded:,}")
         
         print(f"\n💾 Results saved to: {self.output_dir}")
-        print("   - Trajectories: trajectories.jsonl (combined)")
-        print("   - Individual batches: batch_*.jsonl (for debugging)")
+        print(f"   - Trajectories: trajectories.jsonl (combined)")
+        print(f"   - Individual batches: batch_*.jsonl (for debugging)")
         print(f"   - Statistics: {self.stats_file.name}")
         print(f"   - Checkpoint: {self.checkpoint_file.name}")
 
@@ -1238,7 +1238,7 @@ def main(
             with open(prefill_messages_file, 'r', encoding='utf-8') as f:
                 prefill_messages = json.load(f)
             if not isinstance(prefill_messages, list):
-                print("❌ Error: prefill_messages_file must contain a JSON array of messages")
+                print(f"❌ Error: prefill_messages_file must contain a JSON array of messages")
                 return
             print(f"💬 Loaded {len(prefill_messages)} prefill messages from {prefill_messages_file}")
         except Exception as e:

cli-config.yaml.example

@@ -11,7 +11,6 @@ model:
   
   # Inference provider selection:
   #   "auto"       - Use Nous Portal if logged in, otherwise OpenRouter/env vars (default)
-  #   "nous-api"   - Use Nous Portal via API key (requires: NOUS_API_KEY)
   #   "openrouter" - Always use OpenRouter API key from OPENROUTER_API_KEY
   #   "nous"       - Always use Nous Portal (requires: hermes login)
   #   "zai"        - Use z.ai / ZhipuAI GLM models (requires: GLM_API_KEY)
@@ -403,13 +402,11 @@ agent:
 #     discord: [web, vision, skills, todo]
 #
 # If not set, defaults are:
-#   cli:           hermes-cli            (everything + cronjob management)
-#   telegram:      hermes-telegram       (terminal, file, web, vision, image, tts, browser, skills, todo, cronjob, messaging)
-#   discord:       hermes-discord        (same as telegram)
-#   whatsapp:      hermes-whatsapp       (same as telegram)
-#   slack:         hermes-slack          (same as telegram)
-#   signal:        hermes-signal         (same as telegram)
-#   homeassistant: hermes-homeassistant  (same as telegram)
+#   cli:      hermes-cli      (everything + cronjob management)
+#   telegram: hermes-telegram  (terminal, file, web, vision, image, tts, browser, skills, todo, cronjob, messaging)
+#   discord:  hermes-discord   (same as telegram)
+#   whatsapp: hermes-whatsapp  (same as telegram)
+#   slack:    hermes-slack     (same as telegram)
 #
 platform_toolsets:
   cli: [hermes-cli]
@@ -417,8 +414,6 @@ platform_toolsets:
   discord: [hermes-discord]
   whatsapp: [hermes-whatsapp]
   slack: [hermes-slack]
-  signal: [hermes-signal]
-  homeassistant: [hermes-homeassistant]
 
 # ─────────────────────────────────────────────────────────────────────────────
 # Available toolsets (use these names in platform_toolsets or the toolsets list)
@@ -560,21 +555,6 @@ toolsets:
 #     args: ["-y", "@modelcontextprotocol/server-github"]
 #     env:
 #       GITHUB_PERSONAL_ACCESS_TOKEN: "ghp_..."
-#
-# Sampling (server-initiated LLM requests) — enabled by default.
-# Per-server config under the 'sampling' key:
-#   analysis:
-#     command: npx
-#     args: ["-y", "analysis-server"]
-#     sampling:
-#       enabled: true           # default: true
-#       model: "gemini-3-flash" # override model (optional)
-#       max_tokens_cap: 4096    # max tokens per request
-#       timeout: 30             # LLM call timeout (seconds)
-#       max_rpm: 10             # max requests per minute
-#       allowed_models: []      # model whitelist (empty = all)
-#       max_tool_rounds: 5      # tool loop limit (0 = disable)
-#       log_level: "info"       # audit verbosity
 
 # =============================================================================
 # Voice Transcription (Speech-to-Text)
@@ -656,57 +636,7 @@ display:
   # Toggle at runtime with /verbose in the CLI
   tool_progress: all
 
-  # Background process notifications (gateway/messaging only).
-  # Controls how chatty the process watcher is when you use
-  # terminal(background=true, check_interval=...) from Telegram/Discord/etc.
-  #   off:     No watcher messages at all
-  #   result:  Only the final completion message
-  #   error:   Only the final message when exit code != 0
-  #   all:     Running output updates + final message (default)
-  background_process_notifications: all
-
   # Play terminal bell when agent finishes a response.
   # Useful for long-running tasks — your terminal will ding when the agent is done.
   # Works over SSH. Most terminals can be configured to flash the taskbar or play a sound.
   bell_on_complete: false
-
-  # ───────────────────────────────────────────────────────────────────────────
-  # Skin / Theme
-  # ───────────────────────────────────────────────────────────────────────────
-  # Customize CLI visual appearance — banner colors, spinner faces, tool prefix,
-  # response box label, and branding text. Change at runtime with /skin <name>.
-  #
-  # Built-in skins:
-  #   default  — Classic Hermes gold/kawaii
-  #   ares     — Crimson/bronze war-god theme with spinner wings
-  #   mono     — Clean grayscale monochrome
-  #   slate    — Cool blue developer-focused
-  #
-  # Custom skins: drop a YAML file in ~/.hermes/skins/<name>.yaml
-  # Schema (all fields optional, missing values inherit from default):
-  #
-  #   name: my-theme
-  #   description: Short description
-  #   colors:
-  #     banner_border: "#HEX"    # Panel border
-  #     banner_title: "#HEX"     # Panel title
-  #     banner_accent: "#HEX"    # Section headers (Available Tools, etc.)
-  #     banner_dim: "#HEX"       # Dim/muted text
-  #     banner_text: "#HEX"      # Body text (tool names, skill names)
-  #     ui_accent: "#HEX"        # UI accent color
-  #     response_border: "#HEX"  # Response box border color
-  #   spinner:
-  #     waiting_faces: ["(⚔)", "(⛨)"]       # Faces shown while waiting
-  #     thinking_faces: ["(⚔)", "(⌁)"]      # Faces shown while thinking
-  #     thinking_verbs: ["forging", "plotting"]  # Verbs for spinner messages
-  #     wings:                                # Optional left/right spinner decorations
-  #       - ["⟪⚔", "⚔⟫"]
-  #       - ["⟪▲", "▲⟫"]
-  #   branding:
-  #     agent_name: "My Agent"               # Banner title and branding
-  #     welcome: "Welcome message"           # Shown at CLI startup
-  #     response_label: " ⚔ Agent "         # Response box header label
-  #     prompt_symbol: "⚔ ❯ "              # Prompt symbol
-  #   tool_prefix: "╎"                       # Tool output line prefix (default: ┊)
-  #
-  skin: default

cli.py

@@ -19,8 +19,6 @@ import sys
 import json
 import atexit
 import uuid
-import textwrap
-from contextlib import contextmanager
 from pathlib import Path
 from datetime import datetime
 from typing import List, Dict, Any, Optional
@@ -47,16 +45,9 @@ from prompt_toolkit.widgets import TextArea
 from prompt_toolkit.key_binding import KeyBindings
 from prompt_toolkit import print_formatted_text as _pt_print
 from prompt_toolkit.formatted_text import ANSI as _PT_ANSI
-try:
-    from prompt_toolkit.cursor_shapes import CursorShape
-    _STEADY_CURSOR = CursorShape.BLOCK  # Non-blinking block cursor
-except (ImportError, AttributeError):
-    _STEADY_CURSOR = None
 import threading
 import queue
 
-_COMMAND_SPINNER_FRAMES = ("⠋", "⠙", "⠹", "⠸", "⠼", "⠴", "⠦", "⠧", "⠇", "⠏")
-
 
 # Load .env from ~/.hermes/.env first, then project root as dev fallback
 from dotenv import load_dotenv
@@ -167,7 +158,6 @@ def load_cli_config() -> Dict[str, Any]:
             "singularity_image": "docker://python:3.11",
             "modal_image": "python:3.11",
             "daytona_image": "nikolaik/python-nodejs:python3.11-nodejs20",
-            "docker_volumes": [],  # host:container volume mounts for Docker backend
         },
         "browser": {
             "inactivity_timeout": 120,  # Auto-cleanup inactive browser sessions after 2 min
@@ -205,7 +195,6 @@ def load_cli_config() -> Dict[str, Any]:
         "display": {
             "compact": False,
             "resume_display": "full",
-            "skin": "default",
         },
         "clarify": {
             "timeout": 120,  # Seconds to wait for a clarify answer before auto-proceeding
@@ -260,13 +249,8 @@ def load_cli_config() -> Dict[str, Any]:
                 if key not in defaults and key != "model":
                     defaults[key] = file_config[key]
             
-            # Handle legacy root-level max_turns (backwards compat) - copy to
-            # agent.max_turns whenever the nested key is missing.
-            agent_file_config = file_config.get("agent")
-            if "max_turns" in file_config and not (
-                isinstance(agent_file_config, dict)
-                and agent_file_config.get("max_turns") is not None
-            ):
+            # Handle root-level max_turns (backwards compat) - copy to agent.max_turns
+            if "max_turns" in file_config and "agent" not in file_config:
                 defaults["agent"]["max_turns"] = file_config["max_turns"]
         except Exception as e:
             logger.warning("Failed to load cli-config.yaml: %s", e)
@@ -392,14 +376,6 @@ def load_cli_config() -> Dict[str, Any]:
 # Load configuration at module startup
 CLI_CONFIG = load_cli_config()
 
-# Initialize the skin engine from config
-try:
-    from hermes_cli.skin_engine import init_skin_from_config
-    init_skin_from_config(CLI_CONFIG)
-except Exception:
-    pass  # Skin engine is optional — default skin used if unavailable
-
-from rich import box as rich_box
 from rich.console import Console
 from rich.panel import Panel
 from rich.table import Table
@@ -718,8 +694,6 @@ class ChatConsole:
     def print(self, *args, **kwargs):
         self._buffer.seek(0)
         self._buffer.truncate()
-        # Read terminal width at render time so panels adapt to current size
-        self._inner.width = shutil.get_terminal_size((80, 24)).columns
         self._inner.print(*args, **kwargs)
         output = self._buffer.getvalue()
         for line in output.rstrip("\n").split("\n"):
@@ -751,7 +725,6 @@ HERMES_CADUCEUS = """[#CD7F32]⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣀⡀⠀⣀⣀
 [#B8860B]⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀[/]"""
 
 # Compact banner for smaller terminals (fallback)
-# Note: built dynamically by _build_compact_banner() to fit terminal width
 COMPACT_BANNER = """
 [bold #FFD700]╔══════════════════════════════════════════════════════════════╗[/]
 [bold #FFD700]║[/]  [#FFBF00]⚕ NOUS HERMES[/] [dim #B8860B]- AI Agent Framework[/]              [bold #FFD700]║[/]
@@ -760,26 +733,6 @@ COMPACT_BANNER = """
 """
 
 
-def _build_compact_banner() -> str:
-    """Build a compact banner that fits the current terminal width."""
-    w = min(shutil.get_terminal_size().columns - 2, 64)
-    if w < 30:
-        return "\n[#FFBF00]⚕ NOUS HERMES[/] [dim #B8860B]- Nous Research[/]\n"
-    inner = w - 2  # inside the box border
-    bar = "═" * w
-    line1 = "⚕ NOUS HERMES - AI Agent Framework"
-    line2 = "Messenger of the Digital Gods  ·  Nous Research"
-    # Truncate and pad to fit
-    line1 = line1[:inner - 2].ljust(inner - 2)
-    line2 = line2[:inner - 2].ljust(inner - 2)
-    return (
-        f"\n[bold #FFD700]╔{bar}╗[/]\n"
-        f"[bold #FFD700]║[/] [#FFBF00]{line1}[/] [bold #FFD700]║[/]\n"
-        f"[bold #FFD700]║[/] [dim #B8860B]{line2}[/] [bold #FFD700]║[/]\n"
-        f"[bold #FFD700]╚{bar}╝[/]\n"
-    )
-
-
 def _get_available_skills() -> Dict[str, List[str]]:
     """
     Scan ~/.hermes/skills/ and return skills grouped by category.
@@ -853,43 +806,25 @@ def build_welcome_banner(console: Console, model: str, cwd: str, tools: List[dic
     layout_table.add_column("right", justify="left")
     
     # Build left content: caduceus + model info
-    # Resolve skin colors for the banner
-    try:
-        from hermes_cli.skin_engine import get_active_skin
-        _bskin = get_active_skin()
-        _accent = _bskin.get_color("banner_accent", "#FFBF00")
-        _dim = _bskin.get_color("banner_dim", "#B8860B")
-        _text = _bskin.get_color("banner_text", "#FFF8DC")
-        _session_c = _bskin.get_color("session_border", "#8B8682")
-        _title_c = _bskin.get_color("banner_title", "#FFD700")
-        _border_c = _bskin.get_color("banner_border", "#CD7F32")
-        _agent_name = _bskin.get_branding("agent_name", "Hermes Agent")
-    except Exception:
-        _bskin = None
-        _accent, _dim, _text = "#FFBF00", "#B8860B", "#FFF8DC"
-        _session_c, _title_c, _border_c = "#8B8682", "#FFD700", "#CD7F32"
-        _agent_name = "Hermes Agent"
-
-    _hero = _bskin.banner_hero if hasattr(_bskin, 'banner_hero') and _bskin.banner_hero else HERMES_CADUCEUS
-    left_lines = ["", _hero, ""]
+    left_lines = ["", HERMES_CADUCEUS, ""]
     
     # Shorten model name for display
     model_short = model.split("/")[-1] if "/" in model else model
     if len(model_short) > 28:
         model_short = model_short[:25] + "..."
     
-    ctx_str = f" [dim {_dim}]·[/] [dim {_dim}]{_format_context_length(context_length)} context[/]" if context_length else ""
-    left_lines.append(f"[{_accent}]{model_short}[/]{ctx_str} [dim {_dim}]·[/] [dim {_dim}]Nous Research[/]")
-    left_lines.append(f"[dim {_dim}]{cwd}[/]")
+    ctx_str = f" [dim #B8860B]·[/] [dim #B8860B]{_format_context_length(context_length)} context[/]" if context_length else ""
+    left_lines.append(f"[#FFBF00]{model_short}[/]{ctx_str} [dim #B8860B]·[/] [dim #B8860B]Nous Research[/]")
+    left_lines.append(f"[dim #B8860B]{cwd}[/]")
     
     # Add session ID if provided
     if session_id:
-        left_lines.append(f"[dim {_session_c}]Session: {session_id}[/]")
+        left_lines.append(f"[dim #8B8682]Session: {session_id}[/]")
     left_content = "\n".join(left_lines)
     
     # Build right content: tools list grouped by toolset
     right_lines = []
-    right_lines.append(f"[bold {_accent}]Available Tools[/]")
+    right_lines.append("[bold #FFBF00]Available Tools[/]")
     
     # Group tools by toolset (include all possible tools, both enabled and disabled)
     toolsets_dict = {}
@@ -926,7 +861,7 @@ def build_welcome_banner(console: Console, model: str, cwd: str, tools: List[dic
             if name in disabled_tools:
                 colored_names.append(f"[red]{name}[/]")
             else:
-                colored_names.append(f"[{_text}]{name}[/]")
+                colored_names.append(f"[#FFF8DC]{name}[/]")
         
         tools_str = ", ".join(colored_names)
         # Truncate if too long (accounting for markup)
@@ -948,18 +883,18 @@ def build_welcome_banner(console: Console, model: str, cwd: str, tools: List[dic
                 elif name in disabled_tools:
                     colored_names.append(f"[red]{name}[/]")
                 else:
-                    colored_names.append(f"[{_text}]{name}[/]")
+                    colored_names.append(f"[#FFF8DC]{name}[/]")
             tools_str = ", ".join(colored_names)
         
-        right_lines.append(f"[dim {_dim}]{toolset}:[/] {tools_str}")
+        right_lines.append(f"[dim #B8860B]{toolset}:[/] {tools_str}")
     
     if remaining_toolsets > 0:
-        right_lines.append(f"[dim {_dim}](and {remaining_toolsets} more toolsets...)[/]")
+        right_lines.append(f"[dim #B8860B](and {remaining_toolsets} more toolsets...)[/]")
     
     right_lines.append("")
     
     # Add skills section
-    right_lines.append(f"[bold {_accent}]Available Skills[/]")
+    right_lines.append("[bold #FFBF00]Available Skills[/]")
     skills_by_category = _get_available_skills()
     total_skills = sum(len(s) for s in skills_by_category.values())
     
@@ -975,12 +910,12 @@ def build_welcome_banner(console: Console, model: str, cwd: str, tools: List[dic
             # Truncate if still too long
             if len(skills_str) > 50:
                 skills_str = skills_str[:47] + "..."
-            right_lines.append(f"[dim {_dim}]{category}:[/] [{_text}]{skills_str}[/]")
+            right_lines.append(f"[dim #B8860B]{category}:[/] [#FFF8DC]{skills_str}[/]")
     else:
-        right_lines.append(f"[dim {_dim}]No skills installed[/]")
+        right_lines.append("[dim #B8860B]No skills installed[/]")
     
     right_lines.append("")
-    right_lines.append(f"[dim {_dim}]{len(tools)} tools · {total_skills} skills · /help for commands[/]")
+    right_lines.append(f"[dim #B8860B]{len(tools)} tools · {total_skills} skills · /help for commands[/]")
     
     right_content = "\n".join(right_lines)
     
@@ -990,18 +925,15 @@ def build_welcome_banner(console: Console, model: str, cwd: str, tools: List[dic
     # Wrap in a panel with the title
     outer_panel = Panel(
         layout_table,
-        title=f"[bold {_title_c}]{_agent_name} {VERSION}[/]",
-        border_style=_border_c,
+        title=f"[bold #FFD700]Hermes Agent {VERSION}[/]",
+        border_style="#CD7F32",
         padding=(0, 2),
     )
     
-    # Print the big logo — use skin's custom logo if available
+    # Print the big HERMES-AGENT logo first (no panel wrapper for full width)
+    console.print()
+    console.print(HERMES_AGENT_LOGO)
     console.print()
-    term_width = shutil.get_terminal_size().columns
-    if term_width >= 95:
-        _logo = _bskin.banner_logo if hasattr(_bskin, 'banner_logo') and _bskin.banner_logo else HERMES_AGENT_LOGO
-        console.print(_logo)
-        console.print()
     
     # Print the panel with caduceus and info
     console.print(outer_panel)
@@ -1089,8 +1021,6 @@ class HermesCLI:
         verbose: bool = False,
         compact: bool = False,
         resume: str = None,
-        checkpoints: bool = False,
-        agent: str = None,
     ):
         """
         Initialize the Hermes CLI.
@@ -1172,13 +1102,6 @@ class HermesCLI:
             if invalid:
                 self.console.print(f"[bold red]Warning: Unknown toolsets: {', '.join(invalid)}[/]")
         
-        # Filesystem checkpoints: CLI flag > config
-        cp_cfg = CLI_CONFIG.get("checkpoints", {})
-        if isinstance(cp_cfg, bool):
-            cp_cfg = {"enabled": cp_cfg}
-        self.checkpoints_enabled = checkpoints or cp_cfg.get("enabled", False)
-        self.checkpoint_max_snapshots = cp_cfg.get("max_snapshots", 50)
-        
         # Ephemeral system prompt: env var takes precedence, then config
         self.system_prompt = (
             os.getenv("HERMES_EPHEMERAL_SYSTEM_PROMPT", "")
@@ -1209,31 +1132,6 @@ class HermesCLI:
         fb = CLI_CONFIG.get("fallback_model") or {}
         self._fallback_model = fb if fb.get("provider") and fb.get("model") else None
 
-        # Multi-agent: resolve agent config from config.yaml
-        self._agent_id = agent or 'main'
-        self._agent_config = None
-        self._agent_tool_policy = None
-        self._agent_workspace = None
-        agents_config = CLI_CONFIG.get('agents', {})
-        if agents_config and self._agent_id in agents_config:
-            from gateway.agent_registry import AgentRegistry
-            registry = AgentRegistry({'agents': agents_config})
-            self._agent_config = registry.get(self._agent_id)
-            if self._agent_config:
-                # Override model/provider/personality from agent config
-                if self._agent_config.model:
-                    self.model = self._agent_config.model
-                if self._agent_config.provider:
-                    self.provider = self._agent_config.provider
-                if self._agent_config.personality:
-                    self._ephemeral_system_prompt = self._agent_config.personality
-                if self._agent_config.max_turns:
-                    self.max_turns = self._agent_config.max_turns
-                if self._agent_config.toolsets:
-                    self._toolsets = self._agent_config.toolsets
-                self._agent_tool_policy = self._agent_config.tool_policy
-                self._agent_workspace = str(self._agent_config.workspace_dir)
-
         # Agent will be initialized on first use
         self.agent: Optional[AIAgent] = None
         self._app = None  # prompt_toolkit Application (set in run())
@@ -1265,9 +1163,6 @@ class HermesCLI:
         # History file for persistent input recall across sessions
         self._history_file = Path.home() / ".hermes_history"
         self._last_invalidate: float = 0.0  # throttle UI repaints
-        self._spinner_text: str = ""  # thinking spinner text for TUI
-        self._command_running = False
-        self._command_status = ""
 
     def _invalidate(self, min_interval: float = 0.25) -> None:
         """Throttled UI repaint — prevents terminal blinking on slow/SSH connections."""
@@ -1331,49 +1226,6 @@ class HermesCLI:
 
         return changed
 
-    def _on_thinking(self, text: str) -> None:
-        """Called by agent when thinking starts/stops. Updates TUI spinner."""
-        self._spinner_text = text or ""
-        self._invalidate()
-
-    def _slow_command_status(self, command: str) -> str:
-        """Return a user-facing status message for slower slash commands."""
-        cmd_lower = command.lower().strip()
-        if cmd_lower.startswith("/skills search"):
-            return "Searching skills..."
-        if cmd_lower.startswith("/skills browse"):
-            return "Loading skills..."
-        if cmd_lower.startswith("/skills inspect"):
-            return "Inspecting skill..."
-        if cmd_lower.startswith("/skills install"):
-            return "Installing skill..."
-        if cmd_lower.startswith("/skills"):
-            return "Processing skills command..."
-        if cmd_lower == "/reload-mcp":
-            return "Reloading MCP servers..."
-        return "Processing command..."
-
-    def _command_spinner_frame(self) -> str:
-        """Return the current spinner frame for slow slash commands."""
-        import time as _time
-
-        frame_idx = int(_time.monotonic() * 10) % len(_COMMAND_SPINNER_FRAMES)
-        return _COMMAND_SPINNER_FRAMES[frame_idx]
-
-    @contextmanager
-    def _busy_command(self, status: str):
-        """Expose a temporary busy state in the TUI while a slash command runs."""
-        self._command_running = True
-        self._command_status = status
-        self._invalidate(min_interval=0.0)
-        try:
-            print(f"⏳ {status}")
-            yield
-        finally:
-            self._command_running = False
-            self._command_status = ""
-            self._invalidate(min_interval=0.0)
-
     def _ensure_runtime_credentials(self) -> bool:
         """
         Ensure runtime credentials are resolved before agent use.
@@ -1512,11 +1364,6 @@ class HermesCLI:
                 clarify_callback=self._clarify_callback,
                 honcho_session_key=self.session_id,
                 fallback_model=self._fallback_model,
-                thinking_callback=self._on_thinking,
-                checkpoints_enabled=self.checkpoints_enabled,
-                checkpoint_max_snapshots=self.checkpoint_max_snapshots,
-                agent_tool_policy=self._agent_tool_policy,
-                agent_workspace=self._agent_workspace,
             )
             # Apply any pending title now that the session exists in the DB
             if self._pending_title and self._session_db:
@@ -1536,13 +1383,8 @@ class HermesCLI:
         """Display the welcome banner in Claude Code style."""
         self.console.clear()
         
-        # Auto-compact for narrow terminals — the full banner with caduceus
-        # + tool list needs ~80 columns minimum to render without wrapping.
-        term_width = shutil.get_terminal_size().columns
-        use_compact = self.compact or term_width < 80
-        
-        if use_compact:
-            self.console.print(_build_compact_banner())
+        if self.compact:
+            self.console.print(COMPACT_BANNER)
             self._show_status()
         else:
             # Get tools for display
@@ -1786,55 +1628,6 @@ class HermesCLI:
         self._image_counter -= 1
         return False
 
-    def _handle_rollback_command(self, command: str):
-        """Handle /rollback — list or restore filesystem checkpoints."""
-        from tools.checkpoint_manager import CheckpointManager, format_checkpoint_list
-
-        if not hasattr(self, 'agent') or not self.agent:
-            print("  No active agent session.")
-            return
-
-        mgr = self.agent._checkpoint_mgr
-        if not mgr.enabled:
-            print("  Checkpoints are not enabled.")
-            print("  Enable with: hermes --checkpoints")
-            print("  Or in config.yaml: checkpoints: { enabled: true }")
-            return
-
-        cwd = os.getenv("TERMINAL_CWD", os.getcwd())
-        parts = command.split(maxsplit=1)
-        arg = parts[1].strip() if len(parts) > 1 else ""
-
-        if not arg:
-            # List checkpoints
-            checkpoints = mgr.list_checkpoints(cwd)
-            print(format_checkpoint_list(checkpoints, cwd))
-        else:
-            # Restore by number or hash
-            checkpoints = mgr.list_checkpoints(cwd)
-            if not checkpoints:
-                print(f"  No checkpoints found for {cwd}")
-                return
-
-            target_hash = None
-            try:
-                idx = int(arg) - 1  # 1-indexed for user
-                if 0 <= idx < len(checkpoints):
-                    target_hash = checkpoints[idx]["hash"]
-                else:
-                    print(f"  Invalid checkpoint number. Use 1-{len(checkpoints)}.")
-                    return
-            except ValueError:
-                # Try as a git hash
-                target_hash = arg
-
-            result = mgr.restore(cwd, target_hash)
-            if result["success"]:
-                print(f"  ✅ Restored to checkpoint {result['restored_to']}: {result['reason']}")
-                print(f"  A pre-rollback snapshot was saved automatically.")
-            else:
-                print(f"  ❌ {result['error']}")
-
     def _handle_paste_command(self):
         """Handle /paste — explicitly check clipboard for an image.
 
@@ -2573,19 +2366,6 @@ class HermesCLI:
             self.show_tools()
         elif cmd_lower == "/toolsets":
             self.show_toolsets()
-        elif cmd_lower == "/agents":
-            agents_config = CLI_CONFIG.get('agents', {})
-            if not agents_config:
-                print('No agents configured. Add agents to config.yaml.')
-            else:
-                print('\n📋 Configured Agents:\n')
-                for name, cfg in agents_config.items():
-                    marker = ' (active)' if name == self._agent_id else ''
-                    model = cfg.get('model', '(inherited)')
-                    desc = cfg.get('description', '')
-                    print(f'  {name}{marker}  {model}  {desc}')
-                print()
-            return True
         elif cmd_lower == "/config":
             self.show_config()
         elif cmd_lower == "/clear":
@@ -2614,9 +2394,8 @@ class HermesCLI:
             # and gets mangled by patch_stdout).
             if self._app:
                 cc = ChatConsole()
-                term_w = shutil.get_terminal_size().columns
-                if self.compact or term_w < 80:
-                    cc.print(_build_compact_banner())
+                if self.compact:
+                    cc.print(COMPACT_BANNER)
                 else:
                     tools = get_tool_definitions(enabled_toolsets=self.enabled_toolsets, quiet_mode=True)
                     cwd = os.getenv("TERMINAL_CWD", os.getcwd())
@@ -2842,8 +2621,7 @@ class HermesCLI:
         elif cmd_lower.startswith("/cron"):
             self._handle_cron_command(cmd_original)
         elif cmd_lower.startswith("/skills"):
-            with self._busy_command(self._slow_command_status(cmd_original)):
-                self._handle_skills_command(cmd_original)
+            self._handle_skills_command(cmd_original)
         elif cmd_lower == "/platforms" or cmd_lower == "/gateway":
             self._show_gateway_status()
         elif cmd_lower == "/verbose":
@@ -2857,12 +2635,7 @@ class HermesCLI:
         elif cmd_lower == "/paste":
             self._handle_paste_command()
         elif cmd_lower == "/reload-mcp":
-            with self._busy_command(self._slow_command_status(cmd_original)):
-                self._reload_mcp()
-        elif cmd_lower.startswith("/rollback"):
-            self._handle_rollback_command(cmd_original)
-        elif cmd_lower.startswith("/skin"):
-            self._handle_skin_command(cmd_original)
+            self._reload_mcp()
         else:
             # Check for skill slash commands (/gif-search, /axolotl, etc.)
             base_cmd = cmd_lower.split()[0]
@@ -2882,43 +2655,6 @@ class HermesCLI:
         
         return True
     
-    def _handle_skin_command(self, cmd: str):
-        """Handle /skin [name] — show or change the display skin."""
-        try:
-            from hermes_cli.skin_engine import list_skins, set_active_skin, get_active_skin_name
-        except ImportError:
-            print("Skin engine not available.")
-            return
-
-        parts = cmd.strip().split(maxsplit=1)
-        if len(parts) < 2 or not parts[1].strip():
-            # Show current skin and list available
-            current = get_active_skin_name()
-            skins = list_skins()
-            print(f"\n  Current skin: {current}")
-            print(f"  Available skins:")
-            for s in skins:
-                marker = " ●" if s["name"] == current else "  "
-                source = f" ({s['source']})" if s["source"] == "user" else ""
-                print(f"   {marker} {s['name']}{source} — {s['description']}")
-            print(f"\n  Usage: /skin <name>")
-            print(f"  Custom skins: drop a YAML file in ~/.hermes/skins/\n")
-            return
-
-        new_skin = parts[1].strip().lower()
-        available = {s["name"] for s in list_skins()}
-        if new_skin not in available:
-            print(f"  Unknown skin: {new_skin}")
-            print(f"  Available: {', '.join(sorted(available))}")
-            return
-
-        set_active_skin(new_skin)
-        if save_config_value("display.skin", new_skin):
-            print(f"  Skin set to: {new_skin} (saved)")
-        else:
-            print(f"  Skin set to: {new_skin}")
-        print("  Note: banner colors will update on next session start.")
-
     def _toggle_verbose(self):
         """Cycle tool progress mode: off → new → all → verbose → off."""
         cycle = ["off", "new", "all", "verbose"]
@@ -3067,8 +2803,7 @@ class HermesCLI:
             with _lock:
                 old_servers = set(_servers.keys())
 
-            if not self._command_running:
-                print("🔄 Reloading MCP servers...")
+            print("🔄 Reloading MCP servers...")
 
             # Shutdown existing connections
             shutdown_mcp_servers()
@@ -3168,16 +2903,8 @@ class HermesCLI:
         # Trigger prompt_toolkit repaint from this (non-main) thread
         self._invalidate()
 
-        # Poll for the user's response.  The countdown in the hint line
-        # updates on each invalidate — but frequent repaints cause visible
-        # flicker in some terminals (Kitty, ghostty).  We only refresh the
-        # countdown every 5 s; selection changes (↑/↓) trigger instant
-        # Poll for the user's response.  The countdown in the hint line
-        # updates on each invalidate — but frequent repaints cause visible
-        # flicker in some terminals (Kitty, ghostty).  We only refresh the
-        # countdown every 5 s; selection changes (↑/↓) trigger instant
-        # repaints via the key bindings.
-        _last_countdown_refresh = _time.monotonic()
+        # Poll in 1-second ticks so the countdown refreshes in the UI.
+        # Each tick triggers an invalidate() to repaint the hint line.
         while True:
             try:
                 result = response_queue.get(timeout=1)
@@ -3187,14 +2914,8 @@ class HermesCLI:
                 remaining = self._clarify_deadline - _time.monotonic()
                 if remaining <= 0:
                     break
-                # Only repaint every 5 s for the countdown — avoids flicker
-                now = _time.monotonic()
-                if now - _last_countdown_refresh >= 5.0:
-                    _last_countdown_refresh = now
-                    self._invalidate()
-                if now - _last_countdown_refresh >= 5.0:
-                    _last_countdown_refresh = now
-                    self._invalidate()
+                # Repaint so the countdown updates
+                self._invalidate()
 
         # Timed out — tear down the UI and let the agent decide
         self._clarify_state = None
@@ -3274,9 +2995,6 @@ class HermesCLI:
 
         self._invalidate()
 
-        # Same throttled countdown as _clarify_callback — repaint only
-        # every 5 s to avoid flicker in Kitty / ghostty / etc.
-        _last_countdown_refresh = _time.monotonic()
         while True:
             try:
                 result = response_queue.get(timeout=1)
@@ -3288,16 +3006,11 @@ class HermesCLI:
                 remaining = self._approval_deadline - _time.monotonic()
                 if remaining <= 0:
                     break
-                now = _time.monotonic()
-                if now - _last_countdown_refresh >= 5.0:
-                    _last_countdown_refresh = now
-                    self._invalidate()
+                self._invalidate()
 
         self._approval_state = None
         self._approval_deadline = 0
         self._invalidate()
-        return "deny"
-
     def chat(self, message, images: list = None) -> Optional[str]:
         """
         Send a message to the agent and get a response.
@@ -3336,7 +3049,8 @@ class HermesCLI:
         # Add user message to history
         self.conversation_history.append({"role": "user", "content": message})
         
-        _cprint(f"{_GOLD}{'─' * 40}{_RST}")
+        w = shutil.get_terminal_size().columns
+        _cprint(f"{_GOLD}{'─' * w}{_RST}")
         print(flush=True)
         
         try:
@@ -3411,26 +3125,15 @@ class HermesCLI:
                     response = response + "\n\n---\n_[Interrupted - processing new message]_"
             
             if response:
-                # Use a Rich Panel for the response box — adapts to terminal
-                # width at render time instead of hard-coding border length.
-                try:
-                    from hermes_cli.skin_engine import get_active_skin
-                    _skin = get_active_skin()
-                    label = _skin.get_branding("response_label", "⚕ Hermes")
-                    _resp_color = _skin.get_color("response_border", "#CD7F32")
-                except Exception:
-                    label = "⚕ Hermes"
-                    _resp_color = "#CD7F32"
+                w = shutil.get_terminal_size().columns
+                label = " ⚕ Hermes "
+                fill = w - 2 - len(label)  # 2 for ╭ and ╮
+                top = f"{_GOLD}╭─{label}{'─' * max(fill - 1, 0)}╮{_RST}"
+                bot = f"{_GOLD}╰{'─' * (w - 2)}╯{_RST}"
 
-                _chat_console = ChatConsole()
-                _chat_console.print(Panel(
-                    response,
-                    title=f"[bold]{label}[/bold]",
-                    title_align="left",
-                    border_style=_resp_color,
-                    box=rich_box.HORIZONTALS,
-                    padding=(1, 2),
-                ))
+                # Render box + response as a single _cprint call so
+                # nothing can interleave between the box borders.
+                _cprint(f"\n{top}\n{response}\n\n{bot}")
             
             # Play terminal bell when agent finishes (if enabled).
             # Works over SSH — the bell propagates to the user's terminal.
@@ -3495,15 +3198,7 @@ class HermesCLI:
             if self._preload_resumed_session():
                 self._display_resumed_history()
 
-        try:
-            from hermes_cli.skin_engine import get_active_skin
-            _welcome_skin = get_active_skin()
-            _welcome_text = _welcome_skin.get_branding("welcome", "Welcome to Hermes Agent! Type your message or /help for commands.")
-            _welcome_color = _welcome_skin.get_color("banner_text", "#FFF8DC")
-        except Exception:
-            _welcome_text = "Welcome to Hermes Agent! Type your message or /help for commands."
-            _welcome_color = "#FFF8DC"
-        self.console.print(f"[{_welcome_color}]{_welcome_text}[/]")
+        self.console.print("[#FFF8DC]Welcome to Hermes Agent! Type your message or /help for commands.[/]")
         self.console.print()
         
         # State for async operation
@@ -3528,10 +3223,6 @@ class HermesCLI:
         self._approval_state = None     # dict with command, description, choices, selected, response_queue
         self._approval_deadline = 0
 
-        # Slash command loading state
-        self._command_running = False
-        self._command_status = ""
-
         # Clipboard image attachments (paste images into the CLI)
         self._attached_images: list[Path] = []
         self._image_counter = 0
@@ -3804,8 +3495,6 @@ class HermesCLI:
                 return [('class:clarify-selected', '✎ ❯ ')]
             if cli_ref._clarify_state:
                 return [('class:prompt-working', '? ❯ ')]
-            if cli_ref._command_running:
-                return [('class:prompt-working', f"{cli_ref._command_spinner_frame()} ❯ ")]
             if cli_ref._agent_running:
                 return [('class:prompt-working', '⚕ ❯ ')]
             return [('class:prompt', '❯ ')]
@@ -3817,7 +3506,6 @@ class HermesCLI:
             style='class:input-area',
             multiline=True,
             wrap_lines=True,
-            read_only=Condition(lambda: bool(cli_ref._command_running)),
             history=FileHistory(str(self._history_file)),
             completer=SlashCommandCompleter(skill_commands_provider=lambda: _skill_commands),
             complete_while_typing=True,
@@ -3898,14 +3586,8 @@ class HermesCLI:
                 return "type password (hidden), Enter to skip"
             if cli_ref._approval_state:
                 return ""
-            if cli_ref._clarify_freetext:
-                return "type your answer here and press Enter"
             if cli_ref._clarify_state:
                 return ""
-            if cli_ref._command_running:
-                frame = cli_ref._command_spinner_frame()
-                status = cli_ref._command_status or "Processing command..."
-                return f"{frame} {status}"
             if cli_ref._agent_running:
                 return "type a message + Enter to interrupt, Ctrl+C to cancel"
             return ""
@@ -3945,35 +3627,15 @@ class HermesCLI:
                     ('class:clarify-countdown', countdown),
                 ]
 
-            if cli_ref._command_running:
-                frame = cli_ref._command_spinner_frame()
-                return [
-                    ('class:hint', f'  {frame} command in progress · input temporarily disabled'),
-                ]
-
             return []
 
         def get_hint_height():
-            if cli_ref._sudo_state or cli_ref._approval_state or cli_ref._clarify_state or cli_ref._command_running:
+            if cli_ref._sudo_state or cli_ref._approval_state or cli_ref._clarify_state:
                 return 1
             # Keep a 1-line spacer while agent runs so output doesn't push
             # right up against the top rule of the input area
             return 1 if cli_ref._agent_running else 0
 
-        def get_spinner_text():
-            txt = cli_ref._spinner_text
-            if not txt:
-                return []
-            return [('class:hint', f'  {txt}')]
-
-        def get_spinner_height():
-            return 1 if cli_ref._spinner_text else 0
-
-        spinner_widget = Window(
-            content=FormattedTextControl(get_spinner_text),
-            height=get_spinner_height,
-        )
-
         spacer = Window(
             content=FormattedTextControl(get_hint_text),
             height=get_hint_height,
@@ -3981,32 +3643,6 @@ class HermesCLI:
 
         # --- Clarify tool: dynamic display widget for questions + choices ---
 
-        def _panel_box_width(title: str, content_lines: list[str], min_width: int = 46, max_width: int = 76) -> int:
-            """Choose a stable panel width wide enough for the title and content."""
-            term_cols = shutil.get_terminal_size((100, 20)).columns
-            longest = max([len(title)] + [len(line) for line in content_lines] + [min_width - 4])
-            inner = min(max(longest + 4, min_width - 2), max_width - 2, max(24, term_cols - 6))
-            return inner + 2  # account for the single leading/trailing spaces inside borders
-
-        def _wrap_panel_text(text: str, width: int, subsequent_indent: str = "") -> list[str]:
-            wrapped = textwrap.wrap(
-                text,
-                width=max(8, width),
-                break_long_words=False,
-                break_on_hyphens=False,
-                subsequent_indent=subsequent_indent,
-            )
-            return wrapped or [""]
-
-        def _append_panel_line(lines, border_style: str, content_style: str, text: str, box_width: int) -> None:
-            inner_width = max(0, box_width - 2)
-            lines.append((border_style, "│ "))
-            lines.append((content_style, text.ljust(inner_width)))
-            lines.append((border_style, " │\n"))
-
-        def _append_blank_panel_line(lines, border_style: str, box_width: int) -> None:
-            lines.append((border_style, "│" + (" " * box_width) + "│\n"))
-
         def _get_clarify_display():
             """Build styled text for the clarify question/choices panel."""
             state = cli_ref._clarify_state
@@ -4016,62 +3652,43 @@ class HermesCLI:
             question = state["question"]
             choices = state.get("choices") or []
             selected = state.get("selected", 0)
-            preview_lines = _wrap_panel_text(question, 60)
-            for i, choice in enumerate(choices):
-                prefix = "❯ " if i == selected and not cli_ref._clarify_freetext else "  "
-                preview_lines.extend(_wrap_panel_text(f"{prefix}{choice}", 60, subsequent_indent="  "))
-            other_label = (
-                "❯ Other (type below)" if cli_ref._clarify_freetext
-                else "❯ Other (type your answer)" if selected == len(choices)
-                else "  Other (type your answer)"
-            )
-            preview_lines.extend(_wrap_panel_text(other_label, 60, subsequent_indent="  "))
-            box_width = _panel_box_width("Hermes needs your input", preview_lines)
-            inner_text_width = max(8, box_width - 2)
 
             lines = []
             # Box top border
             lines.append(('class:clarify-border', '╭─ '))
             lines.append(('class:clarify-title', 'Hermes needs your input'))
-            lines.append(('class:clarify-border', ' ' + ('─' * max(0, box_width - len("Hermes needs your input") - 3)) + '╮\n'))
-            _append_blank_panel_line(lines, 'class:clarify-border', box_width)
+            lines.append(('class:clarify-border', ' ─────────────────────────────╮\n'))
+            lines.append(('class:clarify-border', '│\n'))
 
             # Question text
-            for wrapped in _wrap_panel_text(question, inner_text_width):
-                _append_panel_line(lines, 'class:clarify-border', 'class:clarify-question', wrapped, box_width)
-            _append_blank_panel_line(lines, 'class:clarify-border', box_width)
-
-            if cli_ref._clarify_freetext and not choices:
-                guidance = "Type your answer in the prompt below, then press Enter."
-                for wrapped in _wrap_panel_text(guidance, inner_text_width):
-                    _append_panel_line(lines, 'class:clarify-border', 'class:clarify-choice', wrapped, box_width)
-                _append_blank_panel_line(lines, 'class:clarify-border', box_width)
+            lines.append(('class:clarify-border', '│  '))
+            lines.append(('class:clarify-question', question))
+            lines.append(('', '\n'))
+            lines.append(('class:clarify-border', '│\n'))
 
             if choices:
                 # Multiple-choice mode: show selectable options
                 for i, choice in enumerate(choices):
-                    style = 'class:clarify-selected' if i == selected and not cli_ref._clarify_freetext else 'class:clarify-choice'
-                    prefix = '❯ ' if i == selected and not cli_ref._clarify_freetext else '  '
-                    wrapped_lines = _wrap_panel_text(f"{prefix}{choice}", inner_text_width, subsequent_indent="  ")
-                    for wrapped in wrapped_lines:
-                        _append_panel_line(lines, 'class:clarify-border', style, wrapped, box_width)
+                    lines.append(('class:clarify-border', '│  '))
+                    if i == selected and not cli_ref._clarify_freetext:
+                        lines.append(('class:clarify-selected', f'❯ {choice}'))
+                    else:
+                        lines.append(('class:clarify-choice', f'  {choice}'))
+                    lines.append(('', '\n'))
 
                 # "Other" option (5th line, only shown when choices exist)
                 other_idx = len(choices)
+                lines.append(('class:clarify-border', '│  '))
                 if selected == other_idx and not cli_ref._clarify_freetext:
-                    other_style = 'class:clarify-selected'
-                    other_label = '❯ Other (type your answer)'
+                    lines.append(('class:clarify-selected', '❯ Other (type your answer)'))
                 elif cli_ref._clarify_freetext:
-                    other_style = 'class:clarify-active-other'
-                    other_label = '❯ Other (type below)'
+                    lines.append(('class:clarify-active-other', '❯ Other (type below)'))
                 else:
-                    other_style = 'class:clarify-choice'
-                    other_label = '  Other (type your answer)'
-                for wrapped in _wrap_panel_text(other_label, inner_text_width, subsequent_indent="  "):
-                    _append_panel_line(lines, 'class:clarify-border', other_style, wrapped, box_width)
+                    lines.append(('class:clarify-choice', '  Other (type your answer)'))
+                lines.append(('', '\n'))
 
-            _append_blank_panel_line(lines, 'class:clarify-border', box_width)
-            lines.append(('class:clarify-border', '╰' + ('─' * box_width) + '╯\n'))
+            lines.append(('class:clarify-border', '│\n'))
+            lines.append(('class:clarify-border', '╰──────────────────────────────────────────────────╯\n'))
             return lines
 
         clarify_widget = ConditionalContainer(
@@ -4088,18 +3705,16 @@ class HermesCLI:
             state = cli_ref._sudo_state
             if not state:
                 return []
-            title = '🔐 Sudo Password Required'
-            body = 'Enter password below (hidden), or press Enter to skip'
-            box_width = _panel_box_width(title, [body])
-            inner = max(0, box_width - 2)
             lines = []
             lines.append(('class:sudo-border', '╭─ '))
-            lines.append(('class:sudo-title', title))
-            lines.append(('class:sudo-border', ' ' + ('─' * max(0, box_width - len(title) - 3)) + '╮\n'))
-            _append_blank_panel_line(lines, 'class:sudo-border', box_width)
-            _append_panel_line(lines, 'class:sudo-border', 'class:sudo-text', body, box_width)
-            _append_blank_panel_line(lines, 'class:sudo-border', box_width)
-            lines.append(('class:sudo-border', '╰' + ('─' * box_width) + '╯\n'))
+            lines.append(('class:sudo-title', '🔐 Sudo Password Required'))
+            lines.append(('class:sudo-border', ' ──────────────────────────╮\n'))
+            lines.append(('class:sudo-border', '│\n'))
+            lines.append(('class:sudo-border', '│  '))
+            lines.append(('class:sudo-text', 'Enter password below (hidden), or press Enter to skip'))
+            lines.append(('', '\n'))
+            lines.append(('class:sudo-border', '│\n'))
+            lines.append(('class:sudo-border', '╰──────────────────────────────────────────────────╯\n'))
             return lines
 
         sudo_widget = ConditionalContainer(
@@ -4128,32 +3743,29 @@ class HermesCLI:
                 "always": "Add to permanent allowlist",
                 "deny": "Deny",
             }
-            preview_lines = _wrap_panel_text(description, 60)
-            preview_lines.extend(_wrap_panel_text(cmd_display, 60))
-            for i, choice in enumerate(choices):
-                prefix = '❯ ' if i == selected else '  '
-                preview_lines.extend(_wrap_panel_text(f"{prefix}{choice_labels.get(choice, choice)}", 60, subsequent_indent="  "))
-            box_width = _panel_box_width("⚠️  Dangerous Command", preview_lines)
-            inner_text_width = max(8, box_width - 2)
 
             lines = []
             lines.append(('class:approval-border', '╭─ '))
             lines.append(('class:approval-title', '⚠️  Dangerous Command'))
-            lines.append(('class:approval-border', ' ' + ('─' * max(0, box_width - len("⚠️  Dangerous Command") - 3)) + '╮\n'))
-            _append_blank_panel_line(lines, 'class:approval-border', box_width)
-            for wrapped in _wrap_panel_text(description, inner_text_width):
-                _append_panel_line(lines, 'class:approval-border', 'class:approval-desc', wrapped, box_width)
-            for wrapped in _wrap_panel_text(cmd_display, inner_text_width):
-                _append_panel_line(lines, 'class:approval-border', 'class:approval-cmd', wrapped, box_width)
-            _append_blank_panel_line(lines, 'class:approval-border', box_width)
+            lines.append(('class:approval-border', ' ───────────────────────────────╮\n'))
+            lines.append(('class:approval-border', '│\n'))
+            lines.append(('class:approval-border', '│  '))
+            lines.append(('class:approval-desc', description))
+            lines.append(('', '\n'))
+            lines.append(('class:approval-border', '│  '))
+            lines.append(('class:approval-cmd', cmd_display))
+            lines.append(('', '\n'))
+            lines.append(('class:approval-border', '│\n'))
             for i, choice in enumerate(choices):
+                lines.append(('class:approval-border', '│  '))
                 label = choice_labels.get(choice, choice)
-                style = 'class:approval-selected' if i == selected else 'class:approval-choice'
-                prefix = '❯ ' if i == selected else '  '
-                for wrapped in _wrap_panel_text(f"{prefix}{label}", inner_text_width, subsequent_indent="  "):
-                    _append_panel_line(lines, 'class:approval-border', style, wrapped, box_width)
-            _append_blank_panel_line(lines, 'class:approval-border', box_width)
-            lines.append(('class:approval-border', '╰' + ('─' * box_width) + '╯\n'))
+                if i == selected:
+                    lines.append(('class:approval-selected', f'❯ {label}'))
+                else:
+                    lines.append(('class:approval-choice', f'  {label}'))
+                lines.append(('', '\n'))
+            lines.append(('class:approval-border', '│\n'))
+            lines.append(('class:approval-border', '╰──────────────────────────────────────────────────────╯\n'))
             return lines
 
         approval_widget = ConditionalContainer(
@@ -4206,7 +3818,6 @@ class HermesCLI:
                 sudo_widget,
                 approval_widget,
                 clarify_widget,
-                spinner_widget,
                 spacer,
                 input_rule_top,
                 image_bar,
@@ -4261,22 +3872,8 @@ class HermesCLI:
             style=style,
             full_screen=False,
             mouse_support=False,
-            **({'cursor': _STEADY_CURSOR} if _STEADY_CURSOR is not None else {}),
         )
         self._app = app  # Store reference for clarify_callback
-
-        def spinner_loop():
-            import time as _time
-
-            while not self._should_exit:
-                if self._command_running and self._app:
-                    self._invalidate(min_interval=0.1)
-                    _time.sleep(0.1)
-                else:
-                    _time.sleep(0.05)
-
-        spinner_thread = threading.Thread(target=spinner_loop, daemon=True)
-        spinner_thread.start()
         
         # Background thread to process inputs and run agent
         def process_loop():
@@ -4343,7 +3940,6 @@ class HermesCLI:
                         self.chat(user_input, images=submit_images or None)
                     finally:
                         self._agent_running = False
-                        self._spinner_text = ""
                         app.invalidate()  # Refresh status line
                     
                 except Exception as e:
@@ -4397,7 +3993,6 @@ def main(
     base_url: str = None,
     max_turns: int = None,
     verbose: bool = False,
-    quiet: bool = False,
     compact: bool = False,
     list_tools: bool = False,
     list_toolsets: bool = False,
@@ -4405,8 +4000,6 @@ def main(
     resume: str = None,
     worktree: bool = False,
     w: bool = False,
-    checkpoints: bool = False,
-    agent: str = None,
 ):
     """
     Hermes Agent CLI - Interactive AI Assistant
@@ -4511,8 +4104,6 @@ def main(
         verbose=verbose,
         compact=compact,
         resume=resume,
-        checkpoints=checkpoints,
-        agent=agent,
     )
 
     # Inject worktree context into agent's system prompt
@@ -4542,22 +4133,10 @@ def main(
     
     # Handle single query mode
     if query:
-        if quiet:
-            # Quiet mode: suppress banner, spinner, tool previews.
-            # Only print the final response and parseable session info.
-            cli.tool_progress_mode = "off"
-            if cli._init_agent():
-                cli.agent.quiet_mode = True
-                result = cli.agent.run_conversation(query)
-                response = result.get("final_response", "") if isinstance(result, dict) else str(result)
-                if response:
-                    print(response)
-                print(f"\nsession_id: {cli.session_id}")
-        else:
-            cli.show_banner()
-            cli.console.print(f"[bold blue]Query:[/] {query}")
-            cli.chat(query)
-            cli._print_exit_summary()
+        cli.show_banner()
+        cli.console.print(f"[bold blue]Query:[/] {query}")
+        cli.chat(query)
+        cli._print_exit_summary()
         return
     
     # Run interactive mode

cron/jobs.py

@@ -26,7 +26,7 @@ except ImportError:
 # Configuration
 # =============================================================================
 
-HERMES_DIR = Path(os.getenv("HERMES_HOME", Path.home() / ".hermes"))
+HERMES_DIR = Path.home() / ".hermes"
 CRON_DIR = HERMES_DIR / "cron"
 JOBS_FILE = CRON_DIR / "jobs.json"
 OUTPUT_DIR = CRON_DIR / "output"

cron/scheduler.py

@@ -138,8 +138,8 @@ def _deliver_result(job: dict, content: str) -> None:
         try:
             from gateway.mirror import mirror_to_session
             mirror_to_session(platform_name, chat_id, content, source_label="cron")
-        except Exception as e:
-            logger.warning("Job '%s': mirror_to_session failed: %s", job["id"], e)
+        except Exception:
+            pass
 
 
 def run_job(job: dict) -> tuple[bool, str, str, Optional[str]]:
@@ -190,8 +190,8 @@ def run_job(job: dict) -> tuple[bool, str, str, Optional[str]]:
                     model = _model_cfg
                 elif isinstance(_model_cfg, dict):
                     model = _model_cfg.get("default", model)
-        except Exception as e:
-            logger.warning("Job '%s': failed to load config.yaml, using defaults: %s", job_id, e)
+        except Exception:
+            pass
 
         # Reasoning config from env or config.yaml
         reasoning_config = None
@@ -219,8 +219,7 @@ def run_job(job: dict) -> tuple[bool, str, str, Optional[str]]:
                         prefill_messages = _json.load(_pf)
                     if not isinstance(prefill_messages, list):
                         prefill_messages = None
-                except Exception as e:
-                    logger.warning("Job '%s': failed to parse prefill messages file '%s': %s", job_id, pfpath, e)
+                except Exception:
                     prefill_messages = None
 
         # Max iterations

datagen-config-examples/web_research.yaml

@@ -1,46 +0,0 @@
-# datagen-config-examples/web_research.yaml
-#
-# Batch data generation config for WebResearchEnv.
-# Generates tool-calling trajectories for multi-step web research tasks.
-#
-# Usage:
-#   python batch_runner.py \
-#     --config datagen-config-examples/web_research.yaml \
-#     --run_name web_research_v1
-
-environment: web-research
-
-# Toolsets available to the agent during data generation
-toolsets:
-  - web
-  - file
-
-# How many parallel workers to use
-num_workers: 4
-
-# Questions per batch
-batch_size: 20
-
-# Total trajectories to generate (comment out to run full dataset)
-max_items: 500
-
-# Model to use for generation (override with --model flag)
-model: openrouter/nousresearch/hermes-3-llama-3.1-405b
-
-# System prompt additions (ephemeral — not saved to trajectories)
-ephemeral_system_prompt: |
-  You are a highly capable research agent. When asked a factual question,
-  always use web_search to find current, accurate information before answering.
-  Cite at least 2 sources. Be concise and accurate.
-
-# Output directory
-output_dir: data/web_research_v1
-
-# Trajectory compression settings (for fitting into training token budgets)
-compression:
-  enabled: true
-  target_max_tokens: 16000
-
-# Eval settings
-eval_every: 100       # Run eval every N trajectories
-eval_size: 25         # Number of held-out questions per eval run

docs/skins/example-skin.yaml

@@ -1,89 +0,0 @@
-# ============================================================================
-# Hermes Agent — Example Skin Template
-# ============================================================================
-#
-# Copy this file to ~/.hermes/skins/<name>.yaml to create a custom skin.
-# All fields are optional — missing values inherit from the default skin.
-# Activate with: /skin <name>  or  display.skin: <name> in config.yaml
-#
-# See hermes_cli/skin_engine.py for the full schema reference.
-# ============================================================================
-
-# Required: unique skin name (used in /skin command and config)
-name: example
-description: An example custom skin — copy and modify this template
-
-# ── Colors ──────────────────────────────────────────────────────────────────
-# Hex color values for Rich markup. These control the CLI's visual palette.
-colors:
-  # Banner panel (the startup welcome box)
-  banner_border: "#CD7F32"        # Panel border
-  banner_title: "#FFD700"         # Panel title text
-  banner_accent: "#FFBF00"        # Section headers (Available Tools, Skills, etc.)
-  banner_dim: "#B8860B"           # Dim/muted text (separators, model info)
-  banner_text: "#FFF8DC"          # Body text (tool names, skill names)
-
-  # UI elements
-  ui_accent: "#FFBF00"            # General accent color
-  ui_label: "#4dd0e1"             # Labels
-  ui_ok: "#4caf50"                # Success indicators
-  ui_error: "#ef5350"             # Error indicators
-  ui_warn: "#ffa726"              # Warning indicators
-
-  # Input area
-  prompt: "#FFF8DC"               # Prompt text color
-  input_rule: "#CD7F32"           # Horizontal rule around input
-
-  # Response box
-  response_border: "#FFD700"      # Response box border (ANSI color)
-
-  # Session display
-  session_label: "#DAA520"        # Session label
-  session_border: "#8B8682"       # Session ID dim color
-
-# ── Spinner ─────────────────────────────────────────────────────────────────
-# Customize the animated spinner shown during API calls and tool execution.
-spinner:
-  # Faces shown while waiting for the API response
-  waiting_faces:
-    - "(｡◕‿◕｡)"
-    - "(◕‿◕✿)"
-    - "٩(◕‿◕｡)۶"
-
-  # Faces shown during extended thinking/reasoning
-  thinking_faces:
-    - "(｡•́︿•̀｡)"
-    - "(◔_◔)"
-    - "(¬‿¬)"
-
-  # Verbs used in spinner messages (e.g., "pondering your request...")
-  thinking_verbs:
-    - "pondering"
-    - "contemplating"
-    - "musing"
-    - "ruminating"
-
-  # Optional: left/right decorations around the spinner
-  # Each entry is a [left, right] pair. Omit entirely for no wings.
-  # wings:
-  #   - ["⟪⚔", "⚔⟫"]
-  #   - ["⟪▲", "▲⟫"]
-
-# ── Branding ────────────────────────────────────────────────────────────────
-# Text strings used throughout the CLI interface.
-branding:
-  agent_name: "Hermes Agent"          # Banner title, about display
-  welcome: "Welcome! Type your message or /help for commands."
-  goodbye: "Goodbye! ⚕"              # Exit message
-  response_label: " ⚕ Hermes "       # Response box header label
-  prompt_symbol: "❯ "                 # Input prompt symbol
-  help_header: "(^_^)? Available Commands"  # /help header text
-
-# ── Tool Output ─────────────────────────────────────────────────────────────
-# Character used as the prefix for tool output lines.
-# Default is "┊" (thin dotted vertical line). Some alternatives:
-#   "╎" (light triple dash vertical)
-#   "▏" (left one-eighth block)
-#   "│" (box drawing light vertical)
-#   "┃" (box drawing heavy vertical)
-tool_prefix: "┊"

environments/benchmarks/terminalbench_2/default.yaml

@@ -29,10 +29,6 @@ env:
   wandb_name: "terminal-bench-2"
   ensure_scores_are_not_same: false
   data_dir_to_save_evals: "environments/benchmarks/evals/terminal-bench-2"
-  # CRITICAL: Limit concurrent Modal sandbox creations to avoid deadlocks.
-  # Modal's blocking calls (App.lookup, etc.) deadlock when too many sandboxes
-  # are created simultaneously inside thread pool workers via asyncio.run().
-  max_concurrent_tasks: 8
 
 openai:
   base_url: "https://openrouter.ai/api/v1"

environments/benchmarks/terminalbench_2/terminalbench2_env.py

@@ -118,15 +118,6 @@ class TerminalBench2EvalConfig(HermesAgentEnvConfig):
         "Tasks exceeding this are scored as FAIL. Default 30 minutes.",
     )
 
-    # --- Concurrency control ---
-    max_concurrent_tasks: int = Field(
-        default=8,
-        description="Maximum number of tasks to run concurrently. "
-        "Limits concurrent Modal sandbox creations to avoid async/threading deadlocks. "
-        "Modal has internal limits and creating too many sandboxes simultaneously "
-        "causes blocking calls to deadlock inside the thread pool.",
-    )
-
 
 # Tasks that cannot run properly on Modal and are excluded from scoring.
 MODAL_INCOMPATIBLE_TASKS = {
@@ -439,7 +430,7 @@ class TerminalBench2EvalEnv(HermesAgentBaseEnv):
                 }
 
             # --- 2. Register per-task Modal image override ---
-            register_task_env_overrides(task_id, {"modal_image": modal_image, "cwd": "/app"})
+            register_task_env_overrides(task_id, {"modal_image": modal_image})
             logger.info(
                 "Task %s: registered image override for task_id %s",
                 task_name, task_id[:8],
@@ -742,23 +733,12 @@ class TerminalBench2EvalEnv(HermesAgentBaseEnv):
         print(f"  Tool thread pool: {self.config.tool_pool_size}")
         print(f"  Terminal timeout: {self.config.terminal_timeout}s/cmd")
         print(f"  Terminal lifetime: {self.config.terminal_lifetime}s (auto: task_timeout + 120)")
-        print(f"  Max concurrent tasks: {self.config.max_concurrent_tasks}")
         print(f"{'='*60}\n")
 
-        # Semaphore to limit concurrent Modal sandbox creations.
-        # Without this, all 86 tasks fire simultaneously, each creating a Modal
-        # sandbox via asyncio.run() inside a thread pool worker. Modal's blocking
-        # calls (App.lookup, etc.) deadlock when too many are created at once.
-        semaphore = asyncio.Semaphore(self.config.max_concurrent_tasks)
-
-        async def _eval_with_semaphore(item):
-            async with semaphore:
-                return await self._eval_with_timeout(item)
-
         # Fire all tasks with wall-clock timeout, track live accuracy on the bar
         total_tasks = len(self.all_eval_items)
         eval_tasks = [
-            asyncio.ensure_future(_eval_with_semaphore(item))
+            asyncio.ensure_future(self._eval_with_timeout(item))
             for item in self.all_eval_items
         ]
 

environments/web_research_env.py

@@ -1,718 +0,0 @@
-"""
-WebResearchEnv — RL Environment for Multi-Step Web Research
-============================================================
-
-Trains models to do accurate, efficient, multi-source web research.
-
-Reward signals:
-  - Answer correctness  (LLM judge, 0.0–1.0)
-  - Source diversity    (used ≥2 distinct domains)
-  - Efficiency          (penalizes excessive tool calls)
-  - Tool usage          (bonus for actually using web tools)
-
-Dataset: FRAMES benchmark (Google, 2024) — multi-hop factual questions
-  HuggingFace: google/frames-benchmark
-  Fallback:    built-in sample questions (no HF token needed)
-
-Usage:
-    # Phase 1 (OpenAI-compatible server)
-    python environments/web_research_env.py serve \\
-        --openai.base_url http://localhost:8000/v1 \\
-        --openai.model_name YourModel \\
-        --openai.server_type openai
-
-    # Process mode (offline data generation)
-    python environments/web_research_env.py process \\
-        --env.data_path_to_save_groups data/web_research.jsonl
-
-    # Standalone eval
-    python environments/web_research_env.py evaluate \\
-        --openai.base_url http://localhost:8000/v1 \\
-        --openai.model_name YourModel
-
-Built by: github.com/jackx707
-Inspired by: GroceryMind — production Hermes agent doing live web research
-             across German grocery stores (firecrawl + hermes-agent)
-"""
-
-from __future__ import annotations
-
-import asyncio
-import json
-import logging
-import os
-import random
-import re
-import sys
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple
-from urllib.parse import urlparse
-
-from pydantic import Field
-
-# Ensure hermes-agent root is on path
-_repo_root = Path(__file__).resolve().parent.parent
-if str(_repo_root) not in sys.path:
-    sys.path.insert(0, str(_repo_root))
-
-# ---------------------------------------------------------------------------
-# Optional HuggingFace datasets import
-# ---------------------------------------------------------------------------
-try:
-    from datasets import load_dataset
-    HF_AVAILABLE = True
-except ImportError:
-    HF_AVAILABLE = False
-
-from atroposlib.envs.base import ScoredDataGroup
-from atroposlib.envs.server_handling.server_manager import APIServerConfig
-from atroposlib.type_definitions import Item
-
-from environments.hermes_base_env import HermesAgentBaseEnv, HermesAgentEnvConfig
-from environments.agent_loop import AgentResult
-from environments.tool_context import ToolContext
-
-logger = logging.getLogger(__name__)
-
-# ---------------------------------------------------------------------------
-# Fallback sample dataset (used when HuggingFace is unavailable)
-# Multi-hop questions requiring real web search to answer.
-# ---------------------------------------------------------------------------
-SAMPLE_QUESTIONS = [
-    {
-        "question": "What is the current population of the capital city of the country that won the 2022 FIFA World Cup?",
-        "answer": "Buenos Aires has approximately 3 million people in the city proper, or around 15 million in the greater metro area.",
-        "difficulty": "medium",
-        "hops": 2,
-    },
-    {
-        "question": "Who is the CEO of the company that makes the most widely used open-source container orchestration platform?",
-        "answer": "The Linux Foundation oversees Kubernetes. CNCF (Cloud Native Computing Foundation) is the specific body — it does not have a traditional CEO but has an executive director.",
-        "difficulty": "medium",
-        "hops": 2,
-    },
-    {
-        "question": "What programming language was used to write the original version of the web framework used by Instagram?",
-        "answer": "Django, which Instagram was built on, is written in Python.",
-        "difficulty": "easy",
-        "hops": 2,
-    },
-    {
-        "question": "In what year was the university founded where the inventor of the World Wide Web currently holds a professorship?",
-        "answer": "Tim Berners-Lee holds a professorship at MIT (founded 1861) and the University of Southampton (founded 1952).",
-        "difficulty": "hard",
-        "hops": 3,
-    },
-    {
-        "question": "What is the latest stable version of the programming language that ranks #1 on the TIOBE index as of this year?",
-        "answer": "Python is currently #1 on TIOBE. The latest stable version should be verified via the official python.org site.",
-        "difficulty": "medium",
-        "hops": 2,
-    },
-    {
-        "question": "How many employees does the parent company of Instagram have?",
-        "answer": "Meta Platforms (parent of Instagram) employs approximately 70,000+ people as of recent reports.",
-        "difficulty": "medium",
-        "hops": 2,
-    },
-    {
-        "question": "What is the current interest rate set by the central bank of the country where the Eiffel Tower is located?",
-        "answer": "The European Central Bank sets rates for France/eurozone. The current rate should be verified — it has changed frequently in 2023-2025.",
-        "difficulty": "hard",
-        "hops": 2,
-    },
-    {
-        "question": "Which company acquired the startup founded by the creator of Oculus VR?",
-        "answer": "Palmer Luckey founded Oculus VR, which was acquired by Facebook (now Meta). He later founded Anduril Industries.",
-        "difficulty": "medium",
-        "hops": 2,
-    },
-    {
-        "question": "What is the market cap of the company that owns the most popular search engine in Russia?",
-        "answer": "Yandex (now split into separate entities after 2024 restructuring). Current market cap should be verified via financial sources.",
-        "difficulty": "hard",
-        "hops": 2,
-    },
-    {
-        "question": "What was the GDP growth rate of the country that hosted the most recent Summer Olympics?",
-        "answer": "Paris, France hosted the 2024 Summer Olympics. France's recent GDP growth should be verified via World Bank or IMF data.",
-        "difficulty": "hard",
-        "hops": 2,
-    },
-]
-
-
-# ---------------------------------------------------------------------------
-# Configuration
-# ---------------------------------------------------------------------------
-
-class WebResearchEnvConfig(HermesAgentEnvConfig):
-    """Configuration for the web research RL environment."""
-
-    # Reward weights
-    correctness_weight: float = Field(
-        default=0.6,
-        description="Weight for answer correctness in reward (LLM judge score).",
-    )
-    tool_usage_weight: float = Field(
-        default=0.2,
-        description="Weight for tool usage signal (did the model actually use web tools?).",
-    )
-    efficiency_weight: float = Field(
-        default=0.2,
-        description="Weight for efficiency signal (penalizes excessive tool calls).",
-    )
-    diversity_bonus: float = Field(
-        default=0.1,
-        description="Bonus reward for citing ≥2 distinct domains.",
-    )
-
-    # Efficiency thresholds
-    efficient_max_calls: int = Field(
-        default=5,
-        description="Maximum tool calls before efficiency penalty begins.",
-    )
-    heavy_penalty_calls: int = Field(
-        default=10,
-        description="Tool call count where efficiency penalty steepens.",
-    )
-
-    # Eval
-    eval_size: int = Field(
-        default=20,
-        description="Number of held-out items for evaluation.",
-    )
-    eval_split_ratio: float = Field(
-        default=0.1,
-        description="Fraction of dataset to hold out for evaluation (0.0–1.0).",
-    )
-
-    # Dataset
-    dataset_name: str = Field(
-        default="google/frames-benchmark",
-        description="HuggingFace dataset name for research questions.",
-    )
-
-
-# ---------------------------------------------------------------------------
-# Environment
-# ---------------------------------------------------------------------------
-
-class WebResearchEnv(HermesAgentBaseEnv):
-    """
-    RL environment for training multi-step web research skills.
-
-    The model is given a factual question requiring 2-3 hops of web research
-    and must use web_search / web_extract tools to find and synthesize the answer.
-
-    Reward is multi-signal:
-      60% — answer correctness (LLM judge)
-      20% — tool usage (did the model actually search the web?)
-      20% — efficiency (penalizes >5 tool calls)
-
-    Bonus +0.1 for source diversity (≥2 distinct domains cited).
-    """
-
-    name = "web-research"
-    env_config_cls = WebResearchEnvConfig
-
-    # Default toolsets for this environment — web + file for saving notes
-    default_toolsets = ["web", "file"]
-
-    @classmethod
-    def config_init(cls) -> Tuple[WebResearchEnvConfig, List[APIServerConfig]]:
-        """Default configuration for the web research environment."""
-        env_config = WebResearchEnvConfig(
-            enabled_toolsets=["web", "file"],
-            max_agent_turns=15,
-            agent_temperature=1.0,
-            system_prompt=(
-                "You are a highly capable research agent. When asked a factual question, "
-                "always use web_search to find current, accurate information before answering. "
-                "Cite at least 2 sources. Be concise and accurate."
-            ),
-            group_size=4,
-            total_steps=1000,
-            steps_per_eval=100,
-            use_wandb=True,
-            wandb_name="web-research",
-        )
-
-        server_configs = [
-            APIServerConfig(
-                base_url="https://openrouter.ai/api/v1",
-                model_name="anthropic/claude-sonnet-4.5",
-                server_type="openai",
-                api_key=os.getenv("OPENROUTER_API_KEY", ""),
-                health_check=False,
-            )
-        ]
-
-        return env_config, server_configs
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self._items: list[dict] = []
-        self._eval_items: list[dict] = []
-        self._index: int = 0
-
-        # Metrics tracking for wandb
-        self._reward_buffer: list[float] = []
-        self._correctness_buffer: list[float] = []
-        self._tool_usage_buffer: list[float] = []
-        self._efficiency_buffer: list[float] = []
-        self._diversity_buffer: list[float] = []
-
-    # ------------------------------------------------------------------
-    # 1. Setup — load dataset
-    # ------------------------------------------------------------------
-
-    async def setup(self) -> None:
-        """Load the FRAMES benchmark or fall back to built-in samples."""
-        if HF_AVAILABLE:
-            try:
-                logger.info("Loading FRAMES benchmark from HuggingFace...")
-                ds = load_dataset(self.config.dataset_name, split="test")
-                self._items = [
-                    {
-                        "question": row["Prompt"],
-                        "answer": row["Answer"],
-                        "difficulty": row.get("reasoning_types", "unknown"),
-                        "hops": 2,
-                    }
-                    for row in ds
-                ]
-                # Hold out for eval
-                eval_size = max(
-                    self.config.eval_size,
-                    int(len(self._items) * self.config.eval_split_ratio),
-                )
-                random.shuffle(self._items)
-                self._eval_items = self._items[:eval_size]
-                self._items = self._items[eval_size:]
-                logger.info(
-                    f"Loaded {len(self._items)} train / {len(self._eval_items)} eval items "
-                    f"from FRAMES benchmark."
-                )
-                return
-            except Exception as e:
-                logger.warning(f"Could not load FRAMES from HuggingFace: {e}. Using built-in samples.")
-
-        # Fallback
-        random.shuffle(SAMPLE_QUESTIONS)
-        split = max(1, len(SAMPLE_QUESTIONS) * 8 // 10)
-        self._items = SAMPLE_QUESTIONS[:split]
-        self._eval_items = SAMPLE_QUESTIONS[split:]
-        logger.info(
-            f"Using built-in sample dataset: {len(self._items)} train / "
-            f"{len(self._eval_items)} eval items."
-        )
-
-    # ------------------------------------------------------------------
-    # 2. get_next_item — return the next question
-    # ------------------------------------------------------------------
-
-    async def get_next_item(self) -> dict:
-        """Return the next item, cycling through the dataset."""
-        if not self._items:
-            raise RuntimeError("Dataset is empty. Did you call setup()?")
-        item = self._items[self._index % len(self._items)]
-        self._index += 1
-        return item
-
-    # ------------------------------------------------------------------
-    # 3. format_prompt — build the user-facing prompt
-    # ------------------------------------------------------------------
-
-    def format_prompt(self, item: dict) -> str:
-        """Format the research question as a task prompt."""
-        return (
-            f"Research the following question thoroughly using web search. "
-            f"You MUST search the web to find current, accurate information — "
-            f"do not rely solely on your training data.\n\n"
-            f"Question: {item['question']}\n\n"
-            f"Requirements:\n"
-            f"- Use web_search and/or web_extract tools to find information\n"
-            f"- Search at least 2 different sources\n"
-            f"- Provide a concise, accurate answer (2-4 sentences)\n"
-            f"- Cite the sources you used"
-        )
-
-    # ------------------------------------------------------------------
-    # 4. compute_reward — multi-signal scoring
-    # ------------------------------------------------------------------
-
-    async def compute_reward(
-        self,
-        item: dict,
-        result: AgentResult,
-        ctx: ToolContext,
-    ) -> float:
-        """
-        Multi-signal reward function:
-
-          correctness_weight * correctness  — LLM judge comparing answer to ground truth
-          tool_usage_weight  * tool_used    — binary: did the model use web tools?
-          efficiency_weight  * efficiency   — penalizes wasteful tool usage
-          + diversity_bonus                 — source diversity (≥2 distinct domains)
-        """
-        # Extract final response from messages (last assistant message with content)
-        final_response = ""
-        tools_used: list[str] = []
-        for msg in reversed(result.messages):
-            if msg.get("role") == "assistant" and msg.get("content") and not final_response:
-                final_response = msg["content"]
-            # Collect tool names from tool call messages
-            if msg.get("role") == "assistant" and msg.get("tool_calls"):
-                for tc in msg["tool_calls"]:
-                    fn = tc.get("function", {}) if isinstance(tc, dict) else {}
-                    name = fn.get("name", "")
-                    if name:
-                        tools_used.append(name)
-        tool_call_count: int = result.turns_used or len(tools_used)
-
-        cfg = self.config
-
-        # ---- Signal 1: Answer correctness (LLM judge) ----------------
-        correctness = await self._llm_judge(
-            question=item["question"],
-            expected=item["answer"],
-            model_answer=final_response,
-        )
-
-        # ---- Signal 2: Web tool usage --------------------------------
-        web_tools = {"web_search", "web_extract", "search", "firecrawl"}
-        tool_used = 1.0 if any(t in web_tools for t in tools_used) else 0.0
-
-        # ---- Signal 3: Efficiency ------------------------------------
-        if tool_call_count <= cfg.efficient_max_calls:
-            efficiency = 1.0
-        elif tool_call_count <= cfg.heavy_penalty_calls:
-            efficiency = 1.0 - (tool_call_count - cfg.efficient_max_calls) * 0.08
-        else:
-            efficiency = max(0.0, 1.0 - (tool_call_count - cfg.efficient_max_calls) * 0.12)
-
-        # ---- Bonus: Source diversity ---------------------------------
-        domains = self._extract_domains(final_response)
-        diversity = cfg.diversity_bonus if len(domains) >= 2 else 0.0
-
-        # ---- Combine ------------------------------------------------
-        reward = (
-            cfg.correctness_weight * correctness
-            + cfg.tool_usage_weight * tool_used
-            + cfg.efficiency_weight * efficiency
-            + diversity
-        )
-        reward = min(1.0, max(0.0, reward))  # clamp to [0, 1]
-
-        # Track for wandb
-        self._reward_buffer.append(reward)
-        self._correctness_buffer.append(correctness)
-        self._tool_usage_buffer.append(tool_used)
-        self._efficiency_buffer.append(efficiency)
-        self._diversity_buffer.append(diversity)
-
-        logger.debug(
-            f"Reward breakdown — correctness={correctness:.2f}, "
-            f"tool_used={tool_used:.1f}, efficiency={efficiency:.2f}, "
-            f"diversity={diversity:.1f} → total={reward:.3f}"
-        )
-
-        return reward
-
-    # ------------------------------------------------------------------
-    # 5. evaluate — run on held-out eval split
-    # ------------------------------------------------------------------
-
-    async def evaluate(self, *args, **kwargs) -> None:
-        """Run evaluation on the held-out split using the full agent loop with tools.
-
-        Each eval item runs through the same agent loop as training —
-        the model can use web_search, web_extract, etc. to research answers.
-        This measures actual agentic research capability, not just knowledge.
-        """
-        import time
-        import uuid
-        from environments.agent_loop import HermesAgentLoop
-        from environments.tool_context import ToolContext
-
-        items = self._eval_items
-        if not items:
-            logger.warning("No eval items available.")
-            return
-
-        eval_size = min(self.config.eval_size, len(items))
-        eval_items = items[:eval_size]
-
-        logger.info(f"Running eval on {len(eval_items)} questions (with agent loop + tools)...")
-        start_time = time.time()
-        samples = []
-
-        # Resolve tools once for all eval items
-        tools, valid_names = self._resolve_tools_for_group()
-
-        for i, item in enumerate(eval_items):
-            task_id = str(uuid.uuid4())
-            logger.info(f"Eval [{i+1}/{len(eval_items)}]: {item['question'][:80]}...")
-
-            try:
-                # Build messages
-                messages: List[Dict[str, Any]] = []
-                if self.config.system_prompt:
-                    messages.append({"role": "system", "content": self.config.system_prompt})
-                messages.append({"role": "user", "content": self.format_prompt(item)})
-
-                # Run the full agent loop with tools
-                agent = HermesAgentLoop(
-                    server=self.server,
-                    tool_schemas=tools,
-                    valid_tool_names=valid_names,
-                    max_turns=self.config.max_agent_turns,
-                    task_id=task_id,
-                    temperature=0.0,  # Deterministic for eval
-                    max_tokens=self.config.max_token_length,
-                    extra_body=self.config.extra_body,
-                )
-                result = await agent.run(messages)
-
-                # Extract final response and tool usage from messages
-                final_response = ""
-                tool_call_count = 0
-                for msg in reversed(result.messages):
-                    if msg.get("role") == "assistant" and msg.get("content") and not final_response:
-                        final_response = msg["content"]
-                    if msg.get("role") == "assistant" and msg.get("tool_calls"):
-                        tool_call_count += len(msg["tool_calls"])
-
-                # Compute reward (includes LLM judge for correctness)
-                # Temporarily save buffer lengths so we can extract the
-                # correctness score without calling judge twice, and avoid
-                # polluting training metric buffers with eval data.
-                buf_len = len(self._correctness_buffer)
-                ctx = ToolContext(task_id)
-                try:
-                    reward = await self.compute_reward(item, result, ctx)
-                finally:
-                    ctx.cleanup()
-
-                # Extract correctness from the buffer (compute_reward appended it)
-                # then remove eval entries from training buffers
-                correctness = (
-                    self._correctness_buffer[buf_len]
-                    if len(self._correctness_buffer) > buf_len
-                    else 0.0
-                )
-                # Roll back buffers to avoid polluting training metrics
-                for buf in (
-                    self._reward_buffer, self._correctness_buffer,
-                    self._tool_usage_buffer, self._efficiency_buffer,
-                    self._diversity_buffer,
-                ):
-                    if len(buf) > buf_len:
-                        buf.pop()
-
-                samples.append({
-                    "prompt": item["question"],
-                    "response": final_response[:500],
-                    "expected": item["answer"],
-                    "correctness": correctness,
-                    "reward": reward,
-                    "tool_calls": tool_call_count,
-                    "turns": result.turns_used,
-                })
-
-                logger.info(
-                    f"  → correctness={correctness:.2f}, reward={reward:.3f}, "
-                    f"tools={tool_call_count}, turns={result.turns_used}"
-                )
-
-            except Exception as e:
-                logger.error(f"Eval error on item: {e}")
-                samples.append({
-                    "prompt": item["question"],
-                    "response": f"ERROR: {e}",
-                    "expected": item["answer"],
-                    "correctness": 0.0,
-                    "reward": 0.0,
-                    "tool_calls": 0,
-                    "turns": 0,
-                })
-
-        end_time = time.time()
-
-        # Compute aggregate metrics
-        correctness_scores = [s["correctness"] for s in samples]
-        rewards = [s["reward"] for s in samples]
-        tool_counts = [s["tool_calls"] for s in samples]
-        n = len(samples)
-
-        eval_metrics = {
-            "eval/mean_correctness": sum(correctness_scores) / n if n else 0.0,
-            "eval/mean_reward": sum(rewards) / n if n else 0.0,
-            "eval/mean_tool_calls": sum(tool_counts) / n if n else 0.0,
-            "eval/tool_usage_rate": sum(1 for t in tool_counts if t > 0) / n if n else 0.0,
-            "eval/n_items": n,
-        }
-
-        logger.info(
-            f"Eval complete — correctness={eval_metrics['eval/mean_correctness']:.3f}, "
-            f"reward={eval_metrics['eval/mean_reward']:.3f}, "
-            f"tool_usage={eval_metrics['eval/tool_usage_rate']:.0%}"
-        )
-
-        await self.evaluate_log(
-            metrics=eval_metrics,
-            samples=samples,
-            start_time=start_time,
-            end_time=end_time,
-        )
-
-    # ------------------------------------------------------------------
-    # 6. wandb_log — custom metrics
-    # ------------------------------------------------------------------
-
-    async def wandb_log(self, wandb_metrics: Optional[Dict] = None) -> None:
-        """Log reward breakdown metrics to wandb."""
-        if wandb_metrics is None:
-            wandb_metrics = {}
-
-        if self._reward_buffer:
-            n = len(self._reward_buffer)
-            wandb_metrics["train/mean_reward"] = sum(self._reward_buffer) / n
-            wandb_metrics["train/mean_correctness"] = sum(self._correctness_buffer) / n
-            wandb_metrics["train/mean_tool_usage"] = sum(self._tool_usage_buffer) / n
-            wandb_metrics["train/mean_efficiency"] = sum(self._efficiency_buffer) / n
-            wandb_metrics["train/mean_diversity"] = sum(self._diversity_buffer) / n
-            wandb_metrics["train/total_rollouts"] = n
-
-            # Accuracy buckets
-            wandb_metrics["train/correct_rate"] = (
-                sum(1 for c in self._correctness_buffer if c >= 0.7) / n
-            )
-            wandb_metrics["train/tool_usage_rate"] = (
-                sum(1 for t in self._tool_usage_buffer if t > 0) / n
-            )
-
-            # Clear buffers
-            self._reward_buffer.clear()
-            self._correctness_buffer.clear()
-            self._tool_usage_buffer.clear()
-            self._efficiency_buffer.clear()
-            self._diversity_buffer.clear()
-
-        await super().wandb_log(wandb_metrics)
-
-    # ------------------------------------------------------------------
-    # Private helpers
-    # ------------------------------------------------------------------
-
-    async def _llm_judge(
-        self,
-        question: str,
-        expected: str,
-        model_answer: str,
-    ) -> float:
-        """
-        Use the server's LLM to judge answer correctness.
-        Falls back to keyword heuristic if LLM call fails.
-        """
-        if not model_answer or not model_answer.strip():
-            return 0.0
-
-        judge_prompt = (
-            "You are an impartial judge evaluating the quality of an AI research answer.\n\n"
-            f"Question: {question}\n\n"
-            f"Reference answer: {expected}\n\n"
-            f"Model answer: {model_answer}\n\n"
-            "Score the model answer on a scale from 0.0 to 1.0 where:\n"
-            "  1.0 = fully correct and complete\n"
-            "  0.7 = mostly correct with minor gaps\n"
-            "  0.4 = partially correct\n"
-            "  0.1 = mentions relevant topic but wrong or very incomplete\n"
-            "  0.0 = completely wrong or no answer\n\n"
-            "Consider: factual accuracy, completeness, and relevance.\n"
-            'Respond with ONLY a JSON object: {"score": <float>, "reason": "<one sentence>"}'
-        )
-
-        try:
-            response = await self.server.chat_completion(
-                messages=[{"role": "user", "content": judge_prompt}],
-                n=1,
-                max_tokens=150,
-                temperature=0.0,
-                split="eval",
-            )
-            text = response.choices[0].message.content if response.choices else ""
-            parsed = self._parse_judge_json(text)
-            if parsed is not None:
-                return float(parsed)
-        except Exception as e:
-            logger.debug(f"LLM judge failed: {e}. Using heuristic.")
-
-        return self._heuristic_score(expected, model_answer)
-
-    @staticmethod
-    def _parse_judge_json(text: str) -> Optional[float]:
-        """Extract the score float from LLM judge JSON response."""
-        try:
-            clean = re.sub(r"```(?:json)?|```", "", text).strip()
-            data = json.loads(clean)
-            score = float(data.get("score", -1))
-            if 0.0 <= score <= 1.0:
-                return score
-        except Exception:
-            match = re.search(r'"score"\s*:\s*([0-9.]+)', text)
-            if match:
-                score = float(match.group(1))
-                if 0.0 <= score <= 1.0:
-                    return score
-        return None
-
-    @staticmethod
-    def _heuristic_score(expected: str, model_answer: str) -> float:
-        """Lightweight keyword overlap score as fallback."""
-        stopwords = {
-            "the", "a", "an", "is", "are", "was", "were", "of", "in", "on",
-            "at", "to", "for", "with", "and", "or", "but", "it", "its",
-            "this", "that", "as", "by", "from", "be", "has", "have", "had",
-        }
-
-        def tokenize(text: str) -> set:
-            tokens = re.findall(r'\b\w+\b', text.lower())
-            return {t for t in tokens if t not in stopwords and len(t) > 2}
-
-        expected_tokens = tokenize(expected)
-        answer_tokens = tokenize(model_answer)
-
-        if not expected_tokens:
-            return 0.5
-
-        overlap = len(expected_tokens & answer_tokens)
-        union = len(expected_tokens | answer_tokens)
-
-        jaccard = overlap / union if union > 0 else 0.0
-        recall = overlap / len(expected_tokens)
-        return min(1.0, 0.4 * jaccard + 0.6 * recall)
-
-    @staticmethod
-    def _extract_domains(text: str) -> set:
-        """Extract unique domains from URLs cited in the response."""
-        urls = re.findall(r'https?://[^\s\)>\]"\']+', text)
-        domains = set()
-        for url in urls:
-            try:
-                parsed = urlparse(url)
-                domain = parsed.netloc.lower().lstrip("www.")
-                if domain:
-                    domains.add(domain)
-            except Exception:
-                pass
-        return domains
-
-
-# ---------------------------------------------------------------------------
-# Entry point
-# ---------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    WebResearchEnv.cli()

gateway/agent_registry.py

@@ -1,504 +0,0 @@
-"""
-Agent registry for multi-agent support.
-
-Manages agent configurations, tool policies, and workspace resolution.
-Each agent has its own identity, model settings, tool access, and workspace.
-"""
-
-from __future__ import annotations
-
-import logging
-import re
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Set
-
-logger = logging.getLogger(__name__)
-
-HERMES_HOME = Path.home() / ".hermes"
-
-# ---------------------------------------------------------------------------
-# Tool profiles -- predefined sets of allowed tools
-# ---------------------------------------------------------------------------
-
-TOOL_PROFILES: Dict[str, Dict[str, Any]] = {
-    "minimal": {
-        "allow": [
-            "clarify",
-            "memory",
-            "todo",
-            "session_search",
-        ],
-    },
-    "coding": {
-        "allow": [
-            "terminal",
-            "process",
-            "read_file",
-            "write_file",
-            "patch",
-            "search_files",
-            "web_search",
-            "web_extract",
-            "memory",
-            "todo",
-            "clarify",
-            "session_search",
-            "delegate_task",
-            "execute_code",
-            "vision_analyze",
-        ],
-    },
-    "messaging": {
-        "allow": [
-            "web_search",
-            "web_extract",
-            "memory",
-            "todo",
-            "clarify",
-            "session_search",
-            "send_message",
-            "text_to_speech",
-            "image_generate",
-        ],
-    },
-    "full": {},  # No restrictions
-}
-
-# Valid agent ID pattern: starts with lowercase letter/digit, rest can include _ and -
-_AGENT_ID_RE = re.compile(r"^[a-z0-9][a-z0-9_-]{0,63}$")
-
-
-# ---------------------------------------------------------------------------
-# ToolPolicy
-# ---------------------------------------------------------------------------
-
-@dataclass
-class ToolPolicy:
-    """
-    Declarative tool access policy for an agent.
-
-    Resolution pipeline (applied in order):
-      1. Start with the profile's allow-list (or all tools if no profile / 'full').
-      2. Add any names from ``also_allow``.
-      3. If an explicit ``allow`` list is set, intersect with it.
-      4. Remove any names from ``deny`` (deny always wins).
-    """
-
-    profile: Optional[str] = None
-    allow: Optional[List[str]] = None
-    also_allow: Optional[List[str]] = None
-    deny: Optional[List[str]] = None
-
-    def apply(self, tools: Set[str]) -> Set[str]:
-        """
-        Filter a set of tool names according to this policy.
-
-        The pipeline is: profile -> also_allow -> allow -> deny.
-        Deny always wins — denied tools are removed regardless of other rules.
-
-        Parameters
-        ----------
-        tools:
-            The full set of available tool names.
-
-        Returns
-        -------
-        Set[str]
-            The subset of tools this agent is permitted to use.
-        """
-        # Step 1: Start from profile
-        if self.profile and self.profile in TOOL_PROFILES:
-            profile_def = TOOL_PROFILES[self.profile]
-            if "allow" in profile_def:
-                result = tools & set(profile_def["allow"])
-            else:
-                # Profile like 'full' with no allow list => all tools
-                result = set(tools)
-        else:
-            # No profile => start with all tools
-            result = set(tools)
-
-        # Step 2: Additive extras from also_allow
-        if self.also_allow:
-            result |= tools & set(self.also_allow)
-
-        # Step 3: Explicit allow list narrows the result
-        if self.allow is not None:
-            result &= set(self.allow)
-
-        # Step 4: Deny always wins
-        if self.deny:
-            result -= set(self.deny)
-
-        return result
-
-
-# ---------------------------------------------------------------------------
-# SubagentPolicy
-# ---------------------------------------------------------------------------
-
-@dataclass
-class SubagentPolicy:
-    """Controls how an agent may spawn sub-agents."""
-
-    max_depth: int = 2
-    max_children: int = 5
-    model: Optional[str] = None
-
-
-# ---------------------------------------------------------------------------
-# AgentConfig
-# ---------------------------------------------------------------------------
-
-@dataclass
-class AgentConfig:
-    """
-    Full configuration for a single agent persona.
-
-    Attributes
-    ----------
-    id:
-        Unique identifier (lowercase, alphanumeric + hyphens/underscores).
-    description:
-        Human-readable description of this agent's purpose.
-    default:
-        Whether this is the default agent (exactly one must be default).
-    model:
-        LLM model identifier. ``None`` inherits the global default.
-    provider:
-        LLM provider name (e.g. ``'anthropic'``, ``'openai'``).
-    personality:
-        Inline personality/system prompt text, or path to a file.
-    workspace:
-        Custom workspace directory override. ``None`` uses the default.
-    toolsets:
-        List of toolset names to load (overrides platform default).
-    tool_policy:
-        Declarative tool access restrictions.
-    reasoning:
-        Provider-specific reasoning/thinking configuration dict.
-    max_turns:
-        Maximum agentic loop iterations per request.
-    sandbox:
-        Sandbox/isolation configuration dict.
-    fallback_model:
-        Fallback model configuration dict (used on primary failure).
-    memory_enabled:
-        Whether long-term memory is active for this agent.
-    subagents:
-        Sub-agent spawning policy.
-    dm_scope:
-        Which agent handles DMs on messaging platforms (``'main'`` by default).
-    """
-
-    id: str
-    description: str = ""
-    default: bool = False
-    model: Optional[str] = None
-    provider: Optional[str] = None
-    personality: Optional[str] = None
-    workspace: Optional[str] = None
-    toolsets: Optional[List[str]] = None
-    tool_policy: Optional[ToolPolicy] = None
-    reasoning: Optional[Dict[str, Any]] = None
-    max_turns: Optional[int] = None
-    sandbox: Optional[Dict[str, Any]] = None
-    fallback_model: Optional[Dict[str, Any]] = None
-    memory_enabled: bool = True
-    subagents: SubagentPolicy = field(default_factory=SubagentPolicy)
-    dm_scope: str = "main"
-
-    # -- derived paths -------------------------------------------------------
-
-    @property
-    def workspace_dir(self) -> Path:
-        """Agent-specific workspace directory."""
-        if self.workspace:
-            return Path(self.workspace).expanduser()
-        return HERMES_HOME / "agents" / self.id
-
-    @property
-    def sessions_dir(self) -> Path:
-        """Directory for this agent's session data."""
-        return self.workspace_dir / "sessions"
-
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-def normalize_tool_config(raw: Any) -> Optional[ToolPolicy]:
-    """
-    Coerce various shorthand forms into a ``ToolPolicy``.
-
-    Accepted inputs::
-
-        None             -> None
-        "coding"         -> ToolPolicy(profile="coding")
-        ["read_file", …] -> ToolPolicy(allow=[…])
-        {profile: …, …}  -> ToolPolicy(**dict)
-
-    Parameters
-    ----------
-    raw:
-        Raw tool policy value from configuration.
-
-    Returns
-    -------
-    Optional[ToolPolicy]
-    """
-    if raw is None:
-        return None
-    if isinstance(raw, str):
-        return ToolPolicy(profile=raw)
-    if isinstance(raw, list):
-        return ToolPolicy(allow=raw)
-    if isinstance(raw, dict):
-        return ToolPolicy(
-            profile=raw.get("profile"),
-            allow=raw.get("allow"),
-            also_allow=raw.get("also_allow"),
-            deny=raw.get("deny"),
-        )
-    raise TypeError(f"Invalid tool_policy value: {raw!r}")
-
-
-def _validate_agent_id(agent_id: str) -> None:
-    """Raise ``ValueError`` if *agent_id* is not a valid identifier."""
-    if not _AGENT_ID_RE.match(agent_id):
-        raise ValueError(
-            f"Invalid agent id {agent_id!r}. Must match "
-            f"[a-z0-9][a-z0-9_-]{{0,63}}"
-        )
-
-
-# ---------------------------------------------------------------------------
-# AgentRegistry
-# ---------------------------------------------------------------------------
-
-class AgentRegistry:
-    """
-    Registry of configured agent personas.
-
-    Parses the ``agents`` section of the top-level config dict and exposes
-    lookup / resolution helpers used by the runtime.
-    """
-
-    def __init__(self, config: dict, global_config: dict = None) -> None:
-        self._agents: Dict[str, AgentConfig] = {}
-        self._default_id: str = "main"
-        self._parse_agents(config, global_config)
-
-    # -- parsing -------------------------------------------------------------
-
-    def _parse_agents(self, config: dict, global_config: dict = None) -> None:
-        """
-        Parse ``config['agents']`` into ``AgentConfig`` instances.
-
-        If the config has no ``agents`` key an implicit *main* agent is
-        created from ``global_config`` so the system always has at least
-        one agent.
-
-        Parameters
-        ----------
-        config:
-            Config dict that may contain an ``agents`` key with a flat dict
-            of agent definitions keyed by name.
-        global_config:
-            Top-level global config dict used to populate the implicit
-            *main* agent when no ``agents`` key is present.
-        """
-        agents_raw: Optional[Dict[str, Any]] = config.get("agents")
-
-        if not agents_raw:
-            # Implicit single-agent setup — derive from global_config
-            gc = global_config or {}
-            main = AgentConfig(
-                id="main",
-                default=True,
-                model=gc.get("model"),
-                provider=gc.get("provider"),
-                personality=gc.get("personality"),
-                tool_policy=normalize_tool_config(gc.get("tools")),
-                reasoning=gc.get("reasoning"),
-                max_turns=gc.get("max_turns"),
-                memory_enabled=gc.get("memory_enabled", True),
-            )
-            self._agents = {"main": main}
-            self._default_id = "main"
-            return
-
-        agents: Dict[str, AgentConfig] = {}
-        seen_ids: Set[str] = set()
-        default_id: Optional[str] = None
-        first_id: Optional[str] = None
-
-        for name, agent_data in agents_raw.items():
-            if agent_data is None:
-                agent_data = {}
-
-            agent_id = agent_data.get("id", name)
-            _validate_agent_id(agent_id)
-
-            if agent_id in seen_ids:
-                raise ValueError(f"Duplicate agent id: {agent_id!r}")
-            seen_ids.add(agent_id)
-
-            if first_id is None:
-                first_id = agent_id
-
-            # Normalize the tools / tool_policy field
-            tool_policy = normalize_tool_config(
-                agent_data.get("tools", agent_data.get("tool_policy"))
-            )
-
-            subagent_raw = agent_data.get("subagents")
-            if isinstance(subagent_raw, dict):
-                subagent_policy = SubagentPolicy(**subagent_raw)
-            else:
-                subagent_policy = SubagentPolicy()
-
-            is_default = agent_data.get("default", False)
-
-            agent_cfg = AgentConfig(
-                id=agent_id,
-                description=agent_data.get("description", ""),
-                default=is_default,
-                model=agent_data.get("model"),
-                provider=agent_data.get("provider"),
-                personality=agent_data.get("personality"),
-                workspace=agent_data.get("workspace"),
-                toolsets=agent_data.get("toolsets"),
-                tool_policy=tool_policy,
-                reasoning=agent_data.get("reasoning"),
-                max_turns=agent_data.get("max_turns"),
-                sandbox=agent_data.get("sandbox"),
-                fallback_model=agent_data.get("fallback_model"),
-                memory_enabled=agent_data.get("memory_enabled", True),
-                subagents=subagent_policy,
-                dm_scope=agent_data.get("dm_scope", "main"),
-            )
-
-            if is_default:
-                if default_id is not None:
-                    raise ValueError(
-                        f"Multiple default agents: {default_id!r} and {agent_id!r}"
-                    )
-                default_id = agent_id
-
-            agents[agent_id] = agent_cfg
-
-        # If nobody was explicitly marked default, the first agent wins
-        if default_id is None and first_id is not None:
-            default_id = first_id
-            agents[first_id].default = True
-            logger.debug(
-                "No explicit default agent; using first: %s", first_id
-            )
-
-        self._agents = agents
-        self._default_id = default_id or "main"
-
-    # -- public API ----------------------------------------------------------
-
-    def get(self, agent_id: str) -> AgentConfig:
-        """
-        Return the config for *agent_id*, falling back to the default agent.
-        """
-        return self._agents.get(agent_id, self.get_default())
-
-    def get_default(self) -> AgentConfig:
-        """Return the default agent configuration."""
-        return self._agents[self._default_id]
-
-    def list_agents(self) -> List[AgentConfig]:
-        """Return all registered agent configurations."""
-        return list(self._agents.values())
-
-    # -- resolution helpers --------------------------------------------------
-
-    def resolve_personality(self, agent: AgentConfig) -> Optional[str]:
-        """
-        Resolve the personality/system-prompt text for *agent*.
-
-        Resolution order:
-          1. ``agent.personality`` field (inline text or file path).
-          2. ``SOUL.md`` in the agent's workspace directory.
-          3. Global ``~/.hermes/SOUL.md`` (only for the *main* agent).
-          4. ``None``.
-        """
-        # 1. Explicit personality in config
-        if agent.personality:
-            personality_path = Path(agent.personality).expanduser()
-            if personality_path.is_file():
-                try:
-                    return personality_path.read_text(encoding="utf-8").strip()
-                except OSError:
-                    logger.warning(
-                        "Could not read personality file: %s", personality_path
-                    )
-            # Treat as inline text
-            return agent.personality
-
-        # 2. Workspace SOUL.md
-        workspace_soul = agent.workspace_dir / "SOUL.md"
-        if workspace_soul.is_file():
-            try:
-                return workspace_soul.read_text(encoding="utf-8").strip()
-            except OSError:
-                logger.warning(
-                    "Could not read workspace SOUL.md: %s", workspace_soul
-                )
-
-        # 3. Global SOUL.md (main agent only)
-        if agent.id == "main":
-            global_soul = HERMES_HOME / "SOUL.md"
-            if global_soul.is_file():
-                try:
-                    return global_soul.read_text(encoding="utf-8").strip()
-                except OSError:
-                    logger.warning(
-                        "Could not read global SOUL.md: %s", global_soul
-                    )
-
-        # 4. Nothing
-        return None
-
-    def resolve_toolsets(
-        self, agent: AgentConfig, platform: str
-    ) -> Optional[List[str]]:
-        """
-        Determine which toolsets to load for *agent* on *platform*.
-
-        Returns the agent's explicit ``toolsets`` list if set, otherwise
-        ``None`` to let the caller fall back to the platform's default
-        toolset configuration.
-
-        Parameters
-        ----------
-        agent:
-            The agent whose toolsets to resolve.
-        platform:
-            The platform name (e.g. ``'telegram'``, ``'local'``).
-
-        Returns
-        -------
-        Optional[List[str]]
-            Ordered list of toolset names, or ``None`` for platform default.
-        """
-        if agent.toolsets is not None:
-            return list(agent.toolsets)
-        return None
-
-    @staticmethod
-    def ensure_workspace(agent: AgentConfig) -> None:
-        """
-        Create the agent's workspace and session directories if they
-        do not already exist.
-        """
-        agent.workspace_dir.mkdir(parents=True, exist_ok=True)
-        agent.sessions_dir.mkdir(parents=True, exist_ok=True)
-        logger.debug(
-            "Ensured workspace for agent %s: %s", agent.id, agent.workspace_dir
-        )

gateway/config.py

@@ -270,7 +270,7 @@ def load_gateway_config() -> GatewayConfig:
     gateway_config_path = Path.home() / ".hermes" / "gateway.json"
     if gateway_config_path.exists():
         try:
-            with open(gateway_config_path, "r", encoding="utf-8") as f:
+            with open(gateway_config_path, "r") as f:
                 data = json.load(f)
                 config = GatewayConfig.from_dict(data)
         except Exception as e:
@@ -283,7 +283,7 @@ def load_gateway_config() -> GatewayConfig:
         import yaml
         config_yaml_path = Path.home() / ".hermes" / "config.yaml"
         if config_yaml_path.exists():
-            with open(config_yaml_path, encoding="utf-8") as f:
+            with open(config_yaml_path) as f:
                 yaml_cfg = yaml.safe_load(f) or {}
             sr = yaml_cfg.get("session_reset")
             if sr and isinstance(sr, dict):
@@ -441,5 +441,5 @@ def save_gateway_config(config: GatewayConfig) -> None:
     gateway_config_path = Path.home() / ".hermes" / "gateway.json"
     gateway_config_path.parent.mkdir(parents=True, exist_ok=True)
     
-    with open(gateway_config_path, "w", encoding="utf-8") as f:
+    with open(gateway_config_path, "w") as f:
         json.dump(config.to_dict(), f, indent=2)

gateway/mirror.py

@@ -111,7 +111,6 @@ def _append_to_jsonl(session_id: str, message: dict) -> None:
 
 def _append_to_sqlite(session_id: str, message: dict) -> None:
     """Append a message to the SQLite session database."""
-    db = None
     try:
         from hermes_state import SessionDB
         db = SessionDB()
@@ -122,6 +121,3 @@ def _append_to_sqlite(session_id: str, message: dict) -> None:
         )
     except Exception as e:
         logger.debug("Mirror SQLite write failed: %s", e)
-    finally:
-        if db is not None:
-            db.close()

gateway/platforms/base.py

@@ -252,7 +252,6 @@ def cleanup_document_cache(max_age_hours: int = 24) -> int:
 class MessageType(Enum):
     """Types of incoming messages."""
     TEXT = "text"
-    LOCATION = "location"
     PHOTO = "photo"
     VIDEO = "video"
     AUDIO = "audio"
@@ -413,12 +412,11 @@ class BasePlatformAdapter(ABC):
         """
         return SendResult(success=False, error="Not supported")
 
-    async def send_typing(self, chat_id: str, metadata=None) -> None:
+    async def send_typing(self, chat_id: str) -> None:
         """
         Send a typing indicator.
         
         Override in subclasses if the platform supports it.
-        metadata: optional dict with platform-specific context (e.g. thread_id for Slack).
         """
         pass
     
@@ -621,7 +619,7 @@ class BasePlatformAdapter(ABC):
         
         return media, cleaned
     
-    async def _keep_typing(self, chat_id: str, interval: float = 2.0, metadata=None) -> None:
+    async def _keep_typing(self, chat_id: str, interval: float = 2.0) -> None:
         """
         Continuously send typing indicator until cancelled.
         
@@ -630,7 +628,7 @@ class BasePlatformAdapter(ABC):
         """
         try:
             while True:
-                await self.send_typing(chat_id, metadata=metadata)
+                await self.send_typing(chat_id)
                 await asyncio.sleep(interval)
         except asyncio.CancelledError:
             pass  # Normal cancellation when handler completes
@@ -688,8 +686,7 @@ class BasePlatformAdapter(ABC):
         self._active_sessions[session_key] = interrupt_event
         
         # Start continuous typing indicator (refreshes every 2 seconds)
-        _thread_metadata = {"thread_id": event.source.thread_id} if event.source.thread_id else None
-        typing_task = asyncio.create_task(self._keep_typing(event.source.chat_id, metadata=_thread_metadata))
+        typing_task = asyncio.create_task(self._keep_typing(event.source.chat_id))
         
         try:
             # Call the handler (this can take a while with tool calls)
@@ -713,8 +710,7 @@ class BasePlatformAdapter(ABC):
                     result = await self.send(
                         chat_id=event.source.chat_id,
                         content=text_content,
-                        reply_to=event.message_id,
-                        metadata=_thread_metadata,
+                        reply_to=event.message_id
                     )
                     
                     # Log send failures (don't raise - user already saw tool progress)
@@ -724,8 +720,7 @@ class BasePlatformAdapter(ABC):
                         fallback_result = await self.send(
                             chat_id=event.source.chat_id,
                             content=f"(Response formatting failed, plain text:)\n\n{text_content[:3500]}",
-                            reply_to=event.message_id,
-                            metadata=_thread_metadata,
+                            reply_to=event.message_id
                         )
                         if not fallback_result.success:
                             print(f"[{self.name}] Fallback send also failed: {fallback_result.error}")
@@ -747,14 +742,12 @@ class BasePlatformAdapter(ABC):
                                 chat_id=event.source.chat_id,
                                 animation_url=image_url,
                                 caption=alt_text if alt_text else None,
-                                metadata=_thread_metadata,
                             )
                         else:
                             img_result = await self.send_image(
                                 chat_id=event.source.chat_id,
                                 image_url=image_url,
                                 caption=alt_text if alt_text else None,
-                                metadata=_thread_metadata,
                             )
                         if not img_result.success:
                             logger.error("[%s] Failed to send image: %s", self.name, img_result.error)
@@ -775,25 +768,21 @@ class BasePlatformAdapter(ABC):
                             media_result = await self.send_voice(
                                 chat_id=event.source.chat_id,
                                 audio_path=media_path,
-                                metadata=_thread_metadata,
                             )
                         elif ext in _VIDEO_EXTS:
                             media_result = await self.send_video(
                                 chat_id=event.source.chat_id,
                                 video_path=media_path,
-                                metadata=_thread_metadata,
                             )
                         elif ext in _IMAGE_EXTS:
                             media_result = await self.send_image_file(
                                 chat_id=event.source.chat_id,
                                 image_path=media_path,
-                                metadata=_thread_metadata,
                             )
                         else:
                             media_result = await self.send_document(
                                 chat_id=event.source.chat_id,
                                 file_path=media_path,
-                                metadata=_thread_metadata,
                             )
 
                         if not media_result.success:

gateway/platforms/discord.py

@@ -359,7 +359,7 @@ class DiscordAdapter(BasePlatformAdapter):
             print(f"[{self.name}] Failed to send image attachment, falling back to URL: {e}")
             return await super().send_image(chat_id, image_url, caption, reply_to)
     
-    async def send_typing(self, chat_id: str, metadata=None) -> None:
+    async def send_typing(self, chat_id: str) -> None:
         """Send typing indicator."""
         if self._client:
             try:

gateway/platforms/homeassistant.py

@@ -419,7 +419,7 @@ class HomeAssistantAdapter(BasePlatformAdapter):
         except Exception as e:
             return SendResult(success=False, error=str(e))
 
-    async def send_typing(self, chat_id: str, metadata=None) -> None:
+    async def send_typing(self, chat_id: str) -> None:
         """No typing indicator for Home Assistant."""
         pass
 

gateway/platforms/signal.py

@@ -104,20 +104,6 @@ def _is_audio_ext(ext: str) -> bool:
     return ext.lower() in (".mp3", ".wav", ".ogg", ".m4a", ".aac")
 
 
-_EXT_TO_MIME = {
-    ".jpg": "image/jpeg", ".jpeg": "image/jpeg", ".png": "image/png",
-    ".gif": "image/gif", ".webp": "image/webp",
-    ".ogg": "audio/ogg", ".mp3": "audio/mpeg", ".wav": "audio/wav",
-    ".m4a": "audio/mp4", ".aac": "audio/aac",
-    ".mp4": "video/mp4", ".pdf": "application/pdf", ".zip": "application/zip",
-}
-
-
-def _ext_to_mime(ext: str) -> str:
-    """Map file extension to MIME type."""
-    return _EXT_TO_MIME.get(ext.lower(), "application/octet-stream")
-
-
 def _render_mentions(text: str, mentions: list) -> str:
     """Replace Signal mention placeholders (\\uFFFC) with readable @identifiers.
 
@@ -418,8 +404,9 @@ class SignalAdapter(BasePlatformAdapter):
 
         # Process attachments
         attachments_data = data_message.get("attachments", [])
-        media_urls = []
-        media_types = []
+        image_paths = []
+        audio_path = None
+        document_paths = []
 
         if attachments_data and not getattr(self, "ignore_attachments", False):
             for att in attachments_data:
@@ -433,10 +420,12 @@ class SignalAdapter(BasePlatformAdapter):
                 try:
                     cached_path, ext = await self._fetch_attachment(att_id)
                     if cached_path:
-                        # Use contentType from Signal if available, else map from extension
-                        content_type = att.get("contentType") or _ext_to_mime(ext)
-                        media_urls.append(cached_path)
-                        media_types.append(content_type)
+                        if _is_image_ext(ext):
+                            image_paths.append(cached_path)
+                        elif _is_audio_ext(ext):
+                            audio_path = cached_path
+                        else:
+                            document_paths.append(cached_path)
                 except Exception:
                     logger.exception("Signal: failed to fetch attachment %s", att_id)
 
@@ -451,13 +440,12 @@ class SignalAdapter(BasePlatformAdapter):
             chat_id_alt=group_id if is_group else None,
         )
 
-        # Determine message type from media
+        # Determine message type
         msg_type = MessageType.TEXT
-        if media_types:
-            if any(mt.startswith("audio/") for mt in media_types):
-                msg_type = MessageType.VOICE
-            elif any(mt.startswith("image/") for mt in media_types):
-                msg_type = MessageType.IMAGE
+        if audio_path:
+            msg_type = MessageType.VOICE
+        elif image_paths:
+            msg_type = MessageType.IMAGE
 
         # Parse timestamp from envelope data (milliseconds since epoch)
         ts_ms = envelope_data.get("timestamp", 0)
@@ -474,8 +462,9 @@ class SignalAdapter(BasePlatformAdapter):
             source=source,
             text=text or "",
             message_type=msg_type,
-            media_urls=media_urls,
-            media_types=media_types,
+            image_paths=image_paths,
+            audio_path=audio_path,
+            document_paths=document_paths,
             timestamp=timestamp,
         )
 
@@ -557,16 +546,16 @@ class SignalAdapter(BasePlatformAdapter):
     async def send(
         self,
         chat_id: str,
-        content: str,
-        reply_to: Optional[str] = None,
-        metadata: Optional[Dict[str, Any]] = None,
+        text: str,
+        reply_to_message_id: Optional[str] = None,
+        **kwargs,
     ) -> SendResult:
         """Send a text message."""
         await self._stop_typing_indicator(chat_id)
 
         params: Dict[str, Any] = {
             "account": self.account,
-            "message": content,
+            "message": text,
         }
 
         if chat_id.startswith("group:"):
@@ -580,7 +569,7 @@ class SignalAdapter(BasePlatformAdapter):
             return SendResult(success=True)
         return SendResult(success=False, error="RPC send failed")
 
-    async def send_typing(self, chat_id: str, metadata=None) -> None:
+    async def send_typing(self, chat_id: str) -> None:
         """Send a typing indicator."""
         params: Dict[str, Any] = {
             "account": self.account,

gateway/platforms/slack.py

@@ -10,7 +10,6 @@ Uses slack-bolt (Python) with Socket Mode for:
 
 import asyncio
 import os
-import re
 from typing import Dict, List, Optional, Any
 
 try:
@@ -34,8 +33,6 @@ from gateway.platforms.base import (
     MessageEvent,
     MessageType,
     SendResult,
-    SUPPORTED_DOCUMENT_TYPES,
-    cache_document_from_bytes,
     cache_image_from_url,
     cache_audio_from_url,
 )
@@ -99,13 +96,6 @@ class SlackAdapter(BasePlatformAdapter):
             async def handle_message_event(event, say):
                 await self._handle_slack_message(event)
 
-            # Acknowledge app_mention events to prevent Bolt 404 errors.
-            # The "message" handler above already processes @mentions in
-            # channels, so this is intentionally a no-op to avoid duplicates.
-            @self._app.event("app_mention")
-            async def handle_app_mention(event, say):
-                pass
-
             # Register slash command handler
             @self._app.command("/hermes")
             async def handle_hermes_command(ack, command):
@@ -185,7 +175,7 @@ class SlackAdapter(BasePlatformAdapter):
         except Exception as e:
             return SendResult(success=False, error=str(e))
 
-    async def send_typing(self, chat_id: str, metadata=None) -> None:
+    async def send_typing(self, chat_id: str) -> None:
         """Slack doesn't have a direct typing indicator API for bots."""
         pass
 
@@ -276,65 +266,6 @@ class SlackAdapter(BasePlatformAdapter):
         except Exception as e:
             return SendResult(success=False, error=str(e))
 
-    async def send_video(
-        self,
-        chat_id: str,
-        video_path: str,
-        caption: Optional[str] = None,
-        reply_to: Optional[str] = None,
-    ) -> SendResult:
-        """Send a video file to Slack."""
-        if not self._app:
-            return SendResult(success=False, error="Not connected")
-
-        if not os.path.exists(video_path):
-            return SendResult(success=False, error=f"Video file not found: {video_path}")
-
-        try:
-            result = await self._app.client.files_upload_v2(
-                channel=chat_id,
-                file=video_path,
-                filename=os.path.basename(video_path),
-                initial_comment=caption or "",
-                thread_ts=reply_to,
-            )
-            return SendResult(success=True, raw_response=result)
-
-        except Exception as e:
-            print(f"[{self.name}] Failed to send video: {e}")
-            return await super().send_video(chat_id, video_path, caption, reply_to)
-
-    async def send_document(
-        self,
-        chat_id: str,
-        file_path: str,
-        caption: Optional[str] = None,
-        file_name: Optional[str] = None,
-        reply_to: Optional[str] = None,
-    ) -> SendResult:
-        """Send a document/file attachment to Slack."""
-        if not self._app:
-            return SendResult(success=False, error="Not connected")
-
-        if not os.path.exists(file_path):
-            return SendResult(success=False, error=f"File not found: {file_path}")
-
-        display_name = file_name or os.path.basename(file_path)
-
-        try:
-            result = await self._app.client.files_upload_v2(
-                channel=chat_id,
-                file=file_path,
-                filename=display_name,
-                initial_comment=caption or "",
-                thread_ts=reply_to,
-            )
-            return SendResult(success=True, raw_response=result)
-
-        except Exception as e:
-            print(f"[{self.name}] Failed to send document: {e}")
-            return await super().send_document(chat_id, file_path, caption, file_name, reply_to)
-
     async def get_chat_info(self, chat_id: str) -> Dict[str, Any]:
         """Get information about a Slack channel."""
         if not self._app:
@@ -416,58 +347,6 @@ class SlackAdapter(BasePlatformAdapter):
                     msg_type = MessageType.VOICE
                 except Exception as e:
                     print(f"[Slack] Failed to cache audio: {e}", flush=True)
-            elif url:
-                # Try to handle as a document attachment
-                try:
-                    original_filename = f.get("name", "")
-                    ext = ""
-                    if original_filename:
-                        _, ext = os.path.splitext(original_filename)
-                        ext = ext.lower()
-
-                    # Fallback: reverse-lookup from MIME type
-                    if not ext and mimetype:
-                        mime_to_ext = {v: k for k, v in SUPPORTED_DOCUMENT_TYPES.items()}
-                        ext = mime_to_ext.get(mimetype, "")
-
-                    if ext not in SUPPORTED_DOCUMENT_TYPES:
-                        continue  # Skip unsupported file types silently
-
-                    # Check file size (Slack limit: 20 MB for bots)
-                    file_size = f.get("size", 0)
-                    MAX_DOC_BYTES = 20 * 1024 * 1024
-                    if not file_size or file_size > MAX_DOC_BYTES:
-                        print(f"[Slack] Document too large or unknown size: {file_size}", flush=True)
-                        continue
-
-                    # Download and cache
-                    raw_bytes = await self._download_slack_file_bytes(url)
-                    cached_path = cache_document_from_bytes(
-                        raw_bytes, original_filename or f"document{ext}"
-                    )
-                    doc_mime = SUPPORTED_DOCUMENT_TYPES[ext]
-                    media_urls.append(cached_path)
-                    media_types.append(doc_mime)
-                    msg_type = MessageType.DOCUMENT
-                    print(f"[Slack] Cached user document: {cached_path}", flush=True)
-
-                    # Inject text content for .txt/.md files (capped at 100 KB)
-                    MAX_TEXT_INJECT_BYTES = 100 * 1024
-                    if ext in (".md", ".txt") and len(raw_bytes) <= MAX_TEXT_INJECT_BYTES:
-                        try:
-                            text_content = raw_bytes.decode("utf-8")
-                            display_name = original_filename or f"document{ext}"
-                            display_name = re.sub(r'[^\w.\- ]', '_', display_name)
-                            injection = f"[Content of {display_name}]:\n{text_content}"
-                            if text:
-                                text = f"{injection}\n\n{text}"
-                            else:
-                                text = injection
-                        except UnicodeDecodeError:
-                            pass  # Binary content, skip injection
-
-                except Exception as e:
-                    print(f"[Slack] Failed to cache document: {e}", flush=True)
 
         # Build source
         source = self.build_source(
@@ -548,16 +427,3 @@ class SlackAdapter(BasePlatformAdapter):
         else:
             from gateway.platforms.base import cache_image_from_bytes
             return cache_image_from_bytes(response.content, ext)
-
-    async def _download_slack_file_bytes(self, url: str) -> bytes:
-        """Download a Slack file and return raw bytes."""
-        import httpx
-
-        bot_token = self.config.token
-        async with httpx.AsyncClient(timeout=30.0, follow_redirects=True) as client:
-            response = await client.get(
-                url,
-                headers={"Authorization": f"Bearer {bot_token}"},
-            )
-            response.raise_for_status()
-        return response.content

gateway/platforms/telegram.py

@@ -86,9 +86,6 @@ def _strip_mdv2(text: str) -> str:
     cleaned = re.sub(r'\\([_*\[\]()~`>#\+\-=|{}.!\\])', r'\1', text)
     # Remove MarkdownV2 bold markers that format_message converted from **bold**
     cleaned = re.sub(r'\*([^*]+)\*', r'\1', cleaned)
-    # Remove MarkdownV2 italic markers that format_message converted from *italic*
-    # Use word boundary (\b) to avoid breaking snake_case like my_variable_name
-    cleaned = re.sub(r'(?<!\w)_([^_]+)_(?!\w)', r'\1', cleaned)
     return cleaned
 
 
@@ -135,10 +132,6 @@ class TelegramAdapter(BasePlatformAdapter):
                 filters.COMMAND,
                 self._handle_command
             ))
-            self._app.add_handler(TelegramMessageHandler(
-                filters.LOCATION | getattr(filters, "VENUE", filters.LOCATION),
-                self._handle_location_message
-            ))
             self._app.add_handler(TelegramMessageHandler(
                 filters.PHOTO | filters.VIDEO | filters.AUDIO | filters.VOICE | filters.Document.ALL | filters.Sticker.ALL,
                 self._handle_media_message
@@ -289,7 +282,6 @@ class TelegramAdapter(BasePlatformAdapter):
         audio_path: str,
         caption: Optional[str] = None,
         reply_to: Optional[str] = None,
-        metadata: Optional[Dict[str, Any]] = None,
     ) -> SendResult:
         """Send audio as a native Telegram voice message or audio file."""
         if not self._bot:
@@ -303,23 +295,19 @@ class TelegramAdapter(BasePlatformAdapter):
             with open(audio_path, "rb") as audio_file:
                 # .ogg files -> send as voice (round playable bubble)
                 if audio_path.endswith(".ogg") or audio_path.endswith(".opus"):
-                    _voice_thread = metadata.get("thread_id") if metadata else None
                     msg = await self._bot.send_voice(
                         chat_id=int(chat_id),
                         voice=audio_file,
                         caption=caption[:1024] if caption else None,
                         reply_to_message_id=int(reply_to) if reply_to else None,
-                        message_thread_id=int(_voice_thread) if _voice_thread else None,
                     )
                 else:
                     # .mp3 and others -> send as audio file
-                    _audio_thread = metadata.get("thread_id") if metadata else None
                     msg = await self._bot.send_audio(
                         chat_id=int(chat_id),
                         audio=audio_file,
                         caption=caption[:1024] if caption else None,
                         reply_to_message_id=int(reply_to) if reply_to else None,
-                        message_thread_id=int(_audio_thread) if _audio_thread else None,
                     )
             return SendResult(success=True, message_id=str(msg.message_id))
         except Exception as e:
@@ -360,7 +348,6 @@ class TelegramAdapter(BasePlatformAdapter):
         image_url: str,
         caption: Optional[str] = None,
         reply_to: Optional[str] = None,
-        metadata: Optional[Dict[str, Any]] = None,
     ) -> SendResult:
         """Send an image natively as a Telegram photo.
         
@@ -372,13 +359,11 @@ class TelegramAdapter(BasePlatformAdapter):
         
         try:
             # Telegram can send photos directly from URLs (up to ~5MB)
-            _photo_thread = metadata.get("thread_id") if metadata else None
             msg = await self._bot.send_photo(
                 chat_id=int(chat_id),
                 photo=image_url,
                 caption=caption[:1024] if caption else None,  # Telegram caption limit
                 reply_to_message_id=int(reply_to) if reply_to else None,
-                message_thread_id=int(_photo_thread) if _photo_thread else None,
             )
             return SendResult(success=True, message_id=str(msg.message_id))
         except Exception as e:
@@ -409,20 +394,17 @@ class TelegramAdapter(BasePlatformAdapter):
         animation_url: str,
         caption: Optional[str] = None,
         reply_to: Optional[str] = None,
-        metadata: Optional[Dict[str, Any]] = None,
     ) -> SendResult:
         """Send an animated GIF natively as a Telegram animation (auto-plays inline)."""
         if not self._bot:
             return SendResult(success=False, error="Not connected")
         
         try:
-            _anim_thread = metadata.get("thread_id") if metadata else None
             msg = await self._bot.send_animation(
                 chat_id=int(chat_id),
                 animation=animation_url,
                 caption=caption[:1024] if caption else None,
                 reply_to_message_id=int(reply_to) if reply_to else None,
-                message_thread_id=int(_anim_thread) if _anim_thread else None,
             )
             return SendResult(success=True, message_id=str(msg.message_id))
         except Exception as e:
@@ -430,15 +412,13 @@ class TelegramAdapter(BasePlatformAdapter):
             # Fallback: try as a regular photo
             return await self.send_image(chat_id, animation_url, caption, reply_to)
 
-    async def send_typing(self, chat_id: str, metadata: Optional[Dict[str, Any]] = None) -> None:
+    async def send_typing(self, chat_id: str) -> None:
         """Send typing indicator."""
         if self._bot:
             try:
-                _typing_thread = metadata.get("thread_id") if metadata else None
                 await self._bot.send_chat_action(
                     chat_id=int(chat_id),
-                    action="typing",
-                    message_thread_id=int(_typing_thread) if _typing_thread else None,
+                    action="typing"
                 )
             except Exception:
                 pass  # Ignore typing indicator failures
@@ -566,41 +546,6 @@ class TelegramAdapter(BasePlatformAdapter):
         event = self._build_message_event(update.message, MessageType.COMMAND)
         await self.handle_message(event)
     
-    async def _handle_location_message(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
-        """Handle incoming location/venue pin messages."""
-        if not update.message:
-            return
-
-        msg = update.message
-        venue = getattr(msg, "venue", None)
-        location = getattr(venue, "location", None) if venue else getattr(msg, "location", None)
-
-        if not location:
-            return
-
-        lat = getattr(location, "latitude", None)
-        lon = getattr(location, "longitude", None)
-        if lat is None or lon is None:
-            return
-
-        # Build a text message with coordinates and context
-        parts = ["[The user shared a location pin.]"]
-        if venue:
-            title = getattr(venue, "title", None)
-            address = getattr(venue, "address", None)
-            if title:
-                parts.append(f"Venue: {title}")
-            if address:
-                parts.append(f"Address: {address}")
-        parts.append(f"latitude: {lat}")
-        parts.append(f"longitude: {lon}")
-        parts.append(f"Map: https://www.google.com/maps/search/?api=1&query={lat},{lon}")
-        parts.append("Ask what they'd like to find nearby (restaurants, cafes, etc.) and any preferences.")
-
-        event = self._build_message_event(msg, MessageType.LOCATION)
-        event.text = "\n".join(parts)
-        await self.handle_message(event)
-
     async def _handle_media_message(self, update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
         """Handle incoming media messages, downloading images to local cache."""
         if not update.message:

gateway/platforms/whatsapp.py

@@ -493,7 +493,7 @@ class WhatsAppAdapter(BasePlatformAdapter):
             file_name or os.path.basename(file_path),
         )
 
-    async def send_typing(self, chat_id: str, metadata=None) -> None:
+    async def send_typing(self, chat_id: str) -> None:
         """Send typing indicator via bridge."""
         if not self._running:
             return

gateway/router.py

@@ -1,195 +0,0 @@
-"""Binding router for multi-agent message routing.
-
-Maps incoming messages to agent IDs based on platform, chat, guild, and
-other session-source fields.  Bindings are ranked by specificity so that
-the most precise rule always wins.
-"""
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from typing import Any, Dict, List, Optional
-
-
-# ── constants ────────────────────────────────────────────────────────────
-
-PLATFORM_NAMES: set[str] = {
-    "telegram",
-    "discord",
-    "slack",
-    "whatsapp",
-    "signal",
-    "homeassistant",
-}
-
-_KEY_EXPANSION: Dict[str, str] = {
-    "guild": "guild_id",
-    "type": "chat_type",
-    "team": "team_id",
-    "peer": "peer",
-}
-
-
-# ── data ─────────────────────────────────────────────────────────────────
-
-@dataclass(frozen=True, slots=True)
-class Binding:
-    """A single routing rule that maps a match pattern to an agent."""
-
-    agent_id: str
-    match: Dict[str, str] = field(default_factory=dict)
-    tier: int = 7  # computed priority (1 = most specific)
-
-
-# ── helpers ──────────────────────────────────────────────────────────────
-
-def _assign_tier(match: Dict[str, str]) -> int:
-    """Return a priority tier (1–7) based on how specific *match* is.
-
-    Lower tier number means higher priority (more specific).
-
-    Tier 1: platform + chat_id   (exact channel)
-    Tier 2: platform + peer      (exact DM user)
-    Tier 3: platform + guild_id + chat_type
-    Tier 4: platform + (guild_id | team_id)
-    Tier 5: platform + chat_type
-    Tier 6: platform only
-    Tier 7: fallback (empty match)
-    """
-    keys = set(match.keys()) - {"platform"}
-
-    if not match:
-        return 7
-    if "chat_id" in keys:
-        return 1
-    if "peer" in keys:
-        return 2
-    if "guild_id" in keys and "chat_type" in keys:
-        return 3
-    if "guild_id" in keys or "team_id" in keys:
-        return 4
-    if "chat_type" in keys:
-        return 5
-    if "platform" in match:
-        return 6
-    return 7
-
-
-def normalize_binding(raw: dict) -> Binding:
-    """Normalise a shorthand binding dict into a :class:`Binding`.
-
-    Accepted shorthand formats::
-
-        {"agent": "coder", "telegram": "-100123"}
-            → Binding(agent_id="coder",
-                      match={"platform": "telegram", "chat_id": "-100123"})
-
-        {"agent": "assistant", "whatsapp": "*"}
-            → Binding(agent_id="assistant",
-                      match={"platform": "whatsapp"})
-
-        {"agent": "coder", "discord": {"guild": "123", "type": "channel"}}
-            → Binding(agent_id="coder",
-                      match={"platform": "discord",
-                             "guild_id": "123", "chat_type": "channel"})
-    """
-    agent_id: str = raw.get("agent", raw.get("agent_id", ""))
-    if not agent_id:
-        raise ValueError(f"Binding missing 'agent' key: {raw!r}")
-
-    match: Dict[str, str] = {}
-
-    for platform in PLATFORM_NAMES:
-        if platform not in raw:
-            continue
-
-        value: Any = raw[platform]
-        match["platform"] = platform
-
-        if isinstance(value, str):
-            if value != "*":
-                match["chat_id"] = value
-        elif isinstance(value, dict):
-            for short_key, expanded_key in _KEY_EXPANSION.items():
-                if short_key in value:
-                    match[expanded_key] = str(value[short_key])
-            # Pass through any keys that are already in expanded form
-            for k, v in value.items():
-                if k not in _KEY_EXPANSION:
-                    match[k] = str(v)
-        else:
-            raise TypeError(
-                f"Unsupported value type for platform '{platform}': "
-                f"{type(value).__name__}"
-            )
-        break  # only one platform key per binding
-
-    tier = _assign_tier(match)
-    return Binding(agent_id=agent_id, match=match, tier=tier)
-
-
-# ── router ───────────────────────────────────────────────────────────────
-
-class BindingRouter:
-    """Route incoming messages to agent IDs based on binding rules.
-
-    Parameters
-    ----------
-    bindings_config:
-        A list of raw binding dicts (shorthand format).
-    default_agent_id:
-        Fallback agent ID when no binding matches.
-    """
-
-    def __init__(self, bindings_config: list, default_agent_id: str) -> None:
-        self._default_agent_id: str = default_agent_id
-        self._bindings: List[Binding] = sorted(
-            (normalize_binding(raw) for raw in bindings_config),
-            key=lambda b: b.tier,
-        )
-
-    # ── public API ───────────────────────────────────────────────────
-
-    def resolve(
-        self,
-        platform: str,
-        chat_id: Optional[str] = None,
-        chat_type: Optional[str] = None,
-        user_id: Optional[str] = None,
-        guild_id: Optional[str] = None,
-        team_id: Optional[str] = None,
-    ) -> str:
-        """Return the agent ID for the most specific matching binding.
-
-        Iterates bindings in tier order (most specific first).  The first
-        match wins.  Falls back to *default_agent_id* if nothing matches.
-        """
-        kwargs: Dict[str, Optional[str]] = {
-            "platform": platform,
-            "chat_id": chat_id,
-            "chat_type": chat_type,
-            "user_id": user_id,
-            "guild_id": guild_id,
-            "team_id": team_id,
-        }
-        for binding in self._bindings:
-            if self._matches(binding, **kwargs):
-                return binding.agent_id
-        return self._default_agent_id
-
-    # ── internals ────────────────────────────────────────────────────
-
-    @staticmethod
-    def _matches(binding: Binding, **kwargs: Optional[str]) -> bool:
-        """Check whether *binding* matches the supplied keyword arguments.
-
-        Uses AND semantics: every key present in ``binding.match`` must
-        equal the corresponding value in *kwargs*.  Keys absent from the
-        binding act as wildcards (always match).
-        """
-        for key, required_value in binding.match.items():
-            actual = kwargs.get(key)
-            if actual is None:
-                return False
-            if str(actual) != str(required_value):
-                return False
-        return True

gateway/run.py

@@ -48,7 +48,7 @@ _config_path = _hermes_home / 'config.yaml'
 if _config_path.exists():
     try:
         import yaml as _yaml
-        with open(_config_path, encoding="utf-8") as _f:
+        with open(_config_path) as _f:
             _cfg = _yaml.safe_load(_f) or {}
         # Top-level simple values (fallback only — don't override .env)
         for _key, _val in _cfg.items():
@@ -75,16 +75,11 @@ if _config_path.exists():
                 "container_memory": "TERMINAL_CONTAINER_MEMORY",
                 "container_disk": "TERMINAL_CONTAINER_DISK",
                 "container_persistent": "TERMINAL_CONTAINER_PERSISTENT",
-                "docker_volumes": "TERMINAL_DOCKER_VOLUMES",
                 "sandbox_dir": "TERMINAL_SANDBOX_DIR",
             }
             for _cfg_key, _env_var in _terminal_env_map.items():
                 if _cfg_key in _terminal_cfg:
-                    _val = _terminal_cfg[_cfg_key]
-                    if isinstance(_val, list):
-                        os.environ[_env_var] = json.dumps(_val)
-                    else:
-                        os.environ[_env_var] = str(_val)
+                    os.environ[_env_var] = str(_terminal_cfg[_cfg_key])
         _compression_cfg = _cfg.get("compression", {})
         if _compression_cfg and isinstance(_compression_cfg, dict):
             _compression_env_map = {
@@ -161,8 +156,6 @@ from gateway.session import (
 )
 from gateway.delivery import DeliveryRouter, DeliveryTarget
 from gateway.platforms.base import BasePlatformAdapter, MessageEvent, MessageType
-from gateway.agent_registry import AgentRegistry, AgentConfig
-from gateway.router import BindingRouter
 
 logger = logging.getLogger(__name__)
 
@@ -209,24 +202,6 @@ class GatewayRunner:
         self._provider_routing = self._load_provider_routing()
         self._fallback_model = self._load_fallback_model()
 
-        # Load raw config dict for multi-agent support
-        self._raw_config: dict = {}
-        try:
-            import yaml as _y
-            _cfg_path = _hermes_home / 'config.yaml'
-            if _cfg_path.exists():
-                with open(_cfg_path, encoding='utf-8') as _f:
-                    self._raw_config = _y.safe_load(_f) or {}
-        except Exception:
-            pass
-
-        # Multi-agent registry and router
-        self._agent_registry = AgentRegistry(self._raw_config, global_config=self._raw_config)
-        self._router = BindingRouter(
-            self._raw_config.get('bindings', []),
-            self._agent_registry.get_default().id,
-        )
-
         # Wire process registry into session store for reset protection
         from tools.process_registry import process_registry
         self.session_store = SessionStore(
@@ -336,7 +311,7 @@ class GatewayRunner:
                 import yaml as _y
                 cfg_path = _hermes_home / "config.yaml"
                 if cfg_path.exists():
-                    with open(cfg_path, encoding="utf-8") as _f:
+                    with open(cfg_path) as _f:
                         cfg = _y.safe_load(_f) or {}
                     file_path = cfg.get("prefill_messages_file", "")
             except Exception:
@@ -374,7 +349,7 @@ class GatewayRunner:
             import yaml as _y
             cfg_path = _hermes_home / "config.yaml"
             if cfg_path.exists():
-                with open(cfg_path, encoding="utf-8") as _f:
+                with open(cfg_path) as _f:
                     cfg = _y.safe_load(_f) or {}
                 return (cfg.get("agent", {}).get("system_prompt", "") or "").strip()
         except Exception:
@@ -395,7 +370,7 @@ class GatewayRunner:
                 import yaml as _y
                 cfg_path = _hermes_home / "config.yaml"
                 if cfg_path.exists():
-                    with open(cfg_path, encoding="utf-8") as _f:
+                    with open(cfg_path) as _f:
                         cfg = _y.safe_load(_f) or {}
                     effort = str(cfg.get("agent", {}).get("reasoning_effort", "") or "").strip()
             except Exception:
@@ -411,41 +386,6 @@ class GatewayRunner:
         logger.warning("Unknown reasoning_effort '%s', using default (medium)", effort)
         return None
 
-    @staticmethod
-    def _load_background_notifications_mode() -> str:
-        """Load background process notification mode from config or env var.
-
-        Modes:
-          - ``all``    — push running-output updates *and* the final message (default)
-          - ``result`` — only the final completion message (regardless of exit code)
-          - ``error``  — only the final message when exit code is non-zero
-          - ``off``    — no watcher messages at all
-        """
-        mode = os.getenv("HERMES_BACKGROUND_NOTIFICATIONS", "")
-        if not mode:
-            try:
-                import yaml as _y
-                cfg_path = _hermes_home / "config.yaml"
-                if cfg_path.exists():
-                    with open(cfg_path, encoding="utf-8") as _f:
-                        cfg = _y.safe_load(_f) or {}
-                    raw = cfg.get("display", {}).get("background_process_notifications")
-                    if raw is False:
-                        mode = "off"
-                    elif raw not in (None, ""):
-                        mode = str(raw)
-            except Exception:
-                pass
-        mode = (mode or "all").strip().lower()
-        valid = {"all", "result", "error", "off"}
-        if mode not in valid:
-            logger.warning(
-                "Unknown background_process_notifications '%s', defaulting to 'all'",
-                mode,
-            )
-            return "all"
-        return mode
-
     @staticmethod
     def _load_provider_routing() -> dict:
         """Load OpenRouter provider routing preferences from config.yaml."""
@@ -453,7 +393,7 @@ class GatewayRunner:
             import yaml as _y
             cfg_path = _hermes_home / "config.yaml"
             if cfg_path.exists():
-                with open(cfg_path, encoding="utf-8") as _f:
+                with open(cfg_path) as _f:
                     cfg = _y.safe_load(_f) or {}
                 return cfg.get("provider_routing", {}) or {}
         except Exception:
@@ -471,7 +411,7 @@ class GatewayRunner:
             import yaml as _y
             cfg_path = _hermes_home / "config.yaml"
             if cfg_path.exists():
-                with open(cfg_path, encoding="utf-8") as _f:
+                with open(cfg_path) as _f:
                     cfg = _y.safe_load(_f) or {}
                 fb = cfg.get("fallback_model", {}) or {}
                 if fb.get("provider") and fb.get("model"):
@@ -805,19 +745,10 @@ class GatewayRunner:
                         )
             return None
         
-        # Resolve which agent handles this message
-        agent_id = self._router.resolve(
-            platform=event.source.platform.value if hasattr(event.source, 'platform') else 'cli',
-            chat_id=getattr(event.source, 'chat_id', None),
-            chat_type=getattr(event.source, 'chat_type', None),
-            user_id=getattr(event.source, 'user_id', None),
-        )
-        agent_config = self._agent_registry.get(agent_id)
-        
         # PRIORITY: If an agent is already running for this session, interrupt it
         # immediately. This is before command parsing to minimize latency -- the
         # user's "stop" message reaches the agent as fast as possible.
-        _quick_key = build_session_key(source, agent_id=agent_id)
+        _quick_key = build_session_key(source)
         if _quick_key in self._running_agents:
             running_agent = self._running_agents[_quick_key]
             logger.debug("PRIORITY interrupt for session %s", _quick_key[:20])
@@ -835,8 +766,7 @@ class GatewayRunner:
         _known_commands = {"new", "reset", "help", "status", "stop", "model",
                           "personality", "retry", "undo", "sethome", "set-home",
                           "compress", "usage", "insights", "reload-mcp", "reload_mcp",
-                          "update", "title", "resume", "provider", "rollback",
-                          "agents"}
+                          "update", "title", "resume", "provider"}
         if command and command in _known_commands:
             await self.hooks.emit(f"command:{command}", {
                 "platform": source.platform.value if source.platform else "",
@@ -895,21 +825,6 @@ class GatewayRunner:
 
         if command == "resume":
             return await self._handle_resume_command(event)
-
-        if command == "rollback":
-            return await self._handle_rollback_command(event)
-
-        if command == "agents":
-            agent_lines = []
-            for ac in self._agent_registry.list_agents():
-                marker = ' *' if ac.default else ''
-                model = ac.model or '(inherited)'
-                agent_lines.append(f'  {ac.id}{marker}  {model}  {ac.description}')
-            response = f"📋 Agents:\n" + '\n'.join(agent_lines) + f"\n\n🤖 This chat → {agent_id}"
-            adapter = self.adapters.get(source.platform)
-            if adapter:
-                await adapter.send(source.chat_id, response)
-            return response
         
         # Skill slash commands: /skill-name loads the skill and sends to agent
         if command:
@@ -927,7 +842,7 @@ class GatewayRunner:
                 logger.debug("Skill command check failed (non-fatal): %s", e)
         
         # Check for pending exec approval responses
-        session_key_preview = build_session_key(source, agent_id=agent_id)
+        session_key_preview = build_session_key(source)
         if session_key_preview in self._pending_approvals:
             user_text = event.text.strip().lower()
             if user_text in ("yes", "y", "approve", "ok", "go", "do it"):
@@ -992,12 +907,9 @@ class GatewayRunner:
         # repeated truncation/context failures.  Detect this early and
         # compress proactively — before the agent even starts.  (#628)
         #
-        # Token source priority:
-        # 1. Actual API-reported prompt_tokens from the last turn
-        #    (stored in session_entry.last_prompt_tokens)
-        # 2. Rough char-based estimate (str(msg)//4) with a 1.4x
-        #    safety factor to account for overestimation on tool-heavy
-        #    conversations (code/JSON tokenizes at 5-7+ chars/token).
+        # Thresholds are derived from the SAME compression config the
+        # agent uses (compression.threshold × model context length) so
+        # CLI and messaging platforms behave identically.
         # -----------------------------------------------------------------
         if history and len(history) >= 4:
             from agent.model_metadata import (
@@ -1014,7 +926,7 @@ class GatewayRunner:
                 _hyg_cfg_path = _hermes_home / "config.yaml"
                 if _hyg_cfg_path.exists():
                     import yaml as _hyg_yaml
-                    with open(_hyg_cfg_path, encoding="utf-8") as _hyg_f:
+                    with open(_hyg_cfg_path) as _hyg_f:
                         _hyg_data = _hyg_yaml.safe_load(_hyg_f) or {}
 
                     # Resolve model name (same logic as run_sync)
@@ -1048,34 +960,19 @@ class GatewayRunner:
                 _compress_token_threshold = int(
                     _hyg_context_length * _hyg_threshold_pct
                 )
+                # Warn if still huge after compression (95% of context)
                 _warn_token_threshold = int(_hyg_context_length * 0.95)
 
                 _msg_count = len(history)
-
-                # Prefer actual API-reported tokens from the last turn
-                # (stored in session entry) over the rough char-based estimate.
-                # The rough estimate (str(msg)//4) overestimates by 30-50% on
-                # tool-heavy/code-heavy conversations, causing premature compression.
-                _stored_tokens = session_entry.last_prompt_tokens
-                if _stored_tokens > 0:
-                    _approx_tokens = _stored_tokens
-                    _token_source = "actual"
-                else:
-                    _approx_tokens = estimate_messages_tokens_rough(history)
-                    # Apply safety factor only for rough estimates
-                    _compress_token_threshold = int(
-                        _compress_token_threshold * 1.4
-                    )
-                    _warn_token_threshold = int(_warn_token_threshold * 1.4)
-                    _token_source = "estimated"
+                _approx_tokens = estimate_messages_tokens_rough(history)
 
                 _needs_compress = _approx_tokens >= _compress_token_threshold
 
                 if _needs_compress:
                     logger.info(
-                        "Session hygiene: %s messages, ~%s tokens (%s) — auto-compressing "
+                        "Session hygiene: %s messages, ~%s tokens — auto-compressing "
                         "(threshold: %s%% of %s = %s tokens)",
-                        _msg_count, f"{_approx_tokens:,}", _token_source,
+                        _msg_count, f"{_approx_tokens:,}",
                         int(_hyg_threshold_pct * 100),
                         f"{_hyg_context_length:,}",
                         f"{_compress_token_threshold:,}",
@@ -1311,8 +1208,7 @@ class GatewayRunner:
                 history=history,
                 source=source,
                 session_id=session_entry.session_id,
-                session_key=session_key,
-                agent_config=agent_config,
+                session_key=session_key
             )
             
             response = agent_result.get("final_response", "")
@@ -1383,11 +1279,6 @@ class GatewayRunner:
                         {"role": "assistant", "content": response, "timestamp": ts}
                     )
             else:
-                # The agent already persisted these messages to SQLite via
-                # _flush_messages_to_session_db(), so skip the DB write here
-                # to prevent the duplicate-write bug (#860).  We still write
-                # to JSONL for backward compatibility and as a backup.
-                agent_persisted = self._session_db is not None
                 for msg in new_messages:
                     # Skip system messages (they're rebuilt each run)
                     if msg.get("role") == "system":
@@ -1395,15 +1286,11 @@ class GatewayRunner:
                     # Add timestamp to each message for debugging
                     entry = {**msg, "timestamp": ts}
                     self.session_store.append_to_transcript(
-                        session_entry.session_id, entry,
-                        skip_db=agent_persisted,
+                        session_entry.session_id, entry
                     )
             
-            # Update session with actual prompt token count from the agent
-            self.session_store.update_session(
-                session_entry.session_key,
-                last_prompt_tokens=agent_result.get("last_prompt_tokens", 0),
-            )
+            # Update session
+            self.session_store.update_session(session_entry.session_key)
             
             return response
             
@@ -1508,7 +1395,6 @@ class GatewayRunner:
             "`/resume [name]` — Resume a previously-named session",
             "`/usage` — Show token usage for this session",
             "`/insights [days]` — Show usage insights and analytics",
-            "`/rollback [number]` — List or restore filesystem checkpoints",
             "`/reload-mcp` — Reload MCP servers from config",
             "`/update` — Update Hermes Agent to the latest version",
             "`/help` — Show this message",
@@ -1543,7 +1429,7 @@ class GatewayRunner:
         current_provider = "openrouter"
         try:
             if config_path.exists():
-                with open(config_path, encoding="utf-8") as f:
+                with open(config_path) as f:
                     cfg = yaml.safe_load(f) or {}
                 model_cfg = cfg.get("model", {})
                 if isinstance(model_cfg, str):
@@ -1563,11 +1449,6 @@ class GatewayRunner:
             except Exception:
                 current_provider = "openrouter"
 
-        # Detect custom endpoint: provider resolved to openrouter but a custom
-        # base URL is configured — the user set up a custom endpoint.
-        if current_provider == "openrouter" and os.getenv("OPENAI_BASE_URL", "").strip():
-            current_provider = "custom"
-
         if not args:
             provider_label = _PROVIDER_LABELS.get(current_provider, current_provider)
             lines = [
@@ -1634,14 +1515,14 @@ class GatewayRunner:
             try:
                 user_config = {}
                 if config_path.exists():
-                    with open(config_path, encoding="utf-8") as f:
+                    with open(config_path) as f:
                         user_config = yaml.safe_load(f) or {}
                 if "model" not in user_config or not isinstance(user_config["model"], dict):
                     user_config["model"] = {}
                 user_config["model"]["default"] = new_model
                 if provider_changed:
                     user_config["model"]["provider"] = target_provider
-                with open(config_path, 'w', encoding="utf-8") as f:
+                with open(config_path, 'w') as f:
                     yaml.dump(user_config, f, default_flow_style=False, sort_keys=False)
             except Exception as e:
                 return f"⚠️ Failed to save model change: {e}"
@@ -1678,7 +1559,7 @@ class GatewayRunner:
         config_path = _hermes_home / 'config.yaml'
         try:
             if config_path.exists():
-                with open(config_path, encoding="utf-8") as f:
+                with open(config_path) as f:
                     cfg = yaml.safe_load(f) or {}
                 model_cfg = cfg.get("model", {})
                 if isinstance(model_cfg, dict):
@@ -1694,10 +1575,6 @@ class GatewayRunner:
             except Exception:
                 current_provider = "openrouter"
 
-        # Detect custom endpoint
-        if current_provider == "openrouter" and os.getenv("OPENAI_BASE_URL", "").strip():
-            current_provider = "custom"
-
         current_label = _PROVIDER_LABELS.get(current_provider, current_provider)
 
         lines = [
@@ -1727,7 +1604,7 @@ class GatewayRunner:
 
         try:
             if config_path.exists():
-                with open(config_path, 'r', encoding="utf-8") as f:
+                with open(config_path, 'r') as f:
                     config = yaml.safe_load(f) or {}
                 personalities = config.get("agent", {}).get("personalities", {})
             else:
@@ -1756,7 +1633,7 @@ class GatewayRunner:
                 if "agent" not in config or not isinstance(config.get("agent"), dict):
                     config["agent"] = {}
                 config["agent"]["system_prompt"] = new_prompt
-                with open(config_path, 'w', encoding="utf-8") as f:
+                with open(config_path, 'w') as f:
                     yaml.dump(config, f, default_flow_style=False, sort_keys=False)
             except Exception as e:
                 return f"⚠️ Failed to save personality change: {e}"
@@ -1840,10 +1717,10 @@ class GatewayRunner:
             config_path = _hermes_home / 'config.yaml'
             user_config = {}
             if config_path.exists():
-                with open(config_path, encoding="utf-8") as f:
+                with open(config_path) as f:
                     user_config = yaml.safe_load(f) or {}
             user_config[env_key] = chat_id
-            with open(config_path, 'w', encoding="utf-8") as f:
+            with open(config_path, 'w') as f:
                 yaml.dump(user_config, f, default_flow_style=False)
             # Also set in the current environment so it takes effect immediately
             os.environ[env_key] = str(chat_id)
@@ -1855,65 +1732,6 @@ class GatewayRunner:
             f"Cron jobs and cross-platform messages will be delivered here."
         )
     
-    async def _handle_rollback_command(self, event: MessageEvent) -> str:
-        """Handle /rollback command — list or restore filesystem checkpoints."""
-        from tools.checkpoint_manager import CheckpointManager, format_checkpoint_list
-
-        # Read checkpoint config from config.yaml
-        cp_cfg = {}
-        try:
-            import yaml as _y
-            _cfg_path = _hermes_home / "config.yaml"
-            if _cfg_path.exists():
-                with open(_cfg_path, encoding="utf-8") as _f:
-                    _data = _y.safe_load(_f) or {}
-                cp_cfg = _data.get("checkpoints", {})
-                if isinstance(cp_cfg, bool):
-                    cp_cfg = {"enabled": cp_cfg}
-        except Exception:
-            pass
-
-        if not cp_cfg.get("enabled", False):
-            return (
-                "Checkpoints are not enabled.\n"
-                "Enable in config.yaml:\n```\ncheckpoints:\n  enabled: true\n```"
-            )
-
-        mgr = CheckpointManager(
-            enabled=True,
-            max_snapshots=cp_cfg.get("max_snapshots", 50),
-        )
-
-        cwd = os.getenv("MESSAGING_CWD", str(Path.home()))
-        arg = event.get_command_args().strip()
-
-        if not arg:
-            checkpoints = mgr.list_checkpoints(cwd)
-            return format_checkpoint_list(checkpoints, cwd)
-
-        # Restore by number or hash
-        checkpoints = mgr.list_checkpoints(cwd)
-        if not checkpoints:
-            return f"No checkpoints found for {cwd}"
-
-        target_hash = None
-        try:
-            idx = int(arg) - 1
-            if 0 <= idx < len(checkpoints):
-                target_hash = checkpoints[idx]["hash"]
-            else:
-                return f"Invalid checkpoint number. Use 1-{len(checkpoints)}."
-        except ValueError:
-            target_hash = arg
-
-        result = mgr.restore(cwd, target_hash)
-        if result["success"]:
-            return (
-                f"✅ Restored to checkpoint {result['restored_to']}: {result['reason']}\n"
-                f"A pre-rollback snapshot was saved automatically."
-            )
-        return f"❌ {result['error']}"
-
     async def _handle_compress_command(self, event: MessageEvent) -> str:
         """Handle /compress command -- manually compress conversation context."""
         source = event.source
@@ -2475,12 +2293,6 @@ class GatewayRunner:
 
         Runs as an asyncio task. Stays silent when nothing changed.
         Auto-removes when the process exits or is killed.
-
-        Notification mode (from ``display.background_process_notifications``):
-          - ``all``    — running-output updates + final message
-          - ``result`` — final completion message only
-          - ``error``  — final message only when exit code != 0
-          - ``off``    — no messages at all
         """
         from tools.process_registry import process_registry
 
@@ -2489,21 +2301,8 @@ class GatewayRunner:
         session_key = watcher.get("session_key", "")
         platform_name = watcher.get("platform", "")
         chat_id = watcher.get("chat_id", "")
-        notify_mode = self._load_background_notifications_mode()
 
-        logger.debug("Process watcher started: %s (every %ss, notify=%s)",
-                      session_id, interval, notify_mode)
-
-        if notify_mode == "off":
-            # Still wait for the process to exit so we can log it, but don't
-            # push any messages to the user.
-            while True:
-                await asyncio.sleep(interval)
-                session = process_registry.get(session_id)
-                if session is None or session.exited:
-                    break
-            logger.debug("Process watcher ended (silent): %s", session_id)
-            return
+        logger.debug("Process watcher started: %s (every %ss)", session_id, interval)
 
         last_output_len = 0
         while True:
@@ -2518,31 +2317,27 @@ class GatewayRunner:
             last_output_len = current_output_len
 
             if session.exited:
-                # Decide whether to notify based on mode
-                should_notify = (
-                    notify_mode in ("all", "result")
-                    or (notify_mode == "error" and session.exit_code not in (0, None))
+                # Process finished -- deliver final update
+                new_output = session.output_buffer[-1000:] if session.output_buffer else ""
+                message_text = (
+                    f"[Background process {session_id} finished with exit code {session.exit_code}~ "
+                    f"Here's the final output:\n{new_output}]"
                 )
-                if should_notify:
-                    new_output = session.output_buffer[-1000:] if session.output_buffer else ""
-                    message_text = (
-                        f"[Background process {session_id} finished with exit code {session.exit_code}~ "
-                        f"Here's the final output:\n{new_output}]"
-                    )
-                    adapter = None
-                    for p, a in self.adapters.items():
-                        if p.value == platform_name:
-                            adapter = a
-                            break
-                    if adapter and chat_id:
-                        try:
-                            await adapter.send(chat_id, message_text)
-                        except Exception as e:
-                            logger.error("Watcher delivery error: %s", e)
+                # Try to deliver to the originating platform
+                adapter = None
+                for p, a in self.adapters.items():
+                    if p.value == platform_name:
+                        adapter = a
+                        break
+                if adapter and chat_id:
+                    try:
+                        await adapter.send(chat_id, message_text)
+                    except Exception as e:
+                        logger.error("Watcher delivery error: %s", e)
                 break
 
-            elif has_new_output and notify_mode == "all":
-                # New output available -- deliver status update (only in "all" mode)
+            elif has_new_output:
+                # New output available -- deliver status update
                 new_output = session.output_buffer[-500:] if session.output_buffer else ""
                 message_text = (
                     f"[Background process {session_id} is still running~ "
@@ -2568,8 +2363,7 @@ class GatewayRunner:
         history: List[Dict[str, Any]],
         source: SessionSource,
         session_id: str,
-        session_key: str = None,
-        agent_config: AgentConfig = None,
+        session_key: str = None
     ) -> Dict[str, Any]:
         """
         Run the agent with the given message and context.
@@ -2594,8 +2388,6 @@ class GatewayRunner:
             Platform.DISCORD: "hermes-discord",
             Platform.WHATSAPP: "hermes-whatsapp",
             Platform.SLACK: "hermes-slack",
-            Platform.SIGNAL: "hermes-signal",
-            Platform.HOMEASSISTANT: "hermes-homeassistant",
         }
         
         # Try to load platform_toolsets from config
@@ -2604,7 +2396,7 @@ class GatewayRunner:
             config_path = _hermes_home / 'config.yaml'
             if config_path.exists():
                 import yaml
-                with open(config_path, 'r', encoding="utf-8") as f:
+                with open(config_path, 'r') as f:
                     user_config = yaml.safe_load(f) or {}
                 platform_toolsets_config = user_config.get("platform_toolsets", {})
         except Exception as e:
@@ -2617,8 +2409,6 @@ class GatewayRunner:
             Platform.DISCORD: "discord",
             Platform.WHATSAPP: "whatsapp",
             Platform.SLACK: "slack",
-            Platform.SIGNAL: "signal",
-            Platform.HOMEASSISTANT: "homeassistant",
         }.get(source.platform, "telegram")
         
         # Use config override if present (list of toolsets), otherwise hardcoded default
@@ -2636,7 +2426,7 @@ class GatewayRunner:
             _tp_cfg_path = _hermes_home / "config.yaml"
             if _tp_cfg_path.exists():
                 import yaml as _tp_yaml
-                with open(_tp_cfg_path, encoding="utf-8") as _tp_f:
+                with open(_tp_cfg_path) as _tp_f:
                     _tp_data = _tp_yaml.safe_load(_tp_f) or {}
                 _progress_cfg = _tp_data.get("display", {})
         except Exception:
@@ -2727,8 +2517,6 @@ class GatewayRunner:
         
         # Background task to send progress messages
         # Accumulates tool lines into a single message that gets edited
-        _progress_metadata = {"thread_id": source.thread_id} if source.thread_id else None
-
         async def send_progress_messages():
             if not progress_queue:
                 return
@@ -2758,15 +2546,15 @@ class GatewayRunner:
                             # Platform doesn't support editing — stop trying,
                             # send just this new line as a separate message
                             can_edit = False
-                            await adapter.send(chat_id=source.chat_id, content=msg, metadata=_progress_metadata)
+                            await adapter.send(chat_id=source.chat_id, content=msg)
                     else:
                         if can_edit:
                             # First tool: send all accumulated text as new message
                             full_text = "\n".join(progress_lines)
-                            result = await adapter.send(chat_id=source.chat_id, content=full_text, metadata=_progress_metadata)
+                            result = await adapter.send(chat_id=source.chat_id, content=full_text)
                         else:
                             # Editing unsupported: send just this line
-                            result = await adapter.send(chat_id=source.chat_id, content=msg, metadata=_progress_metadata)
+                            result = await adapter.send(chat_id=source.chat_id, content=msg)
                         if result.success and result.message_id:
                             progress_msg_id = result.message_id
 
@@ -2840,9 +2628,6 @@ class GatewayRunner:
             combined_ephemeral = context_prompt or ""
             if self._ephemeral_system_prompt:
                 combined_ephemeral = (combined_ephemeral + "\n\n" + self._ephemeral_system_prompt).strip()
-            # Prepend agent personality if available
-            if agent_config and agent_config.personality:
-                combined_ephemeral = (agent_config.personality + "\n\n" + combined_ephemeral).strip()
 
             # Re-read .env and config for fresh credentials (gateway is long-lived,
             # keys may change without restart).
@@ -2859,7 +2644,7 @@ class GatewayRunner:
                 import yaml as _y
                 _cfg_path = _hermes_home / "config.yaml"
                 if _cfg_path.exists():
-                    with open(_cfg_path, encoding="utf-8") as _f:
+                    with open(_cfg_path) as _f:
                         _cfg = _y.safe_load(_f) or {}
                     _model_cfg = _cfg.get("model", {})
                     if isinstance(_model_cfg, str):
@@ -2869,10 +2654,6 @@ class GatewayRunner:
             except Exception:
                 pass
 
-            # Agent-specific model override
-            if agent_config and agent_config.model:
-                model = agent_config.model
-
             try:
                 runtime_kwargs = _resolve_runtime_agent_kwargs()
             except Exception as exc:
@@ -2907,8 +2688,6 @@ class GatewayRunner:
                 honcho_session_key=session_key,
                 session_db=self._session_db,
                 fallback_model=self._fallback_model,
-                agent_tool_policy=agent_config.tool_policy if agent_config else None,
-                agent_workspace=str(agent_config.workspace_dir) if agent_config else None,
             )
             
             # Store agent reference for interrupt support
@@ -2976,13 +2755,6 @@ class GatewayRunner:
             
             # Return final response, or a message if something went wrong
             final_response = result.get("final_response")
-
-            # Extract last actual prompt token count from the agent's compressor
-            _last_prompt_toks = 0
-            _agent = agent_holder[0]
-            if _agent and hasattr(_agent, "context_compressor"):
-                _last_prompt_toks = getattr(_agent.context_compressor, "last_prompt_tokens", 0)
-
             if not final_response:
                 error_msg = f"⚠️ {result['error']}" if result.get("error") else "(No response generated)"
                 return {
@@ -2991,7 +2763,6 @@ class GatewayRunner:
                     "api_calls": result.get("api_calls", 0),
                     "tools": tools_holder[0] or [],
                     "history_offset": len(agent_history),
-                    "last_prompt_tokens": _last_prompt_toks,
                 }
             
             # Scan tool results for MEDIA:<path> tags that need to be delivered
@@ -3035,7 +2806,6 @@ class GatewayRunner:
                 "api_calls": result_holder[0].get("api_calls", 0) if result_holder[0] else 0,
                 "tools": tools_holder[0] or [],
                 "history_offset": len(agent_history),
-                "last_prompt_tokens": _last_prompt_toks,
             }
         
         # Start progress message sender if enabled
@@ -3114,8 +2884,7 @@ class GatewayRunner:
                     history=updated_history,
                     source=source,
                     session_id=session_id,
-                    session_key=session_key,
-                    agent_config=agent_config,
+                    session_key=session_key
                 )
         finally:
             # Stop progress sender and interrupt monitor
@@ -3357,7 +3126,7 @@ def main():
     config = None
     if args.config:
         import json
-        with open(args.config, encoding="utf-8") as f:
+        with open(args.config) as f:
             data = json.load(f)
             config = GatewayConfig.from_dict(data)
     

gateway/session.py

@@ -241,9 +241,6 @@ class SessionEntry:
     output_tokens: int = 0
     total_tokens: int = 0
     
-    # Last API-reported prompt tokens (for accurate compression pre-check)
-    last_prompt_tokens: int = 0
-    
     # Set when a session was created because the previous one expired;
     # consumed once by the message handler to inject a notice into context
     was_auto_reset: bool = False
@@ -260,7 +257,6 @@ class SessionEntry:
             "input_tokens": self.input_tokens,
             "output_tokens": self.output_tokens,
             "total_tokens": self.total_tokens,
-            "last_prompt_tokens": self.last_prompt_tokens,
         }
         if self.origin:
             result["origin"] = self.origin.to_dict()
@@ -276,8 +272,8 @@ class SessionEntry:
         if data.get("platform"):
             try:
                 platform = Platform(data["platform"])
-            except ValueError as e:
-                logger.debug("Unknown platform value %r: %s", data["platform"], e)
+            except ValueError:
+                pass
         
         return cls(
             session_key=data["session_key"],
@@ -291,30 +287,21 @@ class SessionEntry:
             input_tokens=data.get("input_tokens", 0),
             output_tokens=data.get("output_tokens", 0),
             total_tokens=data.get("total_tokens", 0),
-            last_prompt_tokens=data.get("last_prompt_tokens", 0),
         )
 
 
-def build_session_key(source: SessionSource, agent_id: str = 'main', dm_scope: str = 'main') -> str:
+def build_session_key(source: SessionSource) -> str:
     """Build a deterministic session key from a message source.
 
     This is the single source of truth for session key construction.
     WhatsApp DMs include chat_id (multi-user), other DMs do not (single owner).
-
-    Args:
-        source: The session source describing the message origin.
-        agent_id: Agent identifier for multi-agent setups (default 'main').
-        dm_scope: DM scoping strategy. 'per_peer' creates separate sessions
-                  per user_id; 'main' (default) uses a single DM session.
     """
     platform = source.platform.value
     if source.chat_type == "dm":
         if platform == "whatsapp" and source.chat_id:
-            return f"agent:{agent_id}:{platform}:dm:{source.chat_id}"
-        if dm_scope == "per_peer" and source.user_id:
-            return f"agent:{agent_id}:{platform}:dm:{source.user_id}"
-        return f"agent:{agent_id}:{platform}:dm"
-    return f"agent:{agent_id}:{platform}:{source.chat_type}:{source.chat_id}"
+            return f"agent:main:{platform}:dm:{source.chat_id}"
+        return f"agent:main:{platform}:dm"
+    return f"agent:main:{platform}:{source.chat_type}:{source.chat_id}"
 
 
 class SessionStore:
@@ -366,26 +353,12 @@ class SessionStore:
     
     def _save(self) -> None:
         """Save sessions index to disk (kept for session key -> ID mapping)."""
-        import tempfile
         self.sessions_dir.mkdir(parents=True, exist_ok=True)
         sessions_file = self.sessions_dir / "sessions.json"
-
+        
         data = {key: entry.to_dict() for key, entry in self._entries.items()}
-        fd, tmp_path = tempfile.mkstemp(
-            dir=str(self.sessions_dir), suffix=".tmp", prefix=".sessions_"
-        )
-        try:
-            with os.fdopen(fd, "w", encoding="utf-8") as f:
-                json.dump(data, f, indent=2)
-                f.flush()
-                os.fsync(f.fileno())
-            os.replace(tmp_path, sessions_file)
-        except BaseException:
-            try:
-                os.unlink(tmp_path)
-            except OSError as e:
-                logger.debug("Could not remove temp file %s: %s", tmp_path, e)
-            raise
+        with open(sessions_file, "w", encoding="utf-8") as f:
+            json.dump(data, f, indent=2)
     
     def _generate_session_key(self, source: SessionSource) -> str:
         """Generate a session key from a source."""
@@ -563,8 +536,7 @@ class SessionStore:
         self, 
         session_key: str,
         input_tokens: int = 0,
-        output_tokens: int = 0,
-        last_prompt_tokens: int = 0,
+        output_tokens: int = 0
     ) -> None:
         """Update a session's metadata after an interaction."""
         self._ensure_loaded()
@@ -574,8 +546,6 @@ class SessionStore:
             entry.updated_at = datetime.now()
             entry.input_tokens += input_tokens
             entry.output_tokens += output_tokens
-            if last_prompt_tokens > 0:
-                entry.last_prompt_tokens = last_prompt_tokens
             entry.total_tokens = entry.input_tokens + entry.output_tokens
             self._save()
             
@@ -693,17 +663,10 @@ class SessionStore:
         """Get the path to a session's legacy transcript file."""
         return self.sessions_dir / f"{session_id}.jsonl"
     
-    def append_to_transcript(self, session_id: str, message: Dict[str, Any], skip_db: bool = False) -> None:
-        """Append a message to a session's transcript (SQLite + legacy JSONL).
-
-        Args:
-            skip_db: When True, only write to JSONL and skip the SQLite write.
-                     Used when the agent already persisted messages to SQLite
-                     via its own _flush_messages_to_session_db(), preventing
-                     the duplicate-write bug (#860).
-        """
-        # Write to SQLite (unless the agent already handled it)
-        if self._db and not skip_db:
+    def append_to_transcript(self, session_id: str, message: Dict[str, Any]) -> None:
+        """Append a message to a session's transcript (SQLite + legacy JSONL)."""
+        # Write to SQLite
+        if self._db:
             try:
                 self._db.append_message(
                     session_id=session_id,

hermes_cli/auth.py

@@ -23,7 +23,6 @@ import stat
 import base64
 import hashlib
 import subprocess
-import threading
 import time
 import uuid
 import webbrowser
@@ -45,10 +44,6 @@ try:
     import fcntl
 except Exception:
     fcntl = None
-try:
-    import msvcrt
-except Exception:
-    msvcrt = None
 
 # =============================================================================
 # Constants
@@ -108,14 +103,6 @@ PROVIDER_REGISTRY: Dict[str, ProviderConfig] = {
         auth_type="oauth_external",
         inference_base_url=DEFAULT_CODEX_BASE_URL,
     ),
-    "nous-api": ProviderConfig(
-        id="nous-api",
-        name="Nous Portal (API Key)",
-        auth_type="api_key",
-        inference_base_url="https://inference-api.nousresearch.com/v1",
-        api_key_env_vars=("NOUS_API_KEY",),
-        base_url_env_var="NOUS_BASE_URL",
-    ),
     "zai": ProviderConfig(
         id="zai",
         name="Z.AI / GLM",
@@ -312,64 +299,31 @@ def _auth_lock_path() -> Path:
     return _auth_file_path().with_suffix(".lock")
 
 
-_auth_lock_holder = threading.local()
-
 @contextmanager
 def _auth_store_lock(timeout_seconds: float = AUTH_LOCK_TIMEOUT_SECONDS):
-    """Cross-process advisory lock for auth.json reads+writes.  Reentrant."""
-    # Reentrant: if this thread already holds the lock, just yield.
-    if getattr(_auth_lock_holder, "depth", 0) > 0:
-        _auth_lock_holder.depth += 1
-        try:
-            yield
-        finally:
-            _auth_lock_holder.depth -= 1
-        return
-
+    """Cross-process advisory lock for auth.json reads+writes."""
     lock_path = _auth_lock_path()
     lock_path.parent.mkdir(parents=True, exist_ok=True)
 
-    if fcntl is None and msvcrt is None:
-        _auth_lock_holder.depth = 1
-        try:
+    with lock_path.open("a+") as lock_file:
+        if fcntl is None:
             yield
-        finally:
-            _auth_lock_holder.depth = 0
-        return
+            return
 
-    # On Windows, msvcrt.locking needs the file to have content and the
-    # file pointer at position 0.  Ensure the lock file has at least 1 byte.
-    if msvcrt and (not lock_path.exists() or lock_path.stat().st_size == 0):
-        lock_path.write_text(" ", encoding="utf-8")
-
-    with lock_path.open("r+" if msvcrt else "a+") as lock_file:
         deadline = time.time() + max(1.0, timeout_seconds)
         while True:
             try:
-                if fcntl:
-                    fcntl.flock(lock_file.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
-                else:
-                    lock_file.seek(0)
-                    msvcrt.locking(lock_file.fileno(), msvcrt.LK_NBLCK, 1)
+                fcntl.flock(lock_file.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
                 break
-            except (BlockingIOError, OSError, PermissionError):
+            except BlockingIOError:
                 if time.time() >= deadline:
                     raise TimeoutError("Timed out waiting for auth store lock")
                 time.sleep(0.05)
 
-        _auth_lock_holder.depth = 1
         try:
             yield
         finally:
-            _auth_lock_holder.depth = 0
-            if fcntl:
-                fcntl.flock(lock_file.fileno(), fcntl.LOCK_UN)
-            elif msvcrt:
-                try:
-                    lock_file.seek(0)
-                    msvcrt.locking(lock_file.fileno(), msvcrt.LK_UNLCK, 1)
-                except (OSError, IOError):
-                    pass
+            fcntl.flock(lock_file.fileno(), fcntl.LOCK_UN)
 
 
 def _load_auth_store(auth_file: Optional[Path] = None) -> Dict[str, Any]:
@@ -521,7 +475,6 @@ def resolve_provider(
 
     # Normalize provider aliases
     _PROVIDER_ALIASES = {
-        "nous_api": "nous-api", "nousapi": "nous-api", "nous-portal-api": "nous-api",
         "glm": "zai", "z-ai": "zai", "z.ai": "zai", "zhipu": "zai",
         "kimi": "kimi-coding", "moonshot": "kimi-coding",
         "minimax-china": "minimax-cn", "minimax_cn": "minimax-cn",
@@ -1103,19 +1056,6 @@ def fetch_nous_models(
                 continue
             model_ids.append(mid)
 
-    # Sort: prefer opus > pro > haiku/flash > sonnet (sonnet is cheap/fast,
-    # users who want the best model should see opus first).
-    def _model_priority(mid: str) -> tuple:
-        low = mid.lower()
-        if "opus" in low:
-            return (0, mid)
-        if "pro" in low and "sonnet" not in low:
-            return (1, mid)
-        if "sonnet" in low:
-            return (3, mid)
-        return (2, mid)
-
-    model_ids.sort(key=_model_priority)
     return list(dict.fromkeys(model_ids))
 
 
@@ -1684,11 +1624,11 @@ def _save_model_choice(model_id: str) -> None:
     from hermes_cli.config import save_config, load_config, save_env_value
 
     config = load_config()
-    # Always use dict format so provider/base_url can be stored alongside
+    # Handle both string and dict model formats
     if isinstance(config.get("model"), dict):
         config["model"]["default"] = model_id
     else:
-        config["model"] = {"default": model_id}
+        config["model"] = model_id
     save_config(config)
     save_env_value("LLM_MODEL", model_id)
 

hermes_cli/banner.py

@@ -36,28 +36,6 @@ def cprint(text: str):
     _pt_print(_PT_ANSI(text))
 
 
-# =========================================================================
-# Skin-aware color helpers
-# =========================================================================
-
-def _skin_color(key: str, fallback: str) -> str:
-    """Get a color from the active skin, or return fallback."""
-    try:
-        from hermes_cli.skin_engine import get_active_skin
-        return get_active_skin().get_color(key, fallback)
-    except Exception:
-        return fallback
-
-
-def _skin_branding(key: str, fallback: str) -> str:
-    """Get a branding string from the active skin, or return fallback."""
-    try:
-        from hermes_cli.skin_engine import get_active_skin
-        return get_active_skin().get_branding(key, fallback)
-    except Exception:
-        return fallback
-
-
 # =========================================================================
 # ASCII Art & Branding
 # =========================================================================
@@ -239,24 +217,18 @@ def build_welcome_banner(console: Console, model: str, cwd: str,
     layout_table.add_column("left", justify="center")
     layout_table.add_column("right", justify="left")
 
-    # Resolve skin colors once for the entire banner
-    accent = _skin_color("banner_accent", "#FFBF00")
-    dim = _skin_color("banner_dim", "#B8860B")
-    text = _skin_color("banner_text", "#FFF8DC")
-    session_color = _skin_color("session_border", "#8B8682")
-
     left_lines = ["", HERMES_CADUCEUS, ""]
     model_short = model.split("/")[-1] if "/" in model else model
     if len(model_short) > 28:
         model_short = model_short[:25] + "..."
-    ctx_str = f" [dim {dim}]·[/] [dim {dim}]{_format_context_length(context_length)} context[/]" if context_length else ""
-    left_lines.append(f"[{accent}]{model_short}[/]{ctx_str} [dim {dim}]·[/] [dim {dim}]Nous Research[/]")
-    left_lines.append(f"[dim {dim}]{cwd}[/]")
+    ctx_str = f" [dim #B8860B]·[/] [dim #B8860B]{_format_context_length(context_length)} context[/]" if context_length else ""
+    left_lines.append(f"[#FFBF00]{model_short}[/]{ctx_str} [dim #B8860B]·[/] [dim #B8860B]Nous Research[/]")
+    left_lines.append(f"[dim #B8860B]{cwd}[/]")
     if session_id:
-        left_lines.append(f"[dim {session_color}]Session: {session_id}[/]")
+        left_lines.append(f"[dim #8B8682]Session: {session_id}[/]")
     left_content = "\n".join(left_lines)
 
-    right_lines = [f"[bold {accent}]Available Tools[/]"]
+    right_lines = ["[bold #FFBF00]Available Tools[/]"]
     toolsets_dict: Dict[str, list] = {}
 
     for tool in tools:
@@ -284,7 +256,7 @@ def build_welcome_banner(console: Console, model: str, cwd: str,
             if name in disabled_tools:
                 colored_names.append(f"[red]{name}[/]")
             else:
-                colored_names.append(f"[{text}]{name}[/]")
+                colored_names.append(f"[#FFF8DC]{name}[/]")
 
         tools_str = ", ".join(colored_names)
         if len(", ".join(sorted(tool_names))) > 45:
@@ -303,7 +275,7 @@ def build_welcome_banner(console: Console, model: str, cwd: str,
                 elif name in disabled_tools:
                     colored_names.append(f"[red]{name}[/]")
                 else:
-                    colored_names.append(f"[{text}]{name}[/]")
+                    colored_names.append(f"[#FFF8DC]{name}[/]")
             tools_str = ", ".join(colored_names)
 
         right_lines.append(f"[dim #B8860B]{toolset}:[/] {tools_str}")
@@ -334,7 +306,7 @@ def build_welcome_banner(console: Console, model: str, cwd: str,
                 )
 
     right_lines.append("")
-    right_lines.append(f"[bold {accent}]Available Skills[/]")
+    right_lines.append("[bold #FFBF00]Available Skills[/]")
     skills_by_category = get_available_skills()
     total_skills = sum(len(s) for s in skills_by_category.values())
 
@@ -348,9 +320,9 @@ def build_welcome_banner(console: Console, model: str, cwd: str,
                 skills_str = ", ".join(skill_names)
             if len(skills_str) > 50:
                 skills_str = skills_str[:47] + "..."
-            right_lines.append(f"[dim {dim}]{category}:[/] [{text}]{skills_str}[/]")
+            right_lines.append(f"[dim #B8860B]{category}:[/] [#FFF8DC]{skills_str}[/]")
     else:
-        right_lines.append(f"[dim {dim}]No skills installed[/]")
+        right_lines.append("[dim #B8860B]No skills installed[/]")
 
     right_lines.append("")
     mcp_connected = sum(1 for s in mcp_status if s["connected"]) if mcp_status else 0
@@ -358,7 +330,7 @@ def build_welcome_banner(console: Console, model: str, cwd: str,
     if mcp_connected:
         summary_parts.append(f"{mcp_connected} MCP servers")
     summary_parts.append("/help for commands")
-    right_lines.append(f"[dim {dim}]{' · '.join(summary_parts)}[/]")
+    right_lines.append(f"[dim #B8860B]{' · '.join(summary_parts)}[/]")
 
     # Update check — show if behind origin/main
     try:
@@ -375,13 +347,10 @@ def build_welcome_banner(console: Console, model: str, cwd: str,
     right_content = "\n".join(right_lines)
     layout_table.add_row(left_content, right_content)
 
-    agent_name = _skin_branding("agent_name", "Hermes Agent")
-    title_color = _skin_color("banner_title", "#FFD700")
-    border_color = _skin_color("banner_border", "#CD7F32")
     outer_panel = Panel(
         layout_table,
-        title=f"[bold {title_color}]{agent_name} {VERSION}[/]",
-        border_style=border_color,
+        title=f"[bold #FFD700]Hermes Agent {VERSION}[/]",
+        border_style="#CD7F32",
         padding=(0, 2),
     )
 

hermes_cli/clipboard.py

@@ -292,12 +292,9 @@ def _convert_to_png(path: Path) -> bool:
             ["convert", str(tmp), "png:" + str(path)],
             capture_output=True, timeout=5,
         )
+        tmp.unlink(missing_ok=True)
         if r.returncode == 0 and path.exists() and path.stat().st_size > 0:
-            tmp.unlink(missing_ok=True)
             return True
-        else:
-            # Convert failed — restore the original file
-            tmp.rename(path)
     except FileNotFoundError:
         logger.debug("ImageMagick not installed — cannot convert BMP to PNG")
         if tmp.exists() and not path.exists():

hermes_cli/codex_models.py

@@ -47,7 +47,7 @@ def _fetch_models_from_api(access_token: str) -> List[str]:
         if item.get("supported_in_api") is False:
             continue
         visibility = item.get("visibility", "")
-        if isinstance(visibility, str) and visibility.strip().lower() == "hidden":
+        if isinstance(visibility, str) and visibility.strip().lower() == "hide":
             continue
         priority = item.get("priority")
         rank = int(priority) if isinstance(priority, (int, float)) else 10_000

hermes_cli/commands.py

@@ -29,7 +29,6 @@ COMMANDS = {
     "/undo": "Remove the last user/assistant exchange",
     "/save": "Save the current conversation",
     "/config": "Show current configuration",
-    "/agents": "List configured agents and active bindings",
     "/cron": "Manage scheduled tasks (list, add, remove)",
     "/skills": "Search, install, inspect, or manage skills from online registries",
     "/platforms": "Show gateway/messaging platform status",
@@ -40,8 +39,6 @@ COMMANDS = {
     "/insights": "Show usage insights and analytics (last 30 days)",
     "/paste": "Check clipboard for an image and attach it",
     "/reload-mcp": "Reload MCP servers from config.yaml",
-    "/rollback": "List or restore filesystem checkpoints (usage: /rollback [number])",
-    "/skin": "Show or change the display skin/theme",
     "/quit": "Exit the CLI (also: /exit, /q)",
 }
 

hermes_cli/config.py

@@ -14,9 +14,8 @@ This module provides:
 
 import os
 import platform
-import stat
-import subprocess
 import sys
+import subprocess
 from pathlib import Path
 from typing import Dict, Any, Optional, List, Tuple
 
@@ -63,9 +62,7 @@ def ensure_hermes_home():
 DEFAULT_CONFIG = {
     "model": "anthropic/claude-opus-4.6",
     "toolsets": ["hermes-cli"],
-    "agent": {
-        "max_turns": 90,
-    },
+    "max_turns": 100,
     
     "terminal": {
         "backend": "local",
@@ -80,10 +77,6 @@ DEFAULT_CONFIG = {
         "container_memory": 5120,       # MB (default 5GB)
         "container_disk": 51200,        # MB (default 50GB)
         "container_persistent": True,   # Persist filesystem across sessions
-        # Docker volume mounts — share host directories with the container.
-        # Each entry is "host_path:container_path" (standard Docker -v syntax).
-        # Example: ["/home/user/projects:/workspace/projects", "/data:/data"]
-        "docker_volumes": [],
     },
     
     "browser": {
@@ -91,14 +84,6 @@ DEFAULT_CONFIG = {
         "record_sessions": False,  # Auto-record browser sessions as WebM videos
     },
     
-    # Filesystem checkpoints — automatic snapshots before destructive file ops.
-    # When enabled, the agent takes a snapshot of the working directory once per
-    # conversation turn (on first write_file/patch call).  Use /rollback to restore.
-    "checkpoints": {
-        "enabled": False,
-        "max_snapshots": 50,  # Max checkpoints to keep per directory
-    },
-    
     "compression": {
         "enabled": True,
         "threshold": 0.85,
@@ -119,27 +104,11 @@ DEFAULT_CONFIG = {
         },
     },
     
-    # Subagent configuration — model/provider for tasks spawned via delegate_task.
-    # By default subagents inherit the parent agent's model and provider.
-    # Set "model" to use a cheaper/faster model for delegated subtasks.
-    "subagent": {
-        # "model": "google/gemini-3-flash-preview",
-    },
-
-    # Named agent profiles — reusable bundles of model + toolsets + prompt.
-    # Define profiles here and activate them with `hermes chat --agent <name>`.
-    "agents": {},
-
-    # Bindings — map trigger patterns or platforms to named agent profiles.
-    # Example: [{"pattern": "review *", "agent": "code-reviewer"}]
-    "bindings": [],
-
     "display": {
         "compact": False,
         "personality": "kawaii",
-        "resume_display": "full",
-        "bell_on_complete": False,
-        "skin": "default",
+        "resume_display": "full",  # "full" (show previous messages) | "minimal" (one-liner only)
+        "bell_on_complete": False,  # Play terminal bell (\a) when agent finishes a response
     },
     
     # Text-to-speech configuration
@@ -197,7 +166,7 @@ DEFAULT_CONFIG = {
     "command_allowlist": [],
 
     # Config schema version - bump this when adding new required fields
-    "_config_version": 7,
+    "_config_version": 5,
 }
 
 # =============================================================================
@@ -222,22 +191,6 @@ REQUIRED_ENV_VARS = {}
 # Optional environment variables that enhance functionality
 OPTIONAL_ENV_VARS = {
     # ── Provider (handled in provider selection, not shown in checklists) ──
-    "NOUS_API_KEY": {
-        "description": "Nous Portal API key (direct API key access to Nous inference)",
-        "prompt": "Nous Portal API key",
-        "url": "https://portal.nousresearch.com",
-        "password": True,
-        "category": "provider",
-        "advanced": True,
-    },
-    "NOUS_BASE_URL": {
-        "description": "Nous Portal base URL override",
-        "prompt": "Nous Portal base URL (leave empty for default)",
-        "url": None,
-        "password": False,
-        "category": "provider",
-        "advanced": True,
-    },
     "OPENROUTER_API_KEY": {
         "description": "OpenRouter API key (for vision, web scraping helpers, and MoA)",
         "prompt": "OpenRouter API key",
@@ -448,18 +401,14 @@ OPTIONAL_ENV_VARS = {
         "category": "messaging",
     },
     "SLACK_BOT_TOKEN": {
-        "description": "Slack bot token (xoxb-). Get from OAuth & Permissions after installing your app. "
-                       "Required scopes: chat:write, app_mentions:read, channels:history, groups:history, "
-                       "im:history, im:read, im:write, users:read, files:write",
+        "description": "Slack bot integration",
         "prompt": "Slack Bot Token (xoxb-...)",
         "url": "https://api.slack.com/apps",
         "password": True,
         "category": "messaging",
     },
     "SLACK_APP_TOKEN": {
-        "description": "Slack app-level token (xapp-) for Socket Mode. Get from Basic Information → "
-                       "App-Level Tokens. Also ensure Event Subscriptions include: message.im, "
-                       "message.channels, message.groups, app_mention",
+        "description": "Slack Socket Mode connection",
         "prompt": "Slack App Token (xapp-...)",
         "url": "https://api.slack.com/apps",
         "password": True,
@@ -662,23 +611,6 @@ def migrate_config(interactive: bool = True, quiet: bool = False) -> Dict[str, A
                 tz_display = config["timezone"] or "(server-local)"
                 print(f"  ✓ Added timezone to config.yaml: {tz_display}")
 
-    # ── Version 6 → 7: add agents and bindings keys ──
-    if current_ver < 7:
-        config = load_config()
-        changed = False
-        if "agents" not in config:
-            config["agents"] = {}
-            results["config_added"].append("agents={}")
-            changed = True
-        if "bindings" not in config:
-            config["bindings"] = []
-            results["config_added"].append("bindings=[]")
-            changed = True
-        if changed:
-            save_config(config)
-            if not quiet:
-                print("  ✓ Added agents and bindings to config.yaml")
-
     if current_ver < latest_ver and not quiet:
         print(f"Config version: {current_ver} → {latest_ver}")
     
@@ -808,23 +740,6 @@ def _deep_merge(base: dict, override: dict) -> dict:
     return result
 
 
-def _normalize_max_turns_config(config: Dict[str, Any]) -> Dict[str, Any]:
-    """Normalize legacy root-level max_turns into agent.max_turns."""
-    config = dict(config)
-    agent_config = dict(config.get("agent") or {})
-
-    if "max_turns" in config and "max_turns" not in agent_config:
-        agent_config["max_turns"] = config["max_turns"]
-
-    if "max_turns" not in agent_config:
-        agent_config["max_turns"] = DEFAULT_CONFIG["agent"]["max_turns"]
-
-    config["agent"] = agent_config
-    config.pop("max_turns", None)
-    return config
-
-
-
 def load_config() -> Dict[str, Any]:
     """Load configuration from ~/.hermes/config.yaml."""
     import copy
@@ -834,21 +749,14 @@ def load_config() -> Dict[str, Any]:
     
     if config_path.exists():
         try:
-            with open(config_path, encoding="utf-8") as f:
+            with open(config_path) as f:
                 user_config = yaml.safe_load(f) or {}
-
-            if "max_turns" in user_config:
-                agent_user_config = dict(user_config.get("agent") or {})
-                if agent_user_config.get("max_turns") is None:
-                    agent_user_config["max_turns"] = user_config["max_turns"]
-                user_config["agent"] = agent_user_config
-                user_config.pop("max_turns", None)
-
+            
             config = _deep_merge(config, user_config)
         except Exception as e:
             print(f"Warning: Failed to load config: {e}")
     
-    return _normalize_max_turns_config(config)
+    return config
 
 
 _COMMENTED_SECTIONS = """
@@ -883,27 +791,23 @@ _COMMENTED_SECTIONS = """
 
 def save_config(config: Dict[str, Any]):
     """Save configuration to ~/.hermes/config.yaml."""
-    from utils import atomic_yaml_write
-
     ensure_hermes_home()
     config_path = get_config_path()
-    normalized = _normalize_max_turns_config(config)
-
-    # Build optional commented-out sections for features that are off by
-    # default or only relevant when explicitly configured.
-    sections = []
-    sec = normalized.get("security", {})
-    if not sec or sec.get("redact_secrets") is None:
-        sections.append("security")
-    fb = normalized.get("fallback_model", {})
-    if not fb or not (fb.get("provider") and fb.get("model")):
-        sections.append("fallback")
-
-    atomic_yaml_write(
-        config_path,
-        normalized,
-        extra_content=_COMMENTED_SECTIONS if sections else None,
-    )
+    
+    with open(config_path, 'w') as f:
+        yaml.dump(config, f, default_flow_style=False, sort_keys=False)
+        # Append commented-out sections for features that are off by default
+        # or only relevant when explicitly configured. Skip sections the
+        # user has already uncommented and configured.
+        sections = []
+        sec = config.get("security", {})
+        if not sec or sec.get("redact_secrets") is None:
+            sections.append("security")
+        fb = config.get("fallback_model", {})
+        if not fb or not (fb.get("provider") and fb.get("model")):
+            sections.append("fallback")
+        if sections:
+            f.write(_COMMENTED_SECTIONS)
 
 
 def load_env() -> Dict[str, str]:
@@ -957,13 +861,6 @@ def save_env_value(key: str, value: str):
     with open(env_path, 'w', **write_kw) as f:
         f.writelines(lines)
 
-    # Restrict .env permissions to owner-only (contains API keys)
-    if not _IS_WINDOWS:
-        try:
-            os.chmod(env_path, stat.S_IRUSR | stat.S_IWUSR)
-        except OSError:
-            pass
-
 
 def get_env_value(key: str) -> Optional[str]:
     """Get a value from ~/.hermes/.env or environment."""
@@ -1027,7 +924,7 @@ def show_config():
     print()
     print(color("◆ Model", Colors.CYAN, Colors.BOLD))
     print(f"  Model:        {config.get('model', 'not set')}")
-    print(f"  Max turns:    {config.get('agent', {}).get('max_turns', DEFAULT_CONFIG['agent']['max_turns'])}")
+    print(f"  Max turns:    {config.get('max_turns', 100)}")
     print(f"  Toolsets:     {', '.join(config.get('toolsets', ['all']))}")
     
     # Terminal
@@ -1172,7 +1069,7 @@ def set_config_value(key: str, value: str):
     user_config = {}
     if config_path.exists():
         try:
-            with open(config_path, encoding="utf-8") as f:
+            with open(config_path) as f:
                 user_config = yaml.safe_load(f) or {}
         except Exception:
             user_config = {}
@@ -1200,7 +1097,7 @@ def set_config_value(key: str, value: str):
     
     # Write only user config back (not the full merged defaults)
     ensure_hermes_home()
-    with open(config_path, 'w', encoding="utf-8") as f:
+    with open(config_path, 'w') as f:
         yaml.dump(user_config, f, default_flow_style=False, sort_keys=False)
     
     # Keep .env in sync for keys that terminal_tool reads directly from env vars.

hermes_cli/gateway.py

@@ -482,19 +482,14 @@ _PLATFORMS = [
         "token_var": "SLACK_BOT_TOKEN",
         "setup_instructions": [
             "1. Go to https://api.slack.com/apps → Create New App → From Scratch",
-            "2. Enable Socket Mode: Settings → Socket Mode → Enable",
-            "   Create an App-Level Token with scope: connections:write → copy xapp-... token",
-            "3. Add Bot Token Scopes: Features → OAuth & Permissions → Scopes",
-            "   Required: chat:write, app_mentions:read, channels:history, channels:read,",
-            "   groups:history, im:history, im:read, im:write, users:read, files:write",
-            "4. Subscribe to Events: Features → Event Subscriptions → Enable",
-            "   Required events: message.im, message.channels, app_mention",
-            "   Optional: message.groups (for private channels)",
-            "   ⚠ Without message.channels the bot will ONLY work in DMs!",
-            "5. Install to Workspace: Settings → Install App → copy xoxb-... token",
-            "6. Reinstall the app after any scope or event changes",
+            "2. Enable Socket Mode: App Settings → Socket Mode → Enable",
+            "3. Get Bot Token: OAuth & Permissions → Install to Workspace → copy xoxb-... token",
+            "4. Get App Token: Basic Information → App-Level Tokens → Generate",
+            "   Name it anything, add scope: connections:write → copy xapp-... token",
+            "5. Add bot scopes: OAuth & Permissions → Scopes → chat:write, im:history,",
+            "   im:read, im:write, channels:history, channels:read",
+            "6. Reinstall the app to your workspace after adding scopes",
             "7. Find your user ID: click your profile → three dots → Copy member ID",
-            "8. Invite the bot to channels: /invite @YourBot",
         ],
         "vars": [
             {"name": "SLACK_BOT_TOKEN", "prompt": "Bot Token (xoxb-...)", "password": True,

hermes_cli/main.py

@@ -477,10 +477,6 @@ def cmd_chat(args):
     except Exception:
         pass
 
-    # --yolo: bypass all dangerous command approvals
-    if getattr(args, "yolo", False):
-        os.environ["HERMES_YOLO_MODE"] = "1"
-
     # Import and run the CLI
     from cli import main as cli_main
     
@@ -490,12 +486,9 @@ def cmd_chat(args):
         "provider": getattr(args, "provider", None),
         "toolsets": args.toolsets,
         "verbose": args.verbose,
-        "quiet": getattr(args, "quiet", False),
         "query": args.query,
         "resume": getattr(args, "resume", None),
         "worktree": getattr(args, "worktree", False),
-        "checkpoints": getattr(args, "checkpoints", False),
-        "agent": getattr(args, "agent", None),
     }
     # Filter out None values
     kwargs = {k: v for k, v in kwargs.items() if v is not None}
@@ -768,39 +761,9 @@ def cmd_model(args):
         ("kimi-coding", "Kimi / Moonshot (Moonshot AI direct API)"),
         ("minimax", "MiniMax (global direct API)"),
         ("minimax-cn", "MiniMax China (domestic direct API)"),
+        ("custom", "Custom endpoint (self-hosted / VLLM / etc.)"),
     ]
 
-    # Add user-defined custom providers from config.yaml
-    custom_providers_cfg = config.get("custom_providers") or []
-    _custom_provider_map = {}  # key → {name, base_url, api_key}
-    if isinstance(custom_providers_cfg, list):
-        for entry in custom_providers_cfg:
-            if not isinstance(entry, dict):
-                continue
-            name = entry.get("name", "").strip()
-            base_url = entry.get("base_url", "").strip()
-            if not name or not base_url:
-                continue
-            # Generate a stable key from the name
-            key = "custom:" + name.lower().replace(" ", "-")
-            short_url = base_url.replace("https://", "").replace("http://", "").rstrip("/")
-            saved_model = entry.get("model", "")
-            model_hint = f" — {saved_model}" if saved_model else ""
-            providers.append((key, f"{name} ({short_url}){model_hint}"))
-            _custom_provider_map[key] = {
-                "name": name,
-                "base_url": base_url,
-                "api_key": entry.get("api_key", ""),
-                "model": saved_model,
-            }
-
-    # Always add the manual custom endpoint option last
-    providers.append(("custom", "Custom endpoint (enter URL manually)"))
-
-    # Add removal option if there are saved custom providers
-    if _custom_provider_map:
-        providers.append(("remove-custom", "Remove a saved custom provider"))
-
     # Reorder so the active provider is at the top
     known_keys = {k for k, _ in providers}
     active_key = active if active in known_keys else "custom"
@@ -828,10 +791,6 @@ def cmd_model(args):
         _model_flow_openai_codex(config, current_model)
     elif selected_provider == "custom":
         _model_flow_custom(config)
-    elif selected_provider.startswith("custom:") and selected_provider in _custom_provider_map:
-        _model_flow_named_custom(config, _custom_provider_map[selected_provider])
-    elif selected_provider == "remove-custom":
-        _remove_custom_provider(config)
     elif selected_provider in ("zai", "kimi-coding", "minimax", "minimax-cn"):
         _model_flow_api_key_provider(config, selected_provider, current_model)
 
@@ -912,11 +871,9 @@ def _model_flow_openrouter(config, current_model=""):
         from hermes_cli.config import load_config, save_config
         cfg = load_config()
         model = cfg.get("model")
-        if not isinstance(model, dict):
-            model = {"default": model} if model else {}
-            cfg["model"] = model
-        model["provider"] = "openrouter"
-        model["base_url"] = OPENROUTER_BASE_URL
+        if isinstance(model, dict):
+            model["provider"] = "openrouter"
+            model["base_url"] = OPENROUTER_BASE_URL
         save_config(cfg)
         deactivate_provider()
         print(f"Default model set to: {selected} (via OpenRouter)")
@@ -1049,11 +1006,7 @@ def _model_flow_openai_codex(config, current_model=""):
 
 
 def _model_flow_custom(config):
-    """Custom endpoint: collect URL, API key, and model name.
-
-    Automatically saves the endpoint to ``custom_providers`` in config.yaml
-    so it appears in the provider menu on subsequent runs.
-    """
+    """Custom endpoint: collect URL, API key, and model name."""
     from hermes_cli.auth import _save_model_choice, deactivate_provider
     from hermes_cli.config import get_env_value, save_env_value, load_config, save_config
 
@@ -1085,8 +1038,6 @@ def _model_flow_custom(config):
         print(f"Invalid URL: {effective_url} (must start with http:// or https://)")
         return
 
-    effective_key = api_key or current_key
-
     if base_url:
         save_env_value("OPENAI_BASE_URL", base_url)
     if api_key:
@@ -1098,11 +1049,9 @@ def _model_flow_custom(config):
         # Update config and deactivate any OAuth provider
         cfg = load_config()
         model = cfg.get("model")
-        if not isinstance(model, dict):
-            model = {"default": model} if model else {}
-            cfg["model"] = model
-        model["provider"] = "custom"
-        model["base_url"] = effective_url
+        if isinstance(model, dict):
+            model["provider"] = "auto"
+            model["base_url"] = effective_url
         save_config(cfg)
         deactivate_provider()
 
@@ -1112,227 +1061,6 @@ def _model_flow_custom(config):
             deactivate_provider()
         print("Endpoint saved. Use `/model` in chat or `hermes model` to set a model.")
 
-    # Auto-save to custom_providers so it appears in the menu next time
-    _save_custom_provider(effective_url, effective_key, model_name or "")
-
-
-def _save_custom_provider(base_url, api_key="", model=""):
-    """Save a custom endpoint to custom_providers in config.yaml.
-
-    Deduplicates by base_url — if the URL already exists, updates the
-    model name but doesn't add a duplicate entry.
-    Auto-generates a display name from the URL hostname.
-    """
-    from hermes_cli.config import load_config, save_config
-
-    cfg = load_config()
-    providers = cfg.get("custom_providers") or []
-    if not isinstance(providers, list):
-        providers = []
-
-    # Check if this URL is already saved — update model if so
-    for entry in providers:
-        if isinstance(entry, dict) and entry.get("base_url", "").rstrip("/") == base_url.rstrip("/"):
-            if model and entry.get("model") != model:
-                entry["model"] = model
-                cfg["custom_providers"] = providers
-                save_config(cfg)
-            return  # already saved, updated model if needed
-
-    # Auto-generate a name from the URL
-    import re
-    clean = base_url.replace("https://", "").replace("http://", "").rstrip("/")
-    # Remove /v1 suffix for cleaner names
-    clean = re.sub(r"/v1/?$", "", clean)
-    # Use hostname:port as the name
-    name = clean.split("/")[0]
-    # Capitalize for readability
-    if "localhost" in name or "127.0.0.1" in name:
-        name = f"Local ({name})"
-    elif "runpod" in name.lower():
-        name = f"RunPod ({name})"
-    else:
-        name = name.capitalize()
-
-    entry = {"name": name, "base_url": base_url}
-    if api_key:
-        entry["api_key"] = api_key
-    if model:
-        entry["model"] = model
-
-    providers.append(entry)
-    cfg["custom_providers"] = providers
-    save_config(cfg)
-    print(f"  💾 Saved to custom providers as \"{name}\" (edit in config.yaml)")
-
-
-def _remove_custom_provider(config):
-    """Let the user remove a saved custom provider from config.yaml."""
-    from hermes_cli.config import load_config, save_config
-
-    cfg = load_config()
-    providers = cfg.get("custom_providers") or []
-    if not isinstance(providers, list) or not providers:
-        print("No custom providers configured.")
-        return
-
-    print("Remove a custom provider:\n")
-
-    choices = []
-    for entry in providers:
-        if isinstance(entry, dict):
-            name = entry.get("name", "unnamed")
-            url = entry.get("base_url", "")
-            short_url = url.replace("https://", "").replace("http://", "").rstrip("/")
-            choices.append(f"{name} ({short_url})")
-        else:
-            choices.append(str(entry))
-    choices.append("Cancel")
-
-    try:
-        from simple_term_menu import TerminalMenu
-        menu = TerminalMenu(
-            [f"  {c}" for c in choices], cursor_index=0,
-            menu_cursor="-> ", menu_cursor_style=("fg_red", "bold"),
-            menu_highlight_style=("fg_red",),
-            cycle_cursor=True, clear_screen=False,
-            title="Select provider to remove:",
-        )
-        idx = menu.show()
-        print()
-    except (ImportError, NotImplementedError):
-        for i, c in enumerate(choices, 1):
-            print(f"  {i}. {c}")
-        print()
-        try:
-            val = input(f"Choice [1-{len(choices)}]: ").strip()
-            idx = int(val) - 1 if val else None
-        except (ValueError, KeyboardInterrupt, EOFError):
-            idx = None
-
-    if idx is None or idx >= len(providers):
-        print("No change.")
-        return
-
-    removed = providers.pop(idx)
-    cfg["custom_providers"] = providers
-    save_config(cfg)
-    removed_name = removed.get("name", "unnamed") if isinstance(removed, dict) else str(removed)
-    print(f"✅ Removed \"{removed_name}\" from custom providers.")
-
-
-def _model_flow_named_custom(config, provider_info):
-    """Handle a named custom provider from config.yaml custom_providers list.
-
-    If the entry has a saved model name, activates it immediately.
-    Otherwise probes the endpoint's /models API to let the user pick one.
-    """
-    from hermes_cli.auth import _save_model_choice, deactivate_provider
-    from hermes_cli.config import save_env_value, load_config, save_config
-    from hermes_cli.models import fetch_api_models
-
-    name = provider_info["name"]
-    base_url = provider_info["base_url"]
-    api_key = provider_info.get("api_key", "")
-    saved_model = provider_info.get("model", "")
-
-    # If a model is saved, just activate immediately — no probing needed
-    if saved_model:
-        save_env_value("OPENAI_BASE_URL", base_url)
-        if api_key:
-            save_env_value("OPENAI_API_KEY", api_key)
-        _save_model_choice(saved_model)
-
-        cfg = load_config()
-        model = cfg.get("model")
-        if not isinstance(model, dict):
-            model = {"default": model} if model else {}
-            cfg["model"] = model
-        model["provider"] = "custom"
-        model["base_url"] = base_url
-        save_config(cfg)
-        deactivate_provider()
-
-        print(f"✅ Switched to: {saved_model}")
-        print(f"   Provider: {name} ({base_url})")
-        return
-
-    # No saved model — probe endpoint and let user pick
-    print(f"  Provider: {name}")
-    print(f"  URL:      {base_url}")
-    print()
-    print("No model saved for this provider. Fetching available models...")
-    models = fetch_api_models(api_key, base_url, timeout=8.0)
-
-    if models:
-        print(f"Found {len(models)} model(s):\n")
-        try:
-            from simple_term_menu import TerminalMenu
-            menu_items = [f"  {m}" for m in models] + ["  Cancel"]
-            menu = TerminalMenu(
-                menu_items, cursor_index=0,
-                menu_cursor="-> ", menu_cursor_style=("fg_green", "bold"),
-                menu_highlight_style=("fg_green",),
-                cycle_cursor=True, clear_screen=False,
-                title=f"Select model from {name}:",
-            )
-            idx = menu.show()
-            print()
-            if idx is None or idx >= len(models):
-                print("Cancelled.")
-                return
-            model_name = models[idx]
-        except (ImportError, NotImplementedError):
-            for i, m in enumerate(models, 1):
-                print(f"  {i}. {m}")
-            print(f"  {len(models) + 1}. Cancel")
-            print()
-            try:
-                val = input(f"Choice [1-{len(models) + 1}]: ").strip()
-                if not val:
-                    print("Cancelled.")
-                    return
-                idx = int(val) - 1
-                if idx < 0 or idx >= len(models):
-                    print("Cancelled.")
-                    return
-                model_name = models[idx]
-            except (ValueError, KeyboardInterrupt, EOFError):
-                print("\nCancelled.")
-                return
-    else:
-        print("Could not fetch models from endpoint. Enter model name manually.")
-        try:
-            model_name = input("Model name: ").strip()
-        except (KeyboardInterrupt, EOFError):
-            print("\nCancelled.")
-            return
-        if not model_name:
-            print("No model specified. Cancelled.")
-            return
-
-    # Activate and save the model to the custom_providers entry
-    save_env_value("OPENAI_BASE_URL", base_url)
-    if api_key:
-        save_env_value("OPENAI_API_KEY", api_key)
-    _save_model_choice(model_name)
-
-    cfg = load_config()
-    model = cfg.get("model")
-    if not isinstance(model, dict):
-        model = {"default": model} if model else {}
-        cfg["model"] = model
-    model["provider"] = "custom"
-    model["base_url"] = base_url
-    save_config(cfg)
-    deactivate_provider()
-
-    # Save model name to the custom_providers entry for next time
-    _save_custom_provider(base_url, api_key, model_name)
-
-    print(f"\n✅ Model set to: {model_name}")
-    print(f"   Provider: {name} ({base_url})")
-
 
 # Curated model lists for direct API-key providers
 _PROVIDER_MODELS = {
@@ -1434,11 +1162,9 @@ def _model_flow_api_key_provider(config, provider_id, current_model=""):
         # Update config with provider and base URL
         cfg = load_config()
         model = cfg.get("model")
-        if not isinstance(model, dict):
-            model = {"default": model} if model else {}
-            cfg["model"] = model
-        model["provider"] = provider_id
-        model["base_url"] = effective_base
+        if isinstance(model, dict):
+            model["provider"] = provider_id
+            model["base_url"] = effective_base
         save_config(cfg)
         deactivate_provider()
 
@@ -1794,44 +1520,6 @@ def cmd_update(args):
             sys.exit(1)
 
 
-def _coalesce_session_name_args(argv: list) -> list:
-    """Join unquoted multi-word session names after -c/--continue and -r/--resume.
-
-    When a user types ``hermes -c Pokemon Agent Dev`` without quoting the
-    session name, argparse sees three separate tokens.  This function merges
-    them into a single argument so argparse receives
-    ``['-c', 'Pokemon Agent Dev']`` instead.
-
-    Tokens are collected after the flag until we hit another flag (``-*``)
-    or a known top-level subcommand.
-    """
-    _SUBCOMMANDS = {
-        "chat", "model", "gateway", "setup", "whatsapp", "login", "logout",
-        "status", "cron", "doctor", "config", "pairing", "skills", "tools",
-        "sessions", "insights", "version", "update", "uninstall",
-    }
-    _SESSION_FLAGS = {"-c", "--continue", "-r", "--resume"}
-
-    result = []
-    i = 0
-    while i < len(argv):
-        token = argv[i]
-        if token in _SESSION_FLAGS:
-            result.append(token)
-            i += 1
-            # Collect subsequent non-flag, non-subcommand tokens as one name
-            parts: list = []
-            while i < len(argv) and not argv[i].startswith("-") and argv[i] not in _SUBCOMMANDS:
-                parts.append(argv[i])
-                i += 1
-            if parts:
-                result.append(" ".join(parts))
-        else:
-            result.append(token)
-            i += 1
-    return result
-
-
 def main():
     """Main entry point for hermes CLI."""
     parser = argparse.ArgumentParser(
@@ -1890,12 +1578,6 @@ For more help on a command:
         default=False,
         help="Run in an isolated git worktree (for parallel agents)"
     )
-    parser.add_argument(
-        "--yolo",
-        action="store_true",
-        default=False,
-        help="Bypass all dangerous command approval prompts (use at your own risk)"
-    )
     
     subparsers = parser.add_subparsers(dest="command", help="Command to run")
     
@@ -1930,11 +1612,6 @@ For more help on a command:
         action="store_true",
         help="Verbose output"
     )
-    chat_parser.add_argument(
-        "-Q", "--quiet",
-        action="store_true",
-        help="Quiet mode for programmatic use: suppress banner, spinner, and tool previews. Only output the final response and session info."
-    )
     chat_parser.add_argument(
         "--resume", "-r",
         metavar="SESSION_ID",
@@ -1955,24 +1632,6 @@ For more help on a command:
         default=False,
         help="Run in an isolated git worktree (for parallel agents on the same repo)"
     )
-    chat_parser.add_argument(
-        "--checkpoints",
-        action="store_true",
-        default=False,
-        help="Enable filesystem checkpoints before destructive file operations (use /rollback to restore)"
-    )
-    chat_parser.add_argument(
-        "--yolo",
-        action="store_true",
-        default=False,
-        help="Bypass all dangerous command approval prompts (use at your own risk)"
-    )
-    chat_parser.add_argument(
-        "--agent",
-        type=str,
-        default=None,
-        help="Named agent profile to use from config.yaml"
-    )
     chat_parser.set_defaults(func=cmd_chat)
 
     # =========================================================================
@@ -2440,12 +2099,12 @@ For more help on a command:
                 if not data:
                     print(f"Session '{args.session_id}' not found.")
                     return
-                with open(args.output, "w", encoding="utf-8") as f:
+                with open(args.output, "w") as f:
                     f.write(_json.dumps(data, ensure_ascii=False) + "\n")
                 print(f"Exported 1 session to {args.output}")
             else:
                 sessions = db.export_all(source=args.source)
-                with open(args.output, "w", encoding="utf-8") as f:
+                with open(args.output, "w") as f:
                     for s in sessions:
                         f.write(_json.dumps(s, ensure_ascii=False) + "\n")
                 print(f"Exported {len(sessions)} sessions to {args.output}")
@@ -2599,11 +2258,7 @@ For more help on a command:
     # =========================================================================
     # Parse and execute
     # =========================================================================
-    # Pre-process argv so unquoted multi-word session names after -c / -r
-    # are merged into a single token before argparse sees them.
-    # e.g. ``hermes -c Pokemon Agent Dev`` → ``hermes -c 'Pokemon Agent Dev'``
-    _processed_argv = _coalesce_session_name_args(sys.argv[1:])
-    args = parser.parse_args(_processed_argv)
+    args = parser.parse_args()
     
     # Handle --version flag
     if args.version:

hermes_cli/models.py

@@ -63,7 +63,7 @@ _PROVIDER_LABELS = {
     "kimi-coding": "Kimi / Moonshot",
     "minimax": "MiniMax",
     "minimax-cn": "MiniMax (China)",
-    "custom": "Custom endpoint",
+    "custom": "custom endpoint",
 }
 
 _PROVIDER_ALIASES = {

hermes_cli/runtime_provider.py

@@ -66,14 +66,9 @@ def _resolve_openrouter_runtime(
             if not cfg_provider or cfg_provider == "auto":
                 use_config_base_url = True
 
-    # When the user explicitly requested the openrouter provider, skip
-    # OPENAI_BASE_URL — it typically points to a custom / non-OpenRouter
-    # endpoint and would prevent switching back to OpenRouter (#874).
-    skip_openai_base = requested_norm == "openrouter"
-
     base_url = (
         (explicit_base_url or "").strip()
-        or ("" if skip_openai_base else env_openai_base_url)
+        or env_openai_base_url
         or (cfg_base_url.strip() if use_config_base_url else "")
         or env_openrouter_base_url
         or OPENROUTER_BASE_URL

hermes_cli/setup.py

@@ -516,8 +516,7 @@ def setup_model_provider(config: dict):
         keep_label = None  # No provider configured — don't show "Keep current"
 
     provider_choices = [
-        "Nous Portal API key (direct API key access)",
-        "Login with Nous Portal (Nous Research subscription — OAuth)",
+        "Login with Nous Portal (Nous Research subscription)",
         "Login with OpenAI Codex",
         "OpenRouter API key (100+ models, pay-per-use)",
         "Custom OpenAI-compatible endpoint (self-hosted / VLLM / etc.)",
@@ -530,7 +529,7 @@ def setup_model_provider(config: dict):
         provider_choices.append(keep_label)
     
     # Default to "Keep current" if a provider exists, otherwise OpenRouter (most common)
-    default_provider = len(provider_choices) - 1 if has_any_provider else 3
+    default_provider = len(provider_choices) - 1 if has_any_provider else 2
     
     if not has_any_provider:
         print_warning("An inference provider is required for Hermes to work.")
@@ -542,37 +541,7 @@ def setup_model_provider(config: dict):
     selected_provider = None  # "nous", "openai-codex", "openrouter", "custom", or None (keep)
     nous_models = []  # populated if Nous login succeeds
 
-    if provider_idx == 0:  # Nous Portal API Key (direct)
-        selected_provider = "nous-api"
-        print()
-        print_header("Nous Portal API Key")
-        print_info("Use a Nous Portal API key for direct access to Nous inference.")
-        print_info("Get your API key at: https://portal.nousresearch.com")
-        print()
-
-        existing_key = get_env_value("NOUS_API_KEY")
-        if existing_key:
-            print_info(f"Current: {existing_key[:8]}... (configured)")
-            if prompt_yes_no("Update Nous API key?", False):
-                api_key = prompt("  Nous API key", password=True)
-                if api_key:
-                    save_env_value("NOUS_API_KEY", api_key)
-                    print_success("Nous API key updated")
-        else:
-            api_key = prompt("  Nous API key", password=True)
-            if api_key:
-                save_env_value("NOUS_API_KEY", api_key)
-                print_success("Nous API key saved")
-            else:
-                print_warning("Skipped - agent won't work without an API key")
-
-        # Clear custom endpoint vars if switching
-        if existing_custom:
-            save_env_value("OPENAI_BASE_URL", "")
-            save_env_value("OPENAI_API_KEY", "")
-        _update_config_for_provider("nous-api", "https://inference-api.nousresearch.com/v1")
-
-    elif provider_idx == 1:  # Nous Portal
+    if provider_idx == 0:  # Nous Portal
         selected_provider = "nous"
         print()
         print_header("Nous Portal Login")
@@ -612,7 +581,7 @@ def setup_model_provider(config: dict):
             print_info("You can try again later with: hermes model")
             selected_provider = None
 
-    elif provider_idx == 2:  # OpenAI Codex
+    elif provider_idx == 1:  # OpenAI Codex
         selected_provider = "openai-codex"
         print()
         print_header("OpenAI Codex Login")
@@ -636,7 +605,7 @@ def setup_model_provider(config: dict):
             print_info("You can try again later with: hermes model")
             selected_provider = None
 
-    elif provider_idx == 3:  # OpenRouter
+    elif provider_idx == 2:  # OpenRouter
         selected_provider = "openrouter"
         print()
         print_header("OpenRouter API Key")
@@ -663,30 +632,7 @@ def setup_model_provider(config: dict):
             save_env_value("OPENAI_BASE_URL", "")
             save_env_value("OPENAI_API_KEY", "")
 
-        # Update config.yaml and deactivate any OAuth provider so the
-        # resolver doesn't keep returning the old provider (e.g. Codex).
-        try:
-            from hermes_cli.auth import deactivate_provider
-            deactivate_provider()
-        except Exception:
-            pass
-        import yaml
-        config_path = Path(os.environ.get("HERMES_HOME", Path.home() / ".hermes")) / "config.yaml"
-        try:
-            disk_cfg = {}
-            if config_path.exists():
-                disk_cfg = yaml.safe_load(config_path.read_text()) or {}
-            model_section = disk_cfg.get("model", {})
-            if isinstance(model_section, str):
-                model_section = {"default": model_section}
-            model_section["provider"] = "openrouter"
-            model_section.pop("base_url", None)  # OpenRouter uses default URL
-            disk_cfg["model"] = model_section
-            config_path.write_text(yaml.safe_dump(disk_cfg, sort_keys=False))
-        except Exception as e:
-            logger.debug("Could not save provider to config.yaml: %s", e)
-
-    elif provider_idx == 4:  # Custom endpoint
+    elif provider_idx == 3:  # Custom endpoint
         selected_provider = "custom"
         print()
         print_header("Custom OpenAI-Compatible Endpoint")
@@ -713,31 +659,9 @@ def setup_model_provider(config: dict):
         if model_name:
             config['model'] = model_name
             save_env_value("LLM_MODEL", model_name)
-
-        # Save provider and base_url to config.yaml so the gateway and CLI
-        # both resolve the correct provider without relying on env-var heuristics.
-        if base_url:
-            import yaml
-            config_path = Path(os.environ.get("HERMES_HOME", Path.home() / ".hermes")) / "config.yaml"
-            try:
-                disk_cfg = {}
-                if config_path.exists():
-                    disk_cfg = yaml.safe_load(config_path.read_text()) or {}
-                model_section = disk_cfg.get("model", {})
-                if isinstance(model_section, str):
-                    model_section = {"default": model_section}
-                model_section["provider"] = "custom"
-                model_section["base_url"] = base_url.rstrip("/")
-                if model_name:
-                    model_section["default"] = model_name
-                disk_cfg["model"] = model_section
-                config_path.write_text(yaml.safe_dump(disk_cfg, sort_keys=False))
-            except Exception as e:
-                logger.debug("Could not save provider to config.yaml: %s", e)
-
         print_success("Custom endpoint configured")
 
-    elif provider_idx == 5:  # Z.AI / GLM
+    elif provider_idx == 4:  # Z.AI / GLM
         selected_provider = "zai"
         print()
         print_header("Z.AI / GLM API Key")
@@ -791,7 +715,7 @@ def setup_model_provider(config: dict):
             save_env_value("OPENAI_API_KEY", "")
         _update_config_for_provider("zai", zai_base_url)
 
-    elif provider_idx == 6:  # Kimi / Moonshot
+    elif provider_idx == 5:  # Kimi / Moonshot
         selected_provider = "kimi-coding"
         print()
         print_header("Kimi / Moonshot API Key")
@@ -823,7 +747,7 @@ def setup_model_provider(config: dict):
             save_env_value("OPENAI_API_KEY", "")
         _update_config_for_provider("kimi-coding", pconfig.inference_base_url)
 
-    elif provider_idx == 7:  # MiniMax
+    elif provider_idx == 6:  # MiniMax
         selected_provider = "minimax"
         print()
         print_header("MiniMax API Key")
@@ -855,7 +779,7 @@ def setup_model_provider(config: dict):
             save_env_value("OPENAI_API_KEY", "")
         _update_config_for_provider("minimax", pconfig.inference_base_url)
 
-    elif provider_idx == 8:  # MiniMax China
+    elif provider_idx == 7:  # MiniMax China
         selected_provider = "minimax-cn"
         print()
         print_header("MiniMax China API Key")
@@ -887,12 +811,12 @@ def setup_model_provider(config: dict):
             save_env_value("OPENAI_API_KEY", "")
         _update_config_for_provider("minimax-cn", pconfig.inference_base_url)
 
-    # else: provider_idx == 9 (Keep current) — only shown when a provider already exists
+    # else: provider_idx == 8 (Keep current) — only shown when a provider already exists
 
     # ── OpenRouter API Key for tools (if not already set) ──
     # Tools (vision, web, MoA) use OpenRouter independently of the main provider.
     # Prompt for OpenRouter key if not set and a non-OpenRouter provider was chosen.
-    if selected_provider in ("nous", "nous-api", "openai-codex", "custom", "zai", "kimi-coding", "minimax", "minimax-cn") and not get_env_value("OPENROUTER_API_KEY"):
+    if selected_provider in ("nous", "openai-codex", "custom", "zai", "kimi-coding", "minimax", "minimax-cn") and not get_env_value("OPENROUTER_API_KEY"):
         print()
         print_header("OpenRouter API Key (for tools)")
         print_info("Tools like vision analysis, web search, and MoA use OpenRouter")
@@ -945,14 +869,6 @@ def setup_model_provider(config: dict):
             if custom:
                 config['model'] = custom
                 save_env_value("LLM_MODEL", custom)
-        elif selected_provider == "nous-api":
-            # Nous API key provider — prompt for model manually
-            print_info("Enter a model name available on Nous inference API.")
-            print_info("Examples: anthropic/claude-opus-4.6, deepseek/deepseek-r1")
-            custom = prompt(f"  Model name (Enter to keep '{current_model}')")
-            if custom:
-                config['model'] = custom
-                save_env_value("LLM_MODEL", custom)
         elif selected_provider == "openai-codex":
             from hermes_cli.codex_models import get_codex_model_ids
             codex_models = get_codex_model_ids()
@@ -1348,7 +1264,7 @@ def setup_agent_settings(config: dict):
     # ── Max Iterations ──
     print_header("Agent Settings")
 
-    current_max = get_env_value('HERMES_MAX_ITERATIONS') or str(config.get('agent', {}).get('max_turns', 90))
+    current_max = get_env_value('HERMES_MAX_ITERATIONS') or '90'
     print_info("Maximum tool-calling iterations per conversation.")
     print_info("Higher = more complex tasks, but costs more tokens.")
     print_info("Recommended: 30-60 for most tasks, 100+ for open exploration.")
@@ -1358,8 +1274,7 @@ def setup_agent_settings(config: dict):
         max_iter = int(max_iter_str)
         if max_iter > 0:
             save_env_value("HERMES_MAX_ITERATIONS", str(max_iter))
-            config.setdefault('agent', {})['max_turns'] = max_iter
-            config.pop('max_turns', None)
+            config['max_turns'] = max_iter
             print_success(f"Max iterations set to {max_iter}")
     except ValueError:
         print_warning("Invalid number, keeping current value")
@@ -1612,22 +1527,10 @@ def setup_gateway(config: dict):
     
     if not existing_slack and prompt_yes_no("Set up Slack bot?", False):
         print_info("Steps to create a Slack app:")
-        print_info("   1. Go to https://api.slack.com/apps → Create New App (from scratch)")
-        print_info("   2. Enable Socket Mode: Settings → Socket Mode → Enable")
-        print_info("      • Create an App-Level Token with 'connections:write' scope")
-        print_info("   3. Add Bot Token Scopes: Features → OAuth & Permissions")
-        print_info("      Required scopes: chat:write, app_mentions:read,")
-        print_info("      channels:history, channels:read, groups:history,")
-        print_info("      im:history, im:read, im:write, users:read, files:write")
-        print_info("   4. Subscribe to Events: Features → Event Subscriptions → Enable")
-        print_info("      Required events: message.im, message.channels,")
-        print_info("      message.groups, app_mention")
-        print_warning("   ⚠ Without message.channels/message.groups events,")
-        print_warning("     the bot will ONLY work in DMs, not channels!")
-        print_info("   5. Install to Workspace: Settings → Install App")
-        print_info("   6. After installing, invite the bot to channels: /invite @YourBot")
-        print()
-        print_info("   Full guide: https://hermes-agent.ai/docs/user-guide/messaging/slack")
+        print_info("   1. Go to https://api.slack.com/apps → Create New App")
+        print_info("   2. Enable Socket Mode: App Settings → Socket Mode → Enable")
+        print_info("   3. Bot Token: OAuth & Permissions → Install to Workspace")
+        print_info("   4. App Token: Basic Information → App-Level Tokens → Generate")
         print()
         bot_token = prompt("Slack Bot Token (xoxb-...)", password=True)
         if bot_token:
@@ -1639,7 +1542,7 @@ def setup_gateway(config: dict):
             
             print()
             print_info("🔒 Security: Restrict who can use your bot")
-            print_info("   To find a Member ID: click a user's name → View full profile → ⋮ → Copy member ID")
+            print_info("   Find Slack user IDs in your profile or via the Slack API")
             print()
             allowed_users = prompt("Allowed user IDs (comma-separated, leave empty for open access)")
             if allowed_users:

hermes_cli/skin_engine.py

@@ -1,630 +0,0 @@
-"""Hermes CLI skin/theme engine.
-
-A data-driven skin system that lets users customize the CLI's visual appearance.
-Skins are defined as YAML files in ~/.hermes/skins/ or as built-in presets.
-No code changes are needed to add a new skin.
-
-SKIN YAML SCHEMA
-================
-
-All fields are optional. Missing values inherit from the ``default`` skin.
-
-.. code-block:: yaml
-
-    # Required: skin identity
-    name: mytheme                         # Unique skin name (lowercase, hyphens ok)
-    description: Short description        # Shown in /skin listing
-
-    # Colors: hex values for Rich markup (banner, UI, response box)
-    colors:
-      banner_border: "#CD7F32"            # Panel border color
-      banner_title: "#FFD700"             # Panel title text color
-      banner_accent: "#FFBF00"            # Section headers (Available Tools, etc.)
-      banner_dim: "#B8860B"               # Dim/muted text (separators, labels)
-      banner_text: "#FFF8DC"              # Body text (tool names, skill names)
-      ui_accent: "#FFBF00"               # General UI accent
-      ui_label: "#4dd0e1"                # UI labels
-      ui_ok: "#4caf50"                   # Success indicators
-      ui_error: "#ef5350"                # Error indicators
-      ui_warn: "#ffa726"                 # Warning indicators
-      prompt: "#FFF8DC"                  # Prompt text color
-      input_rule: "#CD7F32"              # Input area horizontal rule
-      response_border: "#FFD700"         # Response box border (ANSI)
-      session_label: "#DAA520"           # Session label color
-      session_border: "#8B8682"          # Session ID dim color
-
-    # Spinner: customize the animated spinner during API calls
-    spinner:
-      waiting_faces:                      # Faces shown while waiting for API
-        - "(⚔)"
-        - "(⛨)"
-      thinking_faces:                     # Faces shown during reasoning
-        - "(⌁)"
-        - "(<>)"
-      thinking_verbs:                     # Verbs for spinner messages
-        - "forging"
-        - "plotting"
-      wings:                              # Optional left/right spinner decorations
-        - ["⟪⚔", "⚔⟫"]                  # Each entry is [left, right] pair
-        - ["⟪▲", "▲⟫"]
-
-    # Branding: text strings used throughout the CLI
-    branding:
-      agent_name: "Hermes Agent"          # Banner title, status display
-      welcome: "Welcome message"          # Shown at CLI startup
-      goodbye: "Goodbye! ⚕"              # Shown on exit
-      response_label: " ⚕ Hermes "       # Response box header label
-      prompt_symbol: "❯ "                # Input prompt symbol
-      help_header: "(^_^)? Commands"      # /help header text
-
-    # Tool prefix: character for tool output lines (default: ┊)
-    tool_prefix: "┊"
-
-USAGE
-=====
-
-.. code-block:: python
-
-    from hermes_cli.skin_engine import get_active_skin, list_skins, set_active_skin
-
-    skin = get_active_skin()
-    print(skin.colors["banner_title"])    # "#FFD700"
-    print(skin.get_branding("agent_name"))  # "Hermes Agent"
-
-    set_active_skin("ares")               # Switch to built-in ares skin
-    set_active_skin("mytheme")            # Switch to user skin from ~/.hermes/skins/
-
-BUILT-IN SKINS
-==============
-
-- ``default`` — Classic Hermes gold/kawaii (the current look)
-- ``ares``    — Crimson/bronze war-god theme with custom spinner wings
-- ``mono``    — Clean grayscale monochrome
-- ``slate``   — Cool blue developer-focused theme
-
-USER SKINS
-==========
-
-Drop a YAML file in ``~/.hermes/skins/<name>.yaml`` following the schema above.
-Activate with ``/skin <name>`` in the CLI or ``display.skin: <name>`` in config.yaml.
-"""
-
-import logging
-import os
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple
-
-logger = logging.getLogger(__name__)
-
-
-# =============================================================================
-# Skin data structure
-# =============================================================================
-
-@dataclass
-class SkinConfig:
-    """Complete skin configuration."""
-    name: str
-    description: str = ""
-    colors: Dict[str, str] = field(default_factory=dict)
-    spinner: Dict[str, Any] = field(default_factory=dict)
-    branding: Dict[str, str] = field(default_factory=dict)
-    tool_prefix: str = "┊"
-    banner_logo: str = ""    # Rich-markup ASCII art logo (replaces HERMES_AGENT_LOGO)
-    banner_hero: str = ""    # Rich-markup hero art (replaces HERMES_CADUCEUS)
-
-    def get_color(self, key: str, fallback: str = "") -> str:
-        """Get a color value with fallback."""
-        return self.colors.get(key, fallback)
-
-    def get_spinner_list(self, key: str) -> List[str]:
-        """Get a spinner list (faces, verbs, etc.)."""
-        return self.spinner.get(key, [])
-
-    def get_spinner_wings(self) -> List[Tuple[str, str]]:
-        """Get spinner wing pairs, or empty list if none."""
-        raw = self.spinner.get("wings", [])
-        result = []
-        for pair in raw:
-            if isinstance(pair, (list, tuple)) and len(pair) == 2:
-                result.append((str(pair[0]), str(pair[1])))
-        return result
-
-    def get_branding(self, key: str, fallback: str = "") -> str:
-        """Get a branding value with fallback."""
-        return self.branding.get(key, fallback)
-
-
-# =============================================================================
-# Built-in skin definitions
-# =============================================================================
-
-_BUILTIN_SKINS: Dict[str, Dict[str, Any]] = {
-    "default": {
-        "name": "default",
-        "description": "Classic Hermes — gold and kawaii",
-        "colors": {
-            "banner_border": "#CD7F32",
-            "banner_title": "#FFD700",
-            "banner_accent": "#FFBF00",
-            "banner_dim": "#B8860B",
-            "banner_text": "#FFF8DC",
-            "ui_accent": "#FFBF00",
-            "ui_label": "#4dd0e1",
-            "ui_ok": "#4caf50",
-            "ui_error": "#ef5350",
-            "ui_warn": "#ffa726",
-            "prompt": "#FFF8DC",
-            "input_rule": "#CD7F32",
-            "response_border": "#FFD700",
-            "session_label": "#DAA520",
-            "session_border": "#8B8682",
-        },
-        "spinner": {
-            # Empty = use hardcoded defaults in display.py
-        },
-        "branding": {
-            "agent_name": "Hermes Agent",
-            "welcome": "Welcome to Hermes Agent! Type your message or /help for commands.",
-            "goodbye": "Goodbye! ⚕",
-            "response_label": " ⚕ Hermes ",
-            "prompt_symbol": "❯ ",
-            "help_header": "(^_^)? Available Commands",
-        },
-        "tool_prefix": "┊",
-    },
-    "ares": {
-        "name": "ares",
-        "description": "War-god theme — crimson and bronze",
-        "colors": {
-            "banner_border": "#9F1C1C",
-            "banner_title": "#C7A96B",
-            "banner_accent": "#DD4A3A",
-            "banner_dim": "#6B1717",
-            "banner_text": "#F1E6CF",
-            "ui_accent": "#DD4A3A",
-            "ui_label": "#C7A96B",
-            "ui_ok": "#4caf50",
-            "ui_error": "#ef5350",
-            "ui_warn": "#ffa726",
-            "prompt": "#F1E6CF",
-            "input_rule": "#9F1C1C",
-            "response_border": "#C7A96B",
-            "session_label": "#C7A96B",
-            "session_border": "#6E584B",
-        },
-        "spinner": {
-            "waiting_faces": ["(⚔)", "(⛨)", "(▲)", "(<>)", "(/)"],
-            "thinking_faces": ["(⚔)", "(⛨)", "(▲)", "(⌁)", "(<>)"],
-            "thinking_verbs": [
-                "forging", "marching", "sizing the field", "holding the line",
-                "hammering plans", "tempering steel", "plotting impact", "raising the shield",
-            ],
-            "wings": [
-                ["⟪⚔", "⚔⟫"],
-                ["⟪▲", "▲⟫"],
-                ["⟪╸", "╺⟫"],
-                ["⟪⛨", "⛨⟫"],
-            ],
-        },
-        "branding": {
-            "agent_name": "Ares Agent",
-            "welcome": "Welcome to Ares Agent! Type your message or /help for commands.",
-            "goodbye": "Farewell, warrior! ⚔",
-            "response_label": " ⚔ Ares ",
-            "prompt_symbol": "⚔ ❯ ",
-            "help_header": "(⚔) Available Commands",
-        },
-        "tool_prefix": "╎",
-        "banner_logo": """[bold #A3261F] █████╗ ██████╗ ███████╗███████╗       █████╗  ██████╗ ███████╗███╗   ██╗████████╗[/]
-[bold #B73122]██╔══██╗██╔══██╗██╔════╝██╔════╝      ██╔══██╗██╔════╝ ██╔════╝████╗  ██║╚══██╔══╝[/]
-[#C93C24]███████║██████╔╝█████╗  ███████╗█████╗███████║██║  ███╗█████╗  ██╔██╗ ██║   ██║[/]
-[#D84A28]██╔══██║██╔══██╗██╔══╝  ╚════██║╚════╝██╔══██║██║   ██║██╔══╝  ██║╚██╗██║   ██║[/]
-[#E15A2D]██║  ██║██║  ██║███████╗███████║      ██║  ██║╚██████╔╝███████╗██║ ╚████║   ██║[/]
-[#EB6C32]╚═╝  ╚═╝╚═╝  ╚═╝╚══════╝╚══════╝      ╚═╝  ╚═╝ ╚═════╝ ╚══════╝╚═╝  ╚═══╝   ╚═╝[/]""",
-        "banner_hero": """[#9F1C1C]⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣤⣤⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀[/]
-[#9F1C1C]⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣴⣿⠟⠻⣿⣦⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀[/]
-[#C7A96B]⠀⠀⠀⠀⠀⠀⠀⣠⣾⡿⠋⠀⠀⠀⠙⢿⣷⣄⠀⠀⠀⠀⠀⠀⠀[/]
-[#C7A96B]⠀⠀⠀⠀⠀⢀⣾⡿⠋⠀⠀⢠⡄⠀⠀⠙⢿⣷⡀⠀⠀⠀⠀⠀[/]
-[#DD4A3A]⠀⠀⠀⠀⣰⣿⠟⠀⠀⠀⣰⣿⣿⣆⠀⠀⠀⠻⣿⣆⠀⠀⠀⠀[/]
-[#DD4A3A]⠀⠀⠀⢰⣿⠏⠀⠀⢀⣾⡿⠉⢿⣷⡀⠀⠀⠹⣿⡆⠀⠀⠀[/]
-[#9F1C1C]⠀⠀⠀⣿⡟⠀⠀⣠⣿⠟⠀⠀⠀⠻⣿⣄⠀⠀⢻⣿⠀⠀⠀[/]
-[#9F1C1C]⠀⠀⠀⣿⡇⠀⠀⠙⠋⠀⠀⚔⠀⠀⠙⠋⠀⠀⢸⣿⠀⠀⠀[/]
-[#6B1717]⠀⠀⠀⢿⣧⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣼⡿⠀⠀⠀[/]
-[#6B1717]⠀⠀⠀⠘⢿⣷⣄⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⣾⡿⠃⠀⠀⠀[/]
-[#C7A96B]⠀⠀⠀⠀⠈⠻⣿⣷⣦⣤⣀⣀⣤⣤⣶⣿⠿⠋⠀⠀⠀⠀[/]
-[#C7A96B]⠀⠀⠀⠀⠀⠀⠀⠉⠛⠿⠿⠿⠿⠛⠉⠀⠀⠀⠀⠀⠀⠀[/]
-[#DD4A3A]⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⚔⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀[/]
-[dim #6B1717]⠀⠀⠀⠀⠀⠀⠀⠀war god online⠀⠀⠀⠀⠀⠀⠀⠀[/]""",
-    },
-    "mono": {
-        "name": "mono",
-        "description": "Monochrome — clean grayscale",
-        "colors": {
-            "banner_border": "#555555",
-            "banner_title": "#e6edf3",
-            "banner_accent": "#aaaaaa",
-            "banner_dim": "#444444",
-            "banner_text": "#c9d1d9",
-            "ui_accent": "#aaaaaa",
-            "ui_label": "#888888",
-            "ui_ok": "#888888",
-            "ui_error": "#cccccc",
-            "ui_warn": "#999999",
-            "prompt": "#c9d1d9",
-            "input_rule": "#444444",
-            "response_border": "#aaaaaa",
-            "session_label": "#888888",
-            "session_border": "#555555",
-        },
-        "spinner": {},
-        "branding": {
-            "agent_name": "Hermes Agent",
-            "welcome": "Welcome to Hermes Agent! Type your message or /help for commands.",
-            "goodbye": "Goodbye! ⚕",
-            "response_label": " ⚕ Hermes ",
-            "prompt_symbol": "❯ ",
-            "help_header": "[?] Available Commands",
-        },
-        "tool_prefix": "┊",
-    },
-    "slate": {
-        "name": "slate",
-        "description": "Cool blue — developer-focused",
-        "colors": {
-            "banner_border": "#4169e1",
-            "banner_title": "#7eb8f6",
-            "banner_accent": "#8EA8FF",
-            "banner_dim": "#4b5563",
-            "banner_text": "#c9d1d9",
-            "ui_accent": "#7eb8f6",
-            "ui_label": "#8EA8FF",
-            "ui_ok": "#63D0A6",
-            "ui_error": "#F7A072",
-            "ui_warn": "#e6a855",
-            "prompt": "#c9d1d9",
-            "input_rule": "#4169e1",
-            "response_border": "#7eb8f6",
-            "session_label": "#7eb8f6",
-            "session_border": "#4b5563",
-        },
-        "spinner": {},
-        "branding": {
-            "agent_name": "Hermes Agent",
-            "welcome": "Welcome to Hermes Agent! Type your message or /help for commands.",
-            "goodbye": "Goodbye! ⚕",
-            "response_label": " ⚕ Hermes ",
-            "prompt_symbol": "❯ ",
-            "help_header": "(^_^)? Available Commands",
-        },
-        "tool_prefix": "┊",
-    },
-    "poseidon": {
-        "name": "poseidon",
-        "description": "Ocean-god theme — deep blue and seafoam",
-        "colors": {
-            "banner_border": "#2A6FB9",
-            "banner_title": "#A9DFFF",
-            "banner_accent": "#5DB8F5",
-            "banner_dim": "#153C73",
-            "banner_text": "#EAF7FF",
-            "ui_accent": "#5DB8F5",
-            "ui_label": "#A9DFFF",
-            "ui_ok": "#4caf50",
-            "ui_error": "#ef5350",
-            "ui_warn": "#ffa726",
-            "prompt": "#EAF7FF",
-            "input_rule": "#2A6FB9",
-            "response_border": "#5DB8F5",
-            "session_label": "#A9DFFF",
-            "session_border": "#496884",
-        },
-        "spinner": {
-            "waiting_faces": ["(≈)", "(Ψ)", "(∿)", "(◌)", "(◠)"],
-            "thinking_faces": ["(Ψ)", "(∿)", "(≈)", "(⌁)", "(◌)"],
-            "thinking_verbs": [
-                "charting currents", "sounding the depth", "reading foam lines",
-                "steering the trident", "tracking undertow", "plotting sea lanes",
-                "calling the swell", "measuring pressure",
-            ],
-            "wings": [
-                ["⟪≈", "≈⟫"],
-                ["⟪Ψ", "Ψ⟫"],
-                ["⟪∿", "∿⟫"],
-                ["⟪◌", "◌⟫"],
-            ],
-        },
-        "branding": {
-            "agent_name": "Poseidon Agent",
-            "welcome": "Welcome to Poseidon Agent! Type your message or /help for commands.",
-            "goodbye": "Fair winds! Ψ",
-            "response_label": " Ψ Poseidon ",
-            "prompt_symbol": "Ψ ❯ ",
-            "help_header": "(Ψ) Available Commands",
-        },
-        "tool_prefix": "│",
-        "banner_logo": """[bold #B8E8FF]██████╗  ██████╗ ███████╗██╗██████╗ ███████╗ ██████╗ ███╗   ██╗       █████╗  ██████╗ ███████╗███╗   ██╗████████╗[/]
-[bold #97D6FF]██╔══██╗██╔═══██╗██╔════╝██║██╔══██╗██╔════╝██╔═══██╗████╗  ██║      ██╔══██╗██╔════╝ ██╔════╝████╗  ██║╚══██╔══╝[/]
-[#75C1F6]██████╔╝██║   ██║███████╗██║██║  ██║█████╗  ██║   ██║██╔██╗ ██║█████╗███████║██║  ███╗█████╗  ██╔██╗ ██║   ██║[/]
-[#4FA2E0]██╔═══╝ ██║   ██║╚════██║██║██║  ██║██╔══╝  ██║   ██║██║╚██╗██║╚════╝██╔══██║██║   ██║██╔══╝  ██║╚██╗██║   ██║[/]
-[#2E7CC7]██║     ╚██████╔╝███████║██║██████╔╝███████╗╚██████╔╝██║ ╚████║      ██║  ██║╚██████╔╝███████╗██║ ╚████║   ██║[/]
-[#1B4F95]╚═╝      ╚═════╝ ╚══════╝╚═╝╚═════╝ ╚══════╝ ╚═════╝ ╚═╝  ╚═══╝      ╚═╝  ╚═╝ ╚═════╝ ╚══════╝╚═╝  ╚═══╝   ╚═╝[/]""",
-        "banner_hero": """[#2A6FB9]⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣀⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀[/]
-[#5DB8F5]⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⣾⣿⣷⣄⠀⠀⠀⠀⠀⠀⠀⠀⠀[/]
-[#5DB8F5]⠀⠀⠀⠀⠀⠀⠀⢠⣿⠏⠀Ψ⠀⠹⣿⡄⠀⠀⠀⠀⠀⠀⠀[/]
-[#A9DFFF]⠀⠀⠀⠀⠀⠀⠀⣿⡟⠀⠀⠀⠀⠀⢻⣿⠀⠀⠀⠀⠀⠀⠀[/]
-[#A9DFFF]⠀⠀⠀≈≈≈≈≈⣿⡇⠀⠀⠀⠀⠀⢸⣿≈≈≈≈≈⠀⠀⠀[/]
-[#5DB8F5]⠀⠀⠀⠀⠀⠀⠀⣿⡇⠀⠀⠀⠀⠀⢸⣿⠀⠀⠀⠀⠀⠀⠀[/]
-[#2A6FB9]⠀⠀⠀⠀⠀⠀⠀⢿⣧⠀⠀⠀⠀⠀⣼⡿⠀⠀⠀⠀⠀⠀⠀[/]
-[#2A6FB9]⠀⠀⠀⠀⠀⠀⠀⠘⢿⣷⣄⣀⣠⣾⡿⠃⠀⠀⠀⠀⠀⠀⠀[/]
-[#153C73]⠀⠀⠀⠀⠀⠀⠀⠀⠈⠻⣿⣿⡿⠟⠁⠀⠀⠀⠀⠀⠀⠀⠀[/]
-[#153C73]⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀[/]
-[#5DB8F5]⠀⠀⠀⠀⠀≈≈≈≈≈≈≈≈≈≈≈≈≈≈≈⠀⠀⠀⠀⠀[/]
-[#A9DFFF]⠀⠀⠀⠀⠀⠀≈≈≈≈≈≈≈≈≈≈≈≈≈⠀⠀⠀⠀⠀⠀[/]
-[dim #153C73]⠀⠀⠀⠀⠀⠀⠀deep waters hold⠀⠀⠀⠀⠀⠀⠀[/]""",
-    },
-    "sisyphus": {
-        "name": "sisyphus",
-        "description": "Sisyphean theme — austere grayscale with persistence",
-        "colors": {
-            "banner_border": "#B7B7B7",
-            "banner_title": "#F5F5F5",
-            "banner_accent": "#E7E7E7",
-            "banner_dim": "#4A4A4A",
-            "banner_text": "#D3D3D3",
-            "ui_accent": "#E7E7E7",
-            "ui_label": "#D3D3D3",
-            "ui_ok": "#919191",
-            "ui_error": "#E7E7E7",
-            "ui_warn": "#B7B7B7",
-            "prompt": "#F5F5F5",
-            "input_rule": "#656565",
-            "response_border": "#B7B7B7",
-            "session_label": "#919191",
-            "session_border": "#656565",
-        },
-        "spinner": {
-            "waiting_faces": ["(◉)", "(◌)", "(◬)", "(⬤)", "(::)"],
-            "thinking_faces": ["(◉)", "(◬)", "(◌)", "(○)", "(●)"],
-            "thinking_verbs": [
-                "finding traction", "measuring the grade", "resetting the boulder",
-                "counting the ascent", "testing leverage", "setting the shoulder",
-                "pushing uphill", "enduring the loop",
-            ],
-            "wings": [
-                ["⟪◉", "◉⟫"],
-                ["⟪◬", "◬⟫"],
-                ["⟪◌", "◌⟫"],
-                ["⟪⬤", "⬤⟫"],
-            ],
-        },
-        "branding": {
-            "agent_name": "Sisyphus Agent",
-            "welcome": "Welcome to Sisyphus Agent! Type your message or /help for commands.",
-            "goodbye": "The boulder waits. ◉",
-            "response_label": " ◉ Sisyphus ",
-            "prompt_symbol": "◉ ❯ ",
-            "help_header": "(◉) Available Commands",
-        },
-        "tool_prefix": "│",
-        "banner_logo": """[bold #F5F5F5]███████╗██╗███████╗██╗   ██╗██████╗ ██╗  ██╗██╗   ██╗███████╗       █████╗  ██████╗ ███████╗███╗   ██╗████████╗[/]
-[bold #E7E7E7]██╔════╝██║██╔════╝╚██╗ ██╔╝██╔══██╗██║  ██║██║   ██║██╔════╝      ██╔══██╗██╔════╝ ██╔════╝████╗  ██║╚══██╔══╝[/]
-[#D7D7D7]███████╗██║███████╗ ╚████╔╝ ██████╔╝███████║██║   ██║███████╗█████╗███████║██║  ███╗█████╗  ██╔██╗ ██║   ██║[/]
-[#BFBFBF]╚════██║██║╚════██║  ╚██╔╝  ██╔═══╝ ██╔══██║██║   ██║╚════██║╚════╝██╔══██║██║   ██║██╔══╝  ██║╚██╗██║   ██║[/]
-[#8F8F8F]███████║██║███████║   ██║   ██║     ██║  ██║╚██████╔╝███████║      ██║  ██║╚██████╔╝███████╗██║ ╚████║   ██║[/]
-[#626262]╚══════╝╚═╝╚══════╝   ╚═╝   ╚═╝     ╚═╝  ╚═╝ ╚═════╝ ╚══════╝      ╚═╝  ╚═╝ ╚═════╝ ╚══════╝╚═╝  ╚═══╝   ╚═╝[/]""",
-        "banner_hero": """[#B7B7B7]⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣀⣀⣀⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀[/]
-[#D3D3D3]⠀⠀⠀⠀⠀⠀⠀⣠⣾⣿⣿⣿⣿⣷⣄⠀⠀⠀⠀⠀⠀⠀⠀[/]
-[#E7E7E7]⠀⠀⠀⠀⠀⠀⣾⣿⣿⣿⣿⣿⣿⣿⣷⠀⠀⠀⠀⠀⠀⠀[/]
-[#F5F5F5]⠀⠀⠀⠀⠀⢸⣿⣿⣿⣿⣿⣿⣿⣿⣿⡇⠀⠀⠀⠀⠀⠀[/]
-[#E7E7E7]⠀⠀⠀⠀⠀⠀⣿⣿⣿⣿⣿⣿⣿⣿⣿⠀⠀⠀⠀⠀⠀⠀[/]
-[#D3D3D3]⠀⠀⠀⠀⠀⠀⠘⢿⣿⣿⣿⣿⣿⡿⠃⠀⠀⠀⠀⠀⠀⠀[/]
-[#B7B7B7]⠀⠀⠀⠀⠀⠀⠀⠀⠙⠿⣿⠿⠋⠀⠀⠀⠀⠀⠀⠀⠀⠀[/]
-[#919191]⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀[/]
-[#656565]⠀⠀⠀⠀⠀⠀⠀⠀⠀⣰⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀[/]
-[#656565]⠀⠀⠀⠀⠀⠀⠀⠀⣰⣿⣿⣆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀[/]
-[#4A4A4A]⠀⠀⠀⠀⠀⠀⠀⣰⣿⣿⣿⣿⣆⠀⠀⠀⠀⠀⠀⠀⠀⠀[/]
-[#4A4A4A]⠀⠀⠀⠀⠀⣀⣴⣿⣿⣿⣿⣿⣿⣦⣀⠀⠀⠀⠀⠀⠀[/]
-[#656565]⠀⠀⠀━━━━━━━━━━━━━━━━━━━━━━━⠀⠀⠀[/]
-[dim #4A4A4A]⠀⠀⠀⠀⠀⠀⠀⠀⠀the boulder⠀⠀⠀⠀⠀⠀⠀⠀⠀[/]""",
-    },
-    "charizard": {
-        "name": "charizard",
-        "description": "Volcanic theme — burnt orange and ember",
-        "colors": {
-            "banner_border": "#C75B1D",
-            "banner_title": "#FFD39A",
-            "banner_accent": "#F29C38",
-            "banner_dim": "#7A3511",
-            "banner_text": "#FFF0D4",
-            "ui_accent": "#F29C38",
-            "ui_label": "#FFD39A",
-            "ui_ok": "#4caf50",
-            "ui_error": "#ef5350",
-            "ui_warn": "#ffa726",
-            "prompt": "#FFF0D4",
-            "input_rule": "#C75B1D",
-            "response_border": "#F29C38",
-            "session_label": "#FFD39A",
-            "session_border": "#6C4724",
-        },
-        "spinner": {
-            "waiting_faces": ["(✦)", "(▲)", "(◇)", "(<>)", "(🔥)"],
-            "thinking_faces": ["(✦)", "(▲)", "(◇)", "(⌁)", "(🔥)"],
-            "thinking_verbs": [
-                "banking into the draft", "measuring burn", "reading the updraft",
-                "tracking ember fall", "setting wing angle", "holding the flame core",
-                "plotting a hot landing", "coiling for lift",
-            ],
-            "wings": [
-                ["⟪✦", "✦⟫"],
-                ["⟪▲", "▲⟫"],
-                ["⟪◌", "◌⟫"],
-                ["⟪◇", "◇⟫"],
-            ],
-        },
-        "branding": {
-            "agent_name": "Charizard Agent",
-            "welcome": "Welcome to Charizard Agent! Type your message or /help for commands.",
-            "goodbye": "Flame out! ✦",
-            "response_label": " ✦ Charizard ",
-            "prompt_symbol": "✦ ❯ ",
-            "help_header": "(✦) Available Commands",
-        },
-        "tool_prefix": "│",
-        "banner_logo": """[bold #FFF0D4] ██████╗██╗  ██╗ █████╗ ██████╗ ██╗███████╗ █████╗ ██████╗ ██████╗        █████╗  ██████╗ ███████╗███╗   ██╗████████╗[/]
-[bold #FFD39A]██╔════╝██║  ██║██╔══██╗██╔══██╗██║╚══███╔╝██╔══██╗██╔══██╗██╔══██╗      ██╔══██╗██╔════╝ ██╔════╝████╗  ██║╚══██╔══╝[/]
-[#F29C38]██║     ███████║███████║██████╔╝██║  ███╔╝ ███████║██████╔╝██║  ██║█████╗███████║██║  ███╗█████╗  ██╔██╗ ██║   ██║[/]
-[#E2832B]██║     ██╔══██║██╔══██║██╔══██╗██║ ███╔╝  ██╔══██║██╔══██╗██║  ██║╚════╝██╔══██║██║   ██║██╔══╝  ██║╚██╗██║   ██║[/]
-[#C75B1D]╚██████╗██║  ██║██║  ██║██║  ██║██║███████╗██║  ██║██║  ██║██████╔╝      ██║  ██║╚██████╔╝███████╗██║ ╚████║   ██║[/]
-[#7A3511] ╚═════╝╚═╝  ╚═╝╚═╝  ╚═╝╚═╝  ╚═╝╚═╝╚══════╝╚═╝  ╚═╝╚═╝  ╚═╝╚═════╝       ╚═╝  ╚═╝ ╚═════╝ ╚══════╝╚═╝  ╚═══╝   ╚═╝[/]""",
-        "banner_hero": """[#FFD39A]⠀⠀⠀⠀⠀⠀⠀⠀⣀⣤⠶⠶⠶⣤⣀⠀⠀⠀⠀⠀⠀⠀⠀[/]
-[#F29C38]⠀⠀⠀⠀⠀⠀⣴⠟⠁⠀⠀⠀⠀⠈⠻⣦⠀⠀⠀⠀⠀⠀[/]
-[#F29C38]⠀⠀⠀⠀⠀⣼⠏⠀⠀⠀✦⠀⠀⠀⠀⠹⣧⠀⠀⠀⠀⠀[/]
-[#E2832B]⠀⠀⠀⠀⢰⡟⠀⠀⣀⣤⣤⣤⣀⠀⠀⠀⢻⡆⠀⠀⠀⠀[/]
-[#E2832B]⠀⠀⣠⡾⠛⠁⣠⣾⠟⠉⠀⠉⠻⣷⣄⠀⠈⠛⢷⣄⠀⠀[/]
-[#C75B1D]⠀⣼⠟⠀⢀⣾⠟⠁⠀⠀⠀⠀⠀⠈⠻⣷⡀⠀⠻⣧⠀[/]
-[#C75B1D]⢸⡟⠀⠀⣿⡟⠀⠀⠀🔥⠀⠀⠀⠀⢻⣿⠀⠀⢻⡇[/]
-[#7A3511]⠀⠻⣦⡀⠘⢿⣧⡀⠀⠀⠀⠀⠀⢀⣼⡿⠃⢀⣴⠟⠀[/]
-[#7A3511]⠀⠀⠈⠻⣦⣀⠙⢿⣷⣤⣤⣤⣾⡿⠋⣀⣴⠟⠁⠀⠀[/]
-[#C75B1D]⠀⠀⠀⠀⠈⠙⠛⠶⠤⠭⠭⠤⠶⠛⠋⠁⠀⠀⠀⠀[/]
-[#F29C38]⠀⠀⠀⠀⠀⠀⠀⠀⣰⡿⢿⣆⠀⠀⠀⠀⠀⠀⠀⠀⠀[/]
-[#F29C38]⠀⠀⠀⠀⠀⠀⠀⣼⡟⠀⠀⢻⣧⠀⠀⠀⠀⠀⠀⠀⠀[/]
-[dim #7A3511]⠀⠀⠀⠀⠀⠀⠀tail flame lit⠀⠀⠀⠀⠀⠀⠀⠀[/]""",
-    },
-}
-
-
-# =============================================================================
-# Skin loading and management
-# =============================================================================
-
-_active_skin: Optional[SkinConfig] = None
-_active_skin_name: str = "default"
-
-
-def _skins_dir() -> Path:
-    """User skins directory."""
-    home = Path(os.getenv("HERMES_HOME", Path.home() / ".hermes"))
-    return home / "skins"
-
-
-def _load_skin_from_yaml(path: Path) -> Optional[Dict[str, Any]]:
-    """Load a skin definition from a YAML file."""
-    try:
-        import yaml
-        with open(path, "r", encoding="utf-8") as f:
-            data = yaml.safe_load(f)
-        if isinstance(data, dict) and "name" in data:
-            return data
-    except Exception as e:
-        logger.debug("Failed to load skin from %s: %s", path, e)
-    return None
-
-
-def _build_skin_config(data: Dict[str, Any]) -> SkinConfig:
-    """Build a SkinConfig from a raw dict (built-in or loaded from YAML)."""
-    # Start with default values as base for missing keys
-    default = _BUILTIN_SKINS["default"]
-    colors = dict(default.get("colors", {}))
-    colors.update(data.get("colors", {}))
-    spinner = dict(default.get("spinner", {}))
-    spinner.update(data.get("spinner", {}))
-    branding = dict(default.get("branding", {}))
-    branding.update(data.get("branding", {}))
-
-    return SkinConfig(
-        name=data.get("name", "unknown"),
-        description=data.get("description", ""),
-        colors=colors,
-        spinner=spinner,
-        branding=branding,
-        tool_prefix=data.get("tool_prefix", default.get("tool_prefix", "┊")),
-        banner_logo=data.get("banner_logo", ""),
-        banner_hero=data.get("banner_hero", ""),
-    )
-
-
-def list_skins() -> List[Dict[str, str]]:
-    """List all available skins (built-in + user-installed).
-
-    Returns list of {"name": ..., "description": ..., "source": "builtin"|"user"}.
-    """
-    result = []
-    for name, data in _BUILTIN_SKINS.items():
-        result.append({
-            "name": name,
-            "description": data.get("description", ""),
-            "source": "builtin",
-        })
-
-    skins_path = _skins_dir()
-    if skins_path.is_dir():
-        for f in sorted(skins_path.glob("*.yaml")):
-            data = _load_skin_from_yaml(f)
-            if data:
-                skin_name = data.get("name", f.stem)
-                # Skip if it shadows a built-in
-                if any(s["name"] == skin_name for s in result):
-                    continue
-                result.append({
-                    "name": skin_name,
-                    "description": data.get("description", ""),
-                    "source": "user",
-                })
-
-    return result
-
-
-def load_skin(name: str) -> SkinConfig:
-    """Load a skin by name. Checks user skins first, then built-in."""
-    # Check user skins directory
-    skins_path = _skins_dir()
-    user_file = skins_path / f"{name}.yaml"
-    if user_file.is_file():
-        data = _load_skin_from_yaml(user_file)
-        if data:
-            return _build_skin_config(data)
-
-    # Check built-in skins
-    if name in _BUILTIN_SKINS:
-        return _build_skin_config(_BUILTIN_SKINS[name])
-
-    # Fallback to default
-    logger.warning("Skin '%s' not found, using default", name)
-    return _build_skin_config(_BUILTIN_SKINS["default"])
-
-
-def get_active_skin() -> SkinConfig:
-    """Get the currently active skin config (cached)."""
-    global _active_skin
-    if _active_skin is None:
-        _active_skin = load_skin(_active_skin_name)
-    return _active_skin
-
-
-def set_active_skin(name: str) -> SkinConfig:
-    """Switch the active skin. Returns the new SkinConfig."""
-    global _active_skin, _active_skin_name
-    _active_skin_name = name
-    _active_skin = load_skin(name)
-    return _active_skin
-
-
-def get_active_skin_name() -> str:
-    """Get the name of the currently active skin."""
-    return _active_skin_name
-
-
-def init_skin_from_config(config: dict) -> None:
-    """Initialize the active skin from CLI config at startup.
-
-    Call this once during CLI init with the loaded config dict.
-    """
-    display = config.get("display", {})
-    skin_name = display.get("skin", "default")
-    if isinstance(skin_name, str) and skin_name.strip():
-        set_active_skin(skin_name.strip())
-    else:
-        set_active_skin("default")

hermes_cli/status.py

@@ -263,7 +263,7 @@ def show_status(args):
     if jobs_file.exists():
         import json
         try:
-            with open(jobs_file, encoding="utf-8") as f:
+            with open(jobs_file) as f:
                 data = json.load(f)
                 jobs = data.get("jobs", [])
                 enabled_jobs = [j for j in jobs if j.get("enabled", True)]
@@ -283,7 +283,7 @@ def show_status(args):
     if sessions_file.exists():
         import json
         try:
-            with open(sessions_file, encoding="utf-8") as f:
+            with open(sessions_file) as f:
                 data = json.load(f)
                 print(f"  Active:       {len(data)} session(s)")
         except Exception:

hermes_constants.py

@@ -7,6 +7,3 @@ without risk of circular imports.
 OPENROUTER_BASE_URL = "https://openrouter.ai/api/v1"
 OPENROUTER_MODELS_URL = f"{OPENROUTER_BASE_URL}/models"
 OPENROUTER_CHAT_URL = f"{OPENROUTER_BASE_URL}/chat/completions"
-
-NOUS_API_BASE_URL = "https://inference-api.nousresearch.com/v1"
-NOUS_API_CHAT_URL = f"{NOUS_API_BASE_URL}/chat/completions"

hermes_state.py

@@ -16,7 +16,6 @@ Key design decisions:
 
 import json
 import os
-import re
 import sqlite3
 import time
 from pathlib import Path
@@ -491,16 +490,12 @@ class SessionDB:
         msg_id = cursor.lastrowid
 
         # Update counters
-        # Count actual tool calls from the tool_calls list (not from tool responses).
-        # A single assistant message can contain multiple parallel tool calls.
-        num_tool_calls = 0
-        if tool_calls is not None:
-            num_tool_calls = len(tool_calls) if isinstance(tool_calls, list) else 1
-        if num_tool_calls > 0:
+        is_tool_related = role == "tool" or tool_calls is not None
+        if is_tool_related:
             self._conn.execute(
                 """UPDATE sessions SET message_count = message_count + 1,
-                   tool_call_count = tool_call_count + ? WHERE id = ?""",
-                (num_tool_calls, session_id),
+                   tool_call_count = tool_call_count + 1 WHERE id = ?""",
+                (session_id,),
             )
         else:
             self._conn.execute(
@@ -558,32 +553,6 @@ class SessionDB:
     # Search
     # =========================================================================
 
-    @staticmethod
-    def _sanitize_fts5_query(query: str) -> str:
-        """Sanitize user input for safe use in FTS5 MATCH queries.
-
-        FTS5 has its own query syntax where characters like ``"``, ``(``, ``)``,
-        ``+``, ``*``, ``{``, ``}`` and bare boolean operators (``AND``, ``OR``,
-        ``NOT``) have special meaning.  Passing raw user input directly to
-        MATCH can cause ``sqlite3.OperationalError``.
-
-        Strategy: strip characters that are only meaningful as FTS5 operators
-        and would otherwise cause syntax errors.  This preserves normal keyword
-        search while preventing crashes on inputs like ``C++``, ``"unterminated``,
-        or ``hello AND``.
-        """
-        # Remove FTS5-special characters that are not useful in keyword search
-        sanitized = re.sub(r'[+{}()"^]', " ", query)
-        # Collapse repeated * (e.g. "***") into a single one, and remove
-        # leading * (prefix-only matching requires at least one char before *)
-        sanitized = re.sub(r"\*+", "*", sanitized)
-        sanitized = re.sub(r"(^|\s)\*", r"\1", sanitized)
-        # Remove dangling boolean operators at start/end that would cause
-        # syntax errors (e.g. "hello AND" or "OR world")
-        sanitized = re.sub(r"(?i)^(AND|OR|NOT)\b\s*", "", sanitized.strip())
-        sanitized = re.sub(r"(?i)\s+(AND|OR|NOT)\s*$", "", sanitized.strip())
-        return sanitized.strip()
-
     def search_messages(
         self,
         query: str,
@@ -607,10 +576,6 @@ class SessionDB:
         if not query or not query.strip():
             return []
 
-        query = self._sanitize_fts5_query(query)
-        if not query:
-            return []
-
         if source_filter is None:
             source_filter = ["cli", "telegram", "discord", "whatsapp", "slack"]
 
@@ -650,11 +615,7 @@ class SessionDB:
             LIMIT ? OFFSET ?
         """
 
-        try:
-            cursor = self._conn.execute(sql, params)
-        except sqlite3.OperationalError:
-            # FTS5 query syntax error despite sanitization — return empty
-            return []
+        cursor = self._conn.execute(sql, params)
         matches = [dict(row) for row in cursor.fetchall()]
 
         # Add surrounding context (1 message before + after each match)

landingpage/index.html

@@ -19,10 +19,7 @@
     <link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet">
     
     <link rel="stylesheet" href="style.css">
-    <link rel="icon" type="image/x-icon" href="favicon.ico">
-    <link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png">
-    <link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png">
-    <link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png">
+    <link rel="icon" href="data:image/svg+xml,<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 100 100'><text y='.9em' font-size='90'>⚕</text></svg>">
 </head>
 <body>
     <!-- Ambient glow effects -->

model_tools.py

@@ -165,7 +165,6 @@ def get_tool_definitions(
     enabled_toolsets: List[str] = None,
     disabled_toolsets: List[str] = None,
     quiet_mode: bool = False,
-    agent_tool_policy=None,
 ) -> List[Dict[str, Any]]:
     """
     Get tool definitions for model API calls with toolset-based filtering.
@@ -223,10 +222,6 @@ def get_tool_definitions(
         for ts_name in get_all_toolsets():
             tools_to_include.update(resolve_toolset(ts_name))
 
-    # Apply agent-level tool policy filtering (if provided)
-    if agent_tool_policy is not None:
-        tools_to_include = agent_tool_policy.apply(tools_to_include)
-
     # Ask the registry for schemas (only returns tools whose check_fn passes)
     filtered_tools = registry.get_definitions(tools_to_include, quiet=quiet_mode)
 
@@ -271,7 +266,6 @@ def handle_function_call(
     function_args: Dict[str, Any],
     task_id: Optional[str] = None,
     user_task: Optional[str] = None,
-    enabled_tools: Optional[List[str]] = None,
 ) -> str:
     """
     Main function call dispatcher that routes calls to the tool registry.
@@ -281,40 +275,19 @@ def handle_function_call(
         function_args: Arguments for the function.
         task_id: Unique identifier for terminal/browser session isolation.
         user_task: The user's original task (for browser_snapshot context).
-        enabled_tools: Tool names enabled for this session.  When provided,
-                       execute_code uses this list to determine which sandbox
-                       tools to generate.  Falls back to the process-global
-                       ``_last_resolved_tool_names`` for backward compat.
 
     Returns:
         Function result as a JSON string.
     """
-    # Notify the read-loop tracker when a non-read/search tool runs,
-    # so the *consecutive* counter resets (reads after other work are fine).
-    _READ_SEARCH_TOOLS = {"read_file", "search_files"}
-    if function_name not in _READ_SEARCH_TOOLS:
-        try:
-            from tools.file_tools import notify_other_tool_call
-            notify_other_tool_call(task_id or "default")
-        except Exception:
-            pass  # file_tools may not be loaded yet
-
-    # Early check: reject tools not in the enabled set for this agent
-    if enabled_tools is not None and function_name not in enabled_tools:
-        return json.dumps({"error": f"Tool '{function_name}' is not available for this agent"})
-
     try:
         if function_name in _AGENT_LOOP_TOOLS:
             return json.dumps({"error": f"{function_name} must be handled by the agent loop"})
 
         if function_name == "execute_code":
-            # Prefer the caller-provided list so subagents can't overwrite
-            # the parent's tool set via the process-global.
-            sandbox_enabled = enabled_tools if enabled_tools is not None else _last_resolved_tool_names
             return registry.dispatch(
                 function_name, function_args,
                 task_id=task_id,
-                enabled_tools=sandbox_enabled,
+                enabled_tools=_last_resolved_tool_names,
             )
 
         return registry.dispatch(

optional-skills/migration/DESCRIPTION.md

@@ -1,2 +0,0 @@
-Optional migration workflows for importing user state and customizations from
-other agent systems into Hermes Agent.

optional-skills/migration/openclaw-migration/SKILL.md

@@ -1,281 +0,0 @@
----
-name: openclaw-migration
-description: Migrate a user's OpenClaw customization footprint into Hermes Agent. Imports Hermes-compatible memories, SOUL.md, command allowlists, user skills, and selected workspace assets from ~/.openclaw, then reports exactly what could not be migrated and why.
-version: 1.0.0
-author: Hermes Agent (Nous Research)
-license: MIT
-metadata:
-  hermes:
-    tags: [Migration, OpenClaw, Hermes, Memory, Persona, Import]
-    related_skills: [hermes-agent]
----
-
-# OpenClaw -> Hermes Migration
-
-Use this skill when a user wants to move their OpenClaw setup into Hermes Agent with minimal manual cleanup.
-
-## What this skill does
-
-It uses `scripts/openclaw_to_hermes.py` to:
-
-- import `SOUL.md` into the Hermes home directory as `SOUL.md`
-- transform OpenClaw `MEMORY.md` and `USER.md` into Hermes memory entries
-- merge OpenClaw command approval patterns into Hermes `command_allowlist`
-- migrate Hermes-compatible messaging settings such as `TELEGRAM_ALLOWED_USERS` and `MESSAGING_CWD`
-- copy OpenClaw skills into `~/.hermes/skills/openclaw-imports/`
-- optionally copy the OpenClaw workspace instructions file into a chosen Hermes workspace
-- mirror compatible workspace assets such as `workspace/tts/` into `~/.hermes/tts/`
-- archive non-secret docs that do not have a direct Hermes destination
-- produce a structured report listing migrated items, conflicts, skipped items, and reasons
-
-## Path resolution
-
-The helper script lives in this skill directory at:
-
-- `scripts/openclaw_to_hermes.py`
-
-When this skill is installed from the Skills Hub, the normal location is:
-
-- `~/.hermes/skills/migration/openclaw-migration/scripts/openclaw_to_hermes.py`
-
-Do not guess a shorter path like `~/.hermes/skills/openclaw-migration/...`.
-
-Before running the helper:
-
-1. Prefer the installed path under `~/.hermes/skills/migration/openclaw-migration/`.
-2. If that path fails, inspect the installed skill directory and resolve the script relative to the installed `SKILL.md`.
-3. Only use `find` as a fallback if the installed location is missing or the skill was moved manually.
-4. When calling the terminal tool, do not pass `workdir: "~"`. Use an absolute directory such as the user's home directory, or omit `workdir` entirely.
-
-With `--migrate-secrets`, it will also import a small allowlisted set of Hermes-compatible secrets, currently:
-
-- `TELEGRAM_BOT_TOKEN`
-
-## Default workflow
-
-1. Inspect first with a dry run.
-2. Present a simple summary of what can be migrated, what cannot be migrated, and what would be archived.
-3. If the `clarify` tool is available, use it for user decisions instead of asking for a free-form prose reply.
-4. If the dry run finds imported skill directory conflicts, ask how those should be handled before executing.
-5. Ask the user to choose between the two supported migration modes before executing.
-6. Ask for a target workspace path only if the user wants the workspace instructions file brought over.
-7. Execute the migration with the matching preset and flags.
-8. Summarize the results, especially:
-   - what was migrated
-   - what was archived for manual review
-   - what was skipped and why
-
-## User interaction protocol
-
-Hermes CLI supports the `clarify` tool for interactive prompts, but it is limited to:
-
-- one choice at a time
-- up to 4 predefined choices
-- an automatic `Other` free-text option
-
-It does **not** support true multi-select checkboxes in a single prompt.
-
-For every `clarify` call:
-
-- always include a non-empty `question`
-- include `choices` only for real selectable prompts
-- keep `choices` to 2-4 plain string options
-- never emit placeholder or truncated options such as `...`
-- never pad or stylize choices with extra whitespace
-- never include fake form fields in the question such as `enter directory here`, blank lines to fill in, or underscores like `_____`
-- for open-ended path questions, ask only the plain sentence; the user types in the normal CLI prompt below the panel
-
-If a `clarify` call returns an error, inspect the error text, correct the payload, and retry once with a valid `question` and clean choices.
-
-When `clarify` is available and the dry run reveals any required user decision, your **next action must be a `clarify` tool call**.
-Do not end the turn with a normal assistant message such as:
-
-- "Let me present the choices"
-- "What would you like to do?"
-- "Here are the options"
-
-If a user decision is required, collect it via `clarify` before producing more prose.
-If multiple unresolved decisions remain, do not insert an explanatory assistant message between them. After one `clarify` response is received, your next action should usually be the next required `clarify` call.
-
-Treat `workspace-agents` as an unresolved decision whenever the dry run reports:
-
-- `kind="workspace-agents"`
-- `status="skipped"`
-- reason containing `No workspace target was provided`
-
-In that case, you must ask about workspace instructions before execution. Do not silently treat that as a decision to skip.
-
-Because of that limitation, use this simplified decision flow:
-
-1. For `SOUL.md` conflicts, use `clarify` with choices such as:
-   - `keep existing`
-   - `overwrite with backup`
-   - `review first`
-2. If the dry run shows one or more `kind="skill"` items with `status="conflict"`, use `clarify` with choices such as:
-   - `keep existing skills`
-   - `overwrite conflicting skills with backup`
-   - `import conflicting skills under renamed folders`
-3. For workspace instructions, use `clarify` with choices such as:
-   - `skip workspace instructions`
-   - `copy to a workspace path`
-   - `decide later`
-4. If the user chooses to copy workspace instructions, ask a follow-up open-ended `clarify` question requesting an **absolute path**.
-5. If the user chooses `skip workspace instructions` or `decide later`, proceed without `--workspace-target`.
-5. For migration mode, use `clarify` with these 3 choices:
-   - `user-data only`
-   - `full compatible migration`
-   - `cancel`
-6. `user-data only` means: migrate user data and compatible config, but do **not** import allowlisted secrets.
-7. `full compatible migration` means: migrate the same compatible user data plus the allowlisted secrets when present.
-8. If `clarify` is not available, ask the same question in normal text, but still constrain the answer to `user-data only`, `full compatible migration`, or `cancel`.
-
-Execution gate:
-
-- Do not execute while a `workspace-agents` skip caused by `No workspace target was provided` remains unresolved.
-- The only valid ways to resolve it are:
-  - user explicitly chooses `skip workspace instructions`
-  - user explicitly chooses `decide later`
-  - user provides a workspace path after choosing `copy to a workspace path`
-- Absence of a workspace target in the dry run is not itself permission to execute.
-- Do not execute while any required `clarify` decision remains unresolved.
-
-Use these exact `clarify` payload shapes as the default pattern:
-
-- `{"question":"Your existing SOUL.md conflicts with the imported one. What should I do?","choices":["keep existing","overwrite with backup","review first"]}`
-- `{"question":"One or more imported OpenClaw skills already exist in Hermes. How should I handle those skill conflicts?","choices":["keep existing skills","overwrite conflicting skills with backup","import conflicting skills under renamed folders"]}`
-- `{"question":"Choose migration mode: migrate only user data, or run the full compatible migration including allowlisted secrets?","choices":["user-data only","full compatible migration","cancel"]}`
-- `{"question":"Do you want to copy the OpenClaw workspace instructions file into a Hermes workspace?","choices":["skip workspace instructions","copy to a workspace path","decide later"]}`
-- `{"question":"Please provide an absolute path where the workspace instructions should be copied."}`
-
-## Decision-to-command mapping
-
-Map user decisions to command flags exactly:
-
-- If the user chooses `keep existing` for `SOUL.md`, do **not** add `--overwrite`.
-- If the user chooses `overwrite with backup`, add `--overwrite`.
-- If the user chooses `review first`, stop before execution and review the relevant files.
-- If the user chooses `keep existing skills`, add `--skill-conflict skip`.
-- If the user chooses `overwrite conflicting skills with backup`, add `--skill-conflict overwrite`.
-- If the user chooses `import conflicting skills under renamed folders`, add `--skill-conflict rename`.
-- If the user chooses `user-data only`, execute with `--preset user-data` and do **not** add `--migrate-secrets`.
-- If the user chooses `full compatible migration`, execute with `--preset full --migrate-secrets`.
-- Only add `--workspace-target` if the user explicitly provided an absolute workspace path.
-- If the user chooses `skip workspace instructions` or `decide later`, do not add `--workspace-target`.
-
-Before executing, restate the exact command plan in plain language and make sure it matches the user's choices.
-
-## Post-run reporting rules
-
-After execution, treat the script's JSON output as the source of truth.
-
-1. Base all counts on `report.summary`.
-2. Only list an item under "Successfully Migrated" if its `status` is exactly `migrated`.
-3. Do not claim a conflict was resolved unless the report shows that item as `migrated`.
-4. Do not say `SOUL.md` was overwritten unless the report item for `kind="soul"` has `status="migrated"`.
-5. If `report.summary.conflict > 0`, include a conflict section instead of silently implying success.
-6. If counts and listed items disagree, fix the list to match the report before responding.
-7. Include the `output_dir` path from the report when available so the user can inspect `report.json`, `summary.md`, backups, and archived files.
-8. For memory or user-profile overflow, do not say the entries were archived unless the report explicitly shows an archive path. If `details.overflow_file` exists, say the full overflow list was exported there.
-9. If a skill was imported under a renamed folder, report the final destination and mention `details.renamed_from`.
-10. If `report.skill_conflict_mode` is present, use it as the source of truth for the selected imported-skill conflict policy.
-11. If an item has `status="skipped"`, do not describe it as overwritten, backed up, migrated, or resolved.
-12. If `kind="soul"` has `status="skipped"` with reason `Target already matches source`, say it was left unchanged and do not mention a backup.
-13. If a renamed imported skill has an empty `details.backup`, do not imply the existing Hermes skill was renamed or backed up. Say only that the imported copy was placed in the new destination and reference `details.renamed_from` as the pre-existing folder that remained in place.
-
-## Migration presets
-
-Prefer these two presets in normal use:
-
-- `user-data`
-- `full`
-
-`user-data` includes:
-
-- `soul`
-- `workspace-agents`
-- `memory`
-- `user-profile`
-- `messaging-settings`
-- `command-allowlist`
-- `skills`
-- `tts-assets`
-- `archive`
-
-`full` includes everything in `user-data` plus:
-
-- `secret-settings`
-
-The helper script still supports category-level `--include` / `--exclude`, but treat that as an advanced fallback rather than the default UX.
-
-## Commands
-
-Dry run with full discovery:
-
-```bash
-python3 ~/.hermes/skills/migration/openclaw-migration/scripts/openclaw_to_hermes.py
-```
-
-When using the terminal tool, prefer an absolute invocation pattern such as:
-
-```json
-{"command":"python3 /home/USER/.hermes/skills/migration/openclaw-migration/scripts/openclaw_to_hermes.py","workdir":"/home/USER"}
-```
-
-Dry run with the user-data preset:
-
-```bash
-python3 ~/.hermes/skills/migration/openclaw-migration/scripts/openclaw_to_hermes.py --preset user-data
-```
-
-Execute a user-data migration:
-
-```bash
-python3 ~/.hermes/skills/migration/openclaw-migration/scripts/openclaw_to_hermes.py --execute --preset user-data --skill-conflict skip
-```
-
-Execute a full compatible migration:
-
-```bash
-python3 ~/.hermes/skills/migration/openclaw-migration/scripts/openclaw_to_hermes.py --execute --preset full --migrate-secrets --skill-conflict skip
-```
-
-Execute with workspace instructions included:
-
-```bash
-python3 ~/.hermes/skills/migration/openclaw-migration/scripts/openclaw_to_hermes.py --execute --preset user-data --skill-conflict rename --workspace-target "/absolute/workspace/path"
-```
-
-Do not use `$PWD` or the home directory as the workspace target by default. Ask for an explicit workspace path first.
-
-## Important rules
-
-1. Run a dry run before writing unless the user explicitly says to proceed immediately.
-2. Do not migrate secrets by default. Tokens, auth blobs, device credentials, and raw gateway config should stay out of Hermes unless the user explicitly asks for secret migration.
-3. Do not silently overwrite non-empty Hermes targets unless the user explicitly wants that. The helper script will preserve backups when overwriting is enabled.
-4. Always give the user the skipped-items report. That report is part of the migration, not an optional extra.
-5. Prefer the primary OpenClaw workspace (`~/.openclaw/workspace/`) over `workspace.default/`. Only use the default workspace as fallback when the primary files are missing.
-6. Even in secret-migration mode, only migrate secrets with a clean Hermes destination. Unsupported auth blobs must still be reported as skipped.
-7. If the dry run shows a large asset copy, a conflicting `SOUL.md`, or overflowed memory entries, call those out separately before execution.
-8. Default to `user-data only` if the user is unsure.
-9. Only include `workspace-agents` when the user has explicitly provided a destination workspace path.
-10. Treat category-level `--include` / `--exclude` as an advanced escape hatch, not the normal flow.
-11. Do not end the dry-run summary with a vague “What would you like to do?” if `clarify` is available. Use structured follow-up prompts instead.
-12. Do not use an open-ended `clarify` prompt when a real choice prompt would work. Prefer selectable choices first, then free text only for absolute paths or file review requests.
-13. After a dry run, never stop after summarizing if there is still an unresolved decision. Use `clarify` immediately for the highest-priority blocking decision.
-14. Priority order for follow-up questions:
-    - `SOUL.md` conflict
-    - imported skill conflicts
-    - migration mode
-    - workspace instructions destination
-15. Do not promise to present choices later in the same message. Present them by actually calling `clarify`.
-16. After the migration-mode answer, explicitly check whether `workspace-agents` is still unresolved. If it is, your next action must be the workspace-instructions `clarify` call.
-17. After any `clarify` answer, if another required decision remains, do not narrate what was just decided. Ask the next required question immediately.
-
-## Expected result
-
-After a successful run, the user should have:
-
-- Hermes persona state imported
-- Hermes memory files populated with converted OpenClaw knowledge
-- OpenClaw skills available under `~/.hermes/skills/openclaw-imports/`
-- a migration report showing any conflicts, omissions, or unsupported data

plans/checkpoint-rollback.md

@@ -1,218 +0,0 @@
-# Checkpoint & Rollback — Implementation Plan
-
-## Goal
-
-Automatic filesystem snapshots before destructive file operations, with user-facing rollback. The agent never sees or interacts with this — it's transparent infrastructure.
-
-## Design Principles
-
-1. **Not a tool** — the LLM never knows about it. Zero prompt tokens, zero tool schema overhead.
-2. **Once per turn** — checkpoint at most once per conversation turn (user message → agent response cycle), triggered lazily on the first file-mutating operation. Not on every write.
-3. **Opt-in via config** — disabled by default, enabled with `checkpoints: true` in config.yaml.
-4. **Works on any directory** — uses a shadow git repo completely separate from the user's project git. Works on git repos, non-git directories, anything.
-5. **User-facing rollback** — `/rollback` slash command (CLI + gateway) to list and restore checkpoints. Also `hermes rollback` CLI subcommand.
-
-## Architecture
-
-```
-~/.hermes/checkpoints/
-  {sha256(abs_dir)[:16]}/       # Shadow git repo per working directory
-    HEAD, refs/, objects/...    # Standard git internals
-    HERMES_WORKDIR              # Original dir path (for display)
-    info/exclude                # Default excludes (node_modules, .env, etc.)
-```
-
-### Core: CheckpointManager (new file: tools/checkpoint_manager.py)
-
-Adapted from PR #559's CheckpointStore. Key changes from the PR:
-
-- **Not a tool** — no schema, no registry entry, no handler
-- **Turn-scoped deduplication** — tracks `_checkpointed_dirs: Set[str]` per turn
-- **Configurable** — reads `checkpoints` config key
-- **Pruning** — keeps last N snapshots per directory (default 50), prunes on take
-
-```python
-class CheckpointManager:
-    def __init__(self, enabled: bool = False, max_snapshots: int = 50):
-        self.enabled = enabled
-        self.max_snapshots = max_snapshots
-        self._checkpointed_dirs: Set[str] = set()  # reset each turn
-
-    def new_turn(self):
-        """Call at start of each conversation turn to reset dedup."""
-        self._checkpointed_dirs.clear()
-
-    def ensure_checkpoint(self, working_dir: str, reason: str = "auto") -> None:
-        """Take a checkpoint if enabled and not already done this turn."""
-        if not self.enabled:
-            return
-        abs_dir = str(Path(working_dir).resolve())
-        if abs_dir in self._checkpointed_dirs:
-            return
-        self._checkpointed_dirs.add(abs_dir)
-        try:
-            self._take(abs_dir, reason)
-        except Exception as e:
-            logger.debug("Checkpoint failed (non-fatal): %s", e)
-
-    def list_checkpoints(self, working_dir: str) -> List[dict]:
-        """List available checkpoints for a directory."""
-        ...
-
-    def restore(self, working_dir: str, commit_hash: str) -> dict:
-        """Restore files to a checkpoint state."""
-        ...
-
-    def _take(self, working_dir: str, reason: str):
-        """Shadow git: add -A + commit. Prune if over max_snapshots."""
-        ...
-
-    def _prune(self, shadow_repo: Path):
-        """Keep only last max_snapshots commits."""
-        ...
-```
-
-### Integration Point: run_agent.py
-
-The AIAgent already owns the conversation loop. Add CheckpointManager as an instance attribute:
-
-```python
-class AIAgent:
-    def __init__(self, ...):
-        ...
-        # Checkpoint manager — reads config to determine if enabled
-        self._checkpoint_mgr = CheckpointManager(
-            enabled=config.get("checkpoints", False),
-            max_snapshots=config.get("checkpoint_max_snapshots", 50),
-        )
-```
-
-**Turn boundary** — in `run_conversation()`, call `new_turn()` at the start of each agent iteration (before processing tool calls):
-
-```python
-# Inside the main loop, before _execute_tool_calls():
-self._checkpoint_mgr.new_turn()
-```
-
-**Trigger point** — in `_execute_tool_calls()`, before dispatching file-mutating tools:
-
-```python
-# Before the handle_function_call dispatch:
-if function_name in ("write_file", "patch"):
-    # Determine working dir from the file path in the args
-    file_path = function_args.get("path", "") or function_args.get("old_string", "")
-    if file_path:
-        work_dir = str(Path(file_path).parent.resolve())
-        self._checkpoint_mgr.ensure_checkpoint(work_dir, f"before {function_name}")
-```
-
-This means:
-- First `write_file` in a turn → checkpoint (fast, one `git add -A && git commit`)
-- Subsequent writes in the same turn → no-op (already checkpointed)
-- Next turn (new user message) → fresh checkpoint eligibility
-
-### Config
-
-Add to `DEFAULT_CONFIG` in `hermes_cli/config.py`:
-
-```python
-"checkpoints": False,          # Enable filesystem checkpoints before destructive ops
-"checkpoint_max_snapshots": 50, # Max snapshots to keep per directory
-```
-
-User enables with:
-```yaml
-# ~/.hermes/config.yaml
-checkpoints: true
-```
-
-### User-Facing Rollback
-
-**CLI slash command** — add `/rollback` to `process_command()` in `cli.py`:
-
-```
-/rollback         — List recent checkpoints for the current directory
-/rollback <hash>  — Restore files to that checkpoint
-```
-
-Shows a numbered list:
-```
-📸 Checkpoints for /home/user/project:
-  1. abc1234  2026-03-09 21:15  before write_file (3 files changed)
-  2. def5678  2026-03-09 20:42  before patch (1 file changed)
-  3. ghi9012  2026-03-09 20:30  before write_file (2 files changed)
-
-Use /rollback <number> to restore, e.g. /rollback 1
-```
-
-**Gateway slash command** — add `/rollback` to gateway/run.py with the same behavior.
-
-**CLI subcommand** — `hermes rollback` (optional, lower priority).
-
-### What Gets Excluded (not checkpointed)
-
-Same as the PR's defaults — written to the shadow repo's `info/exclude`:
-
-```
-node_modules/
-dist/
-build/
-.env
-.env.*
-__pycache__/
-*.pyc
-.DS_Store
-*.log
-.cache/
-.venv/
-.git/
-```
-
-Also respects the project's `.gitignore` if present (shadow repo can read it via `core.excludesFile`).
-
-### Safety
-
-- `ensure_checkpoint()` wraps everything in try/except — a checkpoint failure never blocks the actual file operation
-- Shadow repo is completely isolated — GIT_DIR + GIT_WORK_TREE env vars, never touches user's .git
-- If git isn't installed, checkpoints silently disable
-- Large directories: add a file count check — skip checkpoint if >50K files to avoid slowdowns
-
-## Files to Create/Modify
-
-| File | Change |
-|------|--------|
-| `tools/checkpoint_manager.py` | **NEW** — CheckpointManager class (adapted from PR #559) |
-| `run_agent.py` | Add CheckpointManager init + trigger in `_execute_tool_calls()` |
-| `hermes_cli/config.py` | Add `checkpoints` + `checkpoint_max_snapshots` to DEFAULT_CONFIG |
-| `cli.py` | Add `/rollback` slash command handler |
-| `gateway/run.py` | Add `/rollback` slash command handler |
-| `tests/tools/test_checkpoint_manager.py` | **NEW** — tests (adapted from PR #559's tests) |
-
-## What We Take From PR #559
-
-- `_shadow_repo_path()` — deterministic path hashing ✅
-- `_git_env()` — GIT_DIR/GIT_WORK_TREE isolation ✅
-- `_run_git()` — subprocess wrapper with timeout ✅
-- `_init_shadow_repo()` — shadow repo initialization ✅
-- `DEFAULT_EXCLUDES` list ✅
-- Test structure and patterns ✅
-
-## What We Change From PR #559
-
-- **Remove tool schema/registry** — not a tool
-- **Remove injection into file_operations.py and patch_parser.py** — trigger from run_agent.py instead
-- **Add turn-scoped deduplication** — one checkpoint per turn, not per operation
-- **Add pruning** — keep last N snapshots
-- **Add config flag** — opt-in, not mandatory
-- **Add /rollback command** — user-facing restore UI
-- **Add file count guard** — skip huge directories
-
-## Implementation Order
-
-1. `tools/checkpoint_manager.py` — core class with take/list/restore/prune
-2. `tests/tools/test_checkpoint_manager.py` — tests
-3. `hermes_cli/config.py` — config keys
-4. `run_agent.py` — integration (init + trigger)
-5. `cli.py` — `/rollback` slash command
-6. `gateway/run.py` — `/rollback` slash command
-7. Full test suite run + manual smoke test

pyproject.toml

@@ -40,16 +40,13 @@ dependencies = [
 [project.optional-dependencies]
 modal = ["swe-rex[modal]>=1.4.0"]
 daytona = ["daytona>=0.148.0"]
-dev = ["pytest", "pytest-asyncio", "mcp>=1.2.0"]
+dev = ["pytest", "pytest-asyncio"]
 messaging = ["python-telegram-bot>=20.0", "discord.py>=2.0", "aiohttp>=3.9.0", "slack-bolt>=1.18.0", "slack-sdk>=3.27.0"]
 cron = ["croniter"]
 slack = ["slack-bolt>=1.18.0", "slack-sdk>=3.27.0"]
 cli = ["simple-term-menu"]
 tts-premium = ["elevenlabs"]
-pty = [
-  "ptyprocess>=0.7.0; sys_platform != 'win32'",
-  "pywinpty>=2.0.0; sys_platform == 'win32'",
-]
+pty = ["ptyprocess>=0.7.0"]
 honcho = ["honcho-ai>=2.0.1"]
 mcp = ["mcp>=1.2.0"]
 homeassistant = ["aiohttp>=3.9.0"]

run_agent.py

@@ -158,7 +158,6 @@ class AIAgent:
         tool_delay: float = 1.0,
         enabled_toolsets: List[str] = None,
         disabled_toolsets: List[str] = None,
-        agent_tool_policy=None,
         save_trajectories: bool = False,
         verbose_logging: bool = False,
         quiet_mode: bool = False,
@@ -173,7 +172,6 @@ class AIAgent:
         provider_data_collection: str = None,
         session_id: str = None,
         tool_progress_callback: callable = None,
-        thinking_callback: callable = None,
         clarify_callback: callable = None,
         step_callback: callable = None,
         max_tokens: int = None,
@@ -181,14 +179,11 @@ class AIAgent:
         prefill_messages: List[Dict[str, Any]] = None,
         platform: str = None,
         skip_context_files: bool = False,
-        agent_workspace: str = None,
         skip_memory: bool = False,
         session_db=None,
         honcho_session_key: str = None,
         iteration_budget: "IterationBudget" = None,
         fallback_model: Dict[str, Any] = None,
-        checkpoints_enabled: bool = False,
-        checkpoint_max_snapshots: int = 50,
     ):
         """
         Initialize the AI Agent.
@@ -243,8 +238,6 @@ class AIAgent:
         self.ephemeral_system_prompt = ephemeral_system_prompt
         self.platform = platform  # "cli", "telegram", "discord", "whatsapp", etc.
         self.skip_context_files = skip_context_files
-        self._agent_tool_policy = agent_tool_policy
-        self._agent_workspace = Path(agent_workspace) if agent_workspace else None
         self.log_prefix_chars = log_prefix_chars
         self.log_prefix = f"{log_prefix} " if log_prefix else ""
         # Store effective base URL for feature detection (prompt caching, reasoning, etc.)
@@ -263,7 +256,6 @@ class AIAgent:
             self.api_mode = "chat_completions"
 
         self.tool_progress_callback = tool_progress_callback
-        self.thinking_callback = thinking_callback
         self.clarify_callback = clarify_callback
         self.step_callback = step_callback
         self._last_reported_tool = None  # Track for "new tool" mode
@@ -431,7 +423,6 @@ class AIAgent:
             enabled_toolsets=enabled_toolsets,
             disabled_toolsets=disabled_toolsets,
             quiet_mode=self.quiet_mode,
-            agent_tool_policy=agent_tool_policy,
         )
         
         # Show tool configuration and store valid tool names for validation
@@ -493,16 +484,8 @@ class AIAgent:
         # Cached system prompt -- built once per session, only rebuilt on compression
         self._cached_system_prompt: Optional[str] = None
         
-        # Filesystem checkpoint manager (transparent — not a tool)
-        from tools.checkpoint_manager import CheckpointManager
-        self._checkpoint_mgr = CheckpointManager(
-            enabled=checkpoints_enabled,
-            max_snapshots=checkpoint_max_snapshots,
-        )
-        
         # SQLite session store (optional -- provided by CLI or gateway)
         self._session_db = session_db
-        self._last_flushed_db_idx = 0  # tracks DB-write cursor to prevent duplicate writes
         if self._session_db:
             try:
                 self._session_db.create_session(
@@ -808,19 +791,45 @@ class AIAgent:
         self._save_session_log(messages)
         self._flush_messages_to_session_db(messages, conversation_history)
 
-    def _flush_messages_to_session_db(self, messages: List[Dict], conversation_history: List[Dict] = None):
-        """Persist any un-flushed messages to the SQLite session store.
+    def _log_msg_to_db(self, msg: Dict):
+        """Log a single message to SQLite immediately. Called after each messages.append()."""
+        if not self._session_db:
+            return
+        try:
+            role = msg.get("role", "unknown")
+            content = msg.get("content")
+            tool_calls_data = None
+            if hasattr(msg, "tool_calls") and msg.tool_calls:
+                tool_calls_data = [
+                    {"name": tc.function.name, "arguments": tc.function.arguments}
+                    for tc in msg.tool_calls
+                ]
+            elif isinstance(msg.get("tool_calls"), list):
+                tool_calls_data = msg["tool_calls"]
+            self._session_db.append_message(
+                session_id=self.session_id,
+                role=role,
+                content=content,
+                tool_name=msg.get("tool_name"),
+                tool_calls=tool_calls_data,
+                tool_call_id=msg.get("tool_call_id"),
+                finish_reason=msg.get("finish_reason"),
+            )
+        except Exception as e:
+            logger.debug("Session DB log_msg failed: %s", e)
 
-        Uses _last_flushed_db_idx to track which messages have already been
-        written, so repeated calls (from multiple exit paths) only write
-        truly new messages — preventing the duplicate-write bug (#860).
+    def _flush_messages_to_session_db(self, messages: List[Dict], conversation_history: List[Dict] = None):
+        """Persist any un-logged messages to the SQLite session store.
+
+        Called both at the normal end of run_conversation and from every early-
+        return path so that tool calls, tool responses, and assistant messages
+        are never lost even when the conversation errors out.
         """
         if not self._session_db:
             return
         try:
             start_idx = len(conversation_history) if conversation_history else 0
-            flush_from = max(start_idx, self._last_flushed_db_idx)
-            for msg in messages[flush_from:]:
+            for msg in messages[start_idx:]:
                 role = msg.get("role", "unknown")
                 content = msg.get("content")
                 tool_calls_data = None
@@ -840,7 +849,6 @@ class AIAgent:
                     tool_call_id=msg.get("tool_call_id"),
                     finish_reason=msg.get("finish_reason"),
                 )
-            self._last_flushed_db_idx = len(messages)
         except Exception as e:
             logger.debug("Session DB append_message failed: %s", e)
 
@@ -1423,34 +1431,6 @@ class AIAgent:
 
         return "\n\n".join(prompt_parts)
     
-    def _repair_tool_call(self, tool_name: str) -> str | None:
-        """Attempt to repair a mismatched tool name before aborting.
-
-        1. Try lowercase
-        2. Try normalized (lowercase + hyphens/spaces -> underscores)
-        3. Try fuzzy match (difflib, cutoff=0.7)
-
-        Returns the repaired name if found in valid_tool_names, else None.
-        """
-        from difflib import get_close_matches
-
-        # 1. Lowercase
-        lowered = tool_name.lower()
-        if lowered in self.valid_tool_names:
-            return lowered
-
-        # 2. Normalize
-        normalized = lowered.replace("-", "_").replace(" ", "_")
-        if normalized in self.valid_tool_names:
-            return normalized
-
-        # 3. Fuzzy match
-        matches = get_close_matches(lowered, self.valid_tool_names, n=1, cutoff=0.7)
-        if matches:
-            return matches[0]
-
-        return None
-
     def _invalidate_system_prompt(self):
         """
         Invalidate the cached system prompt, forcing a rebuild on the next turn.
@@ -2630,7 +2610,7 @@ class AIAgent:
             if messages and messages[-1].get("_flush_sentinel") == _sentinel:
                 messages.pop()
 
-    def _compress_context(self, messages: list, system_message: str, *, approx_tokens: int = None, task_id: str = "default") -> tuple:
+    def _compress_context(self, messages: list, system_message: str, *, approx_tokens: int = None) -> tuple:
         """Compress conversation context and split the session in SQLite.
 
         Returns:
@@ -2645,25 +2625,6 @@ class AIAgent:
         if todo_snapshot:
             compressed.append({"role": "user", "content": todo_snapshot})
 
-        # Preserve file-read history so the model doesn't re-read files
-        # it already examined before compression.
-        try:
-            from tools.file_tools import get_read_files_summary
-            read_files = get_read_files_summary(task_id)
-            if read_files:
-                file_list = "\n".join(
-                    f"  - {f['path']} ({', '.join(f['regions'])})"
-                    for f in read_files
-                )
-                compressed.append({"role": "user", "content": (
-                    "[Files already read in this session — do NOT re-read these]\n"
-                    f"{file_list}\n"
-                    "Use the information from the context summary above. "
-                    "Proceed with writing, editing, or responding."
-                )})
-        except Exception:
-            pass  # Don't break compression if file tracking fails
-
         self._invalidate_system_prompt()
         new_system_prompt = self._build_system_prompt(system_message)
         self._cached_system_prompt = new_system_prompt
@@ -2689,8 +2650,6 @@ class AIAgent:
                     except (ValueError, Exception) as e:
                         logger.debug("Could not propagate title on compression: %s", e)
                 self._session_db.update_system_prompt(self.session_id, new_system_prompt)
-                # Reset flush cursor — new session starts with no messages written
-                self._last_flushed_db_idx = 0
             except Exception as e:
                 logger.debug("Session DB compression split failed: %s", e)
 
@@ -2714,6 +2673,7 @@ class AIAgent:
                         "tool_call_id": skipped_tc.id,
                     }
                     messages.append(skip_msg)
+                    self._log_msg_to_db(skip_msg)
                 break
 
             function_name = tool_call.function.name
@@ -2729,8 +2689,6 @@ class AIAgent:
             except json.JSONDecodeError as e:
                 logging.warning(f"Unexpected JSON error after validation: {e}")
                 function_args = {}
-            if not isinstance(function_args, dict):
-                function_args = {}
 
             if not self.quiet_mode:
                 args_str = json.dumps(function_args, ensure_ascii=False)
@@ -2744,18 +2702,6 @@ class AIAgent:
                 except Exception as cb_err:
                     logging.debug(f"Tool progress callback error: {cb_err}")
 
-            # Checkpoint: snapshot working dir before file-mutating tools
-            if function_name in ("write_file", "patch") and self._checkpoint_mgr.enabled:
-                try:
-                    file_path = function_args.get("path", "")
-                    if file_path:
-                        work_dir = self._checkpoint_mgr.get_working_dir_for_path(file_path)
-                        self._checkpoint_mgr.ensure_checkpoint(
-                            work_dir, f"before {function_name}"
-                        )
-                except Exception:
-                    pass  # never block tool execution
-
             tool_start_time = time.time()
 
             if function_name == "todo":
@@ -2868,10 +2814,7 @@ class AIAgent:
                 spinner.start()
                 _spinner_result = None
                 try:
-                    function_result = handle_function_call(
-                        function_name, function_args, effective_task_id,
-                        enabled_tools=list(self.valid_tool_names) if self.valid_tool_names else None,
-                    )
+                    function_result = handle_function_call(function_name, function_args, effective_task_id)
                     _spinner_result = function_result
                 except Exception as tool_error:
                     function_result = f"Error executing tool '{function_name}': {tool_error}"
@@ -2882,10 +2825,7 @@ class AIAgent:
                     spinner.stop(cute_msg)
             else:
                 try:
-                    function_result = handle_function_call(
-                        function_name, function_args, effective_task_id,
-                        enabled_tools=list(self.valid_tool_names) if self.valid_tool_names else None,
-                    )
+                    function_result = handle_function_call(function_name, function_args, effective_task_id)
                 except Exception as tool_error:
                     function_result = f"Error executing tool '{function_name}': {tool_error}"
                     logger.error("handle_function_call raised for %s: %s", function_name, tool_error, exc_info=True)
@@ -2922,6 +2862,7 @@ class AIAgent:
                 "tool_call_id": tool_call.id
             }
             messages.append(tool_msg)
+            self._log_msg_to_db(tool_msg)
 
             if not self.quiet_mode:
                 response_preview = function_result[:self.log_prefix_chars] + "..." if len(function_result) > self.log_prefix_chars else function_result
@@ -2938,6 +2879,7 @@ class AIAgent:
                         "tool_call_id": skipped_tc.id
                     }
                     messages.append(skip_msg)
+                    self._log_msg_to_db(skip_msg)
                 break
 
             if self.tool_delay > 0 and i < len(assistant_message.tool_calls):
@@ -3100,8 +3042,6 @@ class AIAgent:
         self._invalid_tool_retries = 0
         self._invalid_json_retries = 0
         self._empty_content_retries = 0
-        self._incomplete_scratchpad_retries = 0
-        self._codex_incomplete_retries = 0
         self._last_content_with_tools = None
         self._turns_since_memory = 0
         self._iters_since_skill = 0
@@ -3168,6 +3108,7 @@ class AIAgent:
         # Add user message
         user_msg = {"role": "user", "content": user_message}
         messages.append(user_msg)
+        self._log_msg_to_db(user_msg)
         
         if not self.quiet_mode:
             print(f"💬 Starting conversation: '{user_message[:60]}{'...' if len(user_message) > 60 else ''}'")
@@ -3249,8 +3190,7 @@ class AIAgent:
                 for _pass in range(3):
                     _orig_len = len(messages)
                     messages, active_system_prompt = self._compress_context(
-                        messages, system_message, approx_tokens=_preflight_tokens,
-                        task_id=effective_task_id,
+                        messages, system_message, approx_tokens=_preflight_tokens
                     )
                     if len(messages) >= _orig_len:
                         break  # Cannot compress further
@@ -3266,16 +3206,11 @@ class AIAgent:
         final_response = None
         interrupted = False
         codex_ack_continuations = 0
-        length_continue_retries = 0
-        truncated_response_prefix = ""
         
         # Clear any stale interrupt state at start
         self.clear_interrupt()
         
         while api_call_count < self.max_iterations and self.iteration_budget.remaining > 0:
-            # Reset per-turn checkpoint dedup so each iteration can take one snapshot
-            self._checkpoint_mgr.new_turn()
-
             # Check for interrupt request (e.g., user sent new message)
             if self._interrupt_requested:
                 interrupted = True
@@ -3319,7 +3254,7 @@ class AIAgent:
             api_messages = []
             for msg in messages:
                 api_msg = msg.copy()
-
+                
                 # For ALL assistant messages, pass reasoning back to the API
                 # This ensures multi-turn reasoning context is preserved
                 if msg.get("role") == "assistant":
@@ -3327,7 +3262,7 @@ class AIAgent:
                     if reasoning_text:
                         # Add reasoning_content for API compatibility (Moonshot AI, Novita, OpenRouter)
                         api_msg["reasoning_content"] = reasoning_text
-
+                
                 # Remove 'reasoning' field - it's for trajectory storage only
                 # We've copied it to 'reasoning_content' for the API above
                 if "reasoning" in api_msg:
@@ -3338,7 +3273,7 @@ class AIAgent:
                 # Keep 'reasoning_details' - OpenRouter uses this for multi-turn reasoning context
                 # The signature field helps maintain reasoning continuity
                 api_messages.append(api_msg)
-
+            
             # Build the final system message: cached prompt + ephemeral system prompt.
             # The ephemeral part is appended here (not baked into the cached prompt)
             # so it stays out of the session DB and logs.
@@ -3351,21 +3286,21 @@ class AIAgent:
                 effective_system = (effective_system + "\n\n" + self.ephemeral_system_prompt).strip()
             if effective_system:
                 api_messages = [{"role": "system", "content": effective_system}] + api_messages
-
+            
             # Inject ephemeral prefill messages right after the system prompt
             # but before conversation history. Same API-call-time-only pattern.
             if self.prefill_messages:
                 sys_offset = 1 if effective_system else 0
                 for idx, pfm in enumerate(self.prefill_messages):
                     api_messages.insert(sys_offset + idx, pfm.copy())
-
+            
             # Apply Anthropic prompt caching for Claude models via OpenRouter.
             # Auto-detected: if model name contains "claude" and base_url is OpenRouter,
             # inject cache_control breakpoints (system + last 3 messages) to reduce
             # input token costs by ~75% on multi-turn conversations.
             if self._use_prompt_caching:
                 api_messages = apply_anthropic_cache_control(api_messages, cache_ttl=self._cache_ttl)
-
+            
             # Safety net: strip orphaned tool results / add stubs for missing
             # results before sending to the API.  The compressor handles this
             # during compression, but orphans can also sneak in from session
@@ -3388,13 +3323,9 @@ class AIAgent:
                 # Animated thinking spinner in quiet mode
                 face = random.choice(KawaiiSpinner.KAWAII_THINKING)
                 verb = random.choice(KawaiiSpinner.THINKING_VERBS)
-                if self.thinking_callback:
-                    # CLI TUI mode: use prompt_toolkit widget instead of raw spinner
-                    self.thinking_callback(f"{face} {verb}...")
-                else:
-                    spinner_type = random.choice(['brain', 'sparkle', 'pulse', 'moon', 'star'])
-                    thinking_spinner = KawaiiSpinner(f"{face} {verb}...", spinner_type=spinner_type)
-                    thinking_spinner.start()
+                spinner_type = random.choice(['brain', 'sparkle', 'pulse', 'moon', 'star'])
+                thinking_spinner = KawaiiSpinner(f"{face} {verb}...", spinner_type=spinner_type)
+                thinking_spinner.start()
             
             # Log request details if verbose
             if self.verbose_logging:
@@ -3409,8 +3340,6 @@ class AIAgent:
             max_compression_attempts = 3
             codex_auth_retry_attempted = False
             nous_auth_retry_attempted = False
-            restart_with_compressed_messages = False
-            restart_with_length_continuation = False
 
             finish_reason = "stop"
             response = None  # Guard against UnboundLocalError if all retries fail
@@ -3433,8 +3362,6 @@ class AIAgent:
                     if thinking_spinner:
                         thinking_spinner.stop("")
                         thinking_spinner = None
-                    if self.thinking_callback:
-                        self.thinking_callback("")
                     
                     if not self.quiet_mode:
                         print(f"{self.log_prefix}⏱️  API call completed in {api_duration:.2f}s")
@@ -3475,8 +3402,6 @@ class AIAgent:
                         if thinking_spinner:
                             thinking_spinner.stop(f"(´;ω;`) oops, retrying...")
                             thinking_spinner = None
-                        if self.thinking_callback:
-                            self.thinking_callback("")
                         
                         # This is often rate limiting or provider returning malformed response
                         retry_count += 1
@@ -3561,58 +3486,19 @@ class AIAgent:
                             finish_reason = "stop"
                     else:
                         finish_reason = response.choices[0].finish_reason
-
+                    
+                    # Handle "length" finish_reason - response was truncated
                     if finish_reason == "length":
                         print(f"{self.log_prefix}⚠️  Response truncated (finish_reason='length') - model hit max output tokens")
-
-                        if self.api_mode == "chat_completions":
-                            assistant_message = response.choices[0].message
-                            if not assistant_message.tool_calls:
-                                length_continue_retries += 1
-                                interim_msg = self._build_assistant_message(assistant_message, finish_reason)
-                                messages.append(interim_msg)
-                                if assistant_message.content:
-                                    truncated_response_prefix += assistant_message.content
-
-                                if length_continue_retries < 3:
-                                    print(
-                                        f"{self.log_prefix}↻ Requesting continuation "
-                                        f"({length_continue_retries}/3)..."
-                                    )
-                                    continue_msg = {
-                                        "role": "user",
-                                        "content": (
-                                            "[System: Your previous response was truncated by the output "
-                                            "length limit. Continue exactly where you left off. Do not "
-                                            "restart or repeat prior text. Finish the answer directly.]"
-                                        ),
-                                    }
-                                    messages.append(continue_msg)
-                                    self._session_messages = messages
-                                    self._save_session_log(messages)
-                                    restart_with_length_continuation = True
-                                    break
-
-                                partial_response = self._strip_think_blocks(truncated_response_prefix).strip()
-                                self._cleanup_task_resources(effective_task_id)
-                                self._persist_session(messages, conversation_history)
-                                return {
-                                    "final_response": partial_response or None,
-                                    "messages": messages,
-                                    "api_calls": api_call_count,
-                                    "completed": False,
-                                    "partial": True,
-                                    "error": "Response remained truncated after 3 continuation attempts",
-                                }
-
+                        
                         # If we have prior messages, roll back to last complete state
                         if len(messages) > 1:
                             print(f"{self.log_prefix}   ⏪ Rolling back to last complete assistant turn")
                             rolled_back_messages = self._get_messages_up_to_last_assistant(messages)
-
+                            
                             self._cleanup_task_resources(effective_task_id)
                             self._persist_session(messages, conversation_history)
-
+                            
                             return {
                                 "final_response": None,
                                 "messages": rolled_back_messages,
@@ -3685,8 +3571,6 @@ class AIAgent:
                     if thinking_spinner:
                         thinking_spinner.stop("")
                         thinking_spinner = None
-                    if self.thinking_callback:
-                        self.thinking_callback("")
                     api_elapsed = time.time() - api_start_time
                     print(f"{self.log_prefix}⚡ Interrupted during API call.")
                     self._persist_session(messages, conversation_history)
@@ -3699,8 +3583,6 @@ class AIAgent:
                     if thinking_spinner:
                         thinking_spinner.stop(f"(╥_╥) error, retrying...")
                         thinking_spinner = None
-                    if self.thinking_callback:
-                        self.thinking_callback("")
 
                     status_code = getattr(api_error, "status_code", None)
                     if (
@@ -3777,15 +3659,13 @@ class AIAgent:
 
                         original_len = len(messages)
                         messages, active_system_prompt = self._compress_context(
-                            messages, system_message, approx_tokens=approx_tokens,
-                            task_id=effective_task_id,
+                            messages, system_message, approx_tokens=approx_tokens
                         )
 
                         if len(messages) < original_len:
                             print(f"{self.log_prefix}   🗜️  Compressed {original_len} → {len(messages)} messages, retrying...")
                             time.sleep(2)  # Brief pause between compression retries
-                            restart_with_compressed_messages = True
-                            break
+                            continue  # Retry with compressed messages
                         else:
                             print(f"{self.log_prefix}❌ Payload too large and cannot compress further.")
                             logging.error(f"{self.log_prefix}413 payload too large. Cannot compress further.")
@@ -3846,16 +3726,14 @@ class AIAgent:
 
                         original_len = len(messages)
                         messages, active_system_prompt = self._compress_context(
-                            messages, system_message, approx_tokens=approx_tokens,
-                            task_id=effective_task_id,
+                            messages, system_message, approx_tokens=approx_tokens
                         )
 
                         if len(messages) < original_len or new_ctx and new_ctx < old_ctx:
                             if len(messages) < original_len:
                                 print(f"{self.log_prefix}   🗜️  Compressed {original_len} → {len(messages)} messages, retrying...")
                             time.sleep(2)  # Brief pause between compression retries
-                            restart_with_compressed_messages = True
-                            break
+                            continue  # Retry with compressed messages or new tier
                         else:
                             # Can't compress further and already at minimum tier
                             print(f"{self.log_prefix}❌ Context length exceeded and cannot compress further.")
@@ -3942,14 +3820,6 @@ class AIAgent:
             if interrupted:
                 break
 
-            if restart_with_compressed_messages:
-                api_call_count -= 1
-                self.iteration_budget.refund()
-                continue
-
-            if restart_with_length_continuation:
-                continue
-
             # Guard: if all retries exhausted without a successful response
             # (e.g. repeated context-length errors that exhausted retry_count),
             # the `response` variable is still None. Break out cleanly.
@@ -3964,27 +3834,6 @@ class AIAgent:
                 else:
                     assistant_message = response.choices[0].message
                 
-                # Normalize content to string — some OpenAI-compatible servers
-                # (llama-server, etc.) return content as a dict or list instead
-                # of a plain string, which crashes downstream .strip() calls.
-                if assistant_message.content is not None and not isinstance(assistant_message.content, str):
-                    raw = assistant_message.content
-                    if isinstance(raw, dict):
-                        assistant_message.content = raw.get("text", "") or raw.get("content", "") or json.dumps(raw)
-                    elif isinstance(raw, list):
-                        # Multimodal content list — extract text parts
-                        parts = []
-                        for part in raw:
-                            if isinstance(part, str):
-                                parts.append(part)
-                            elif isinstance(part, dict) and part.get("type") == "text":
-                                parts.append(part.get("text", ""))
-                            elif isinstance(part, dict) and "text" in part:
-                                parts.append(str(part["text"]))
-                        assistant_message.content = "\n".join(parts)
-                    else:
-                        assistant_message.content = str(raw)
-
                 # Handle assistant response
                 if assistant_message.content and not self.quiet_mode:
                     print(f"{self.log_prefix}🤖 Assistant: {assistant_message.content[:100]}{'...' if len(assistant_message.content) > 100 else ''}")
@@ -4062,6 +3911,7 @@ class AIAgent:
                         )
                         if not duplicate_interim:
                             messages.append(interim_msg)
+                            self._log_msg_to_db(interim_msg)
 
                     if self._codex_incomplete_retries < 3:
                         if not self.quiet_mode:
@@ -4093,36 +3943,39 @@ class AIAgent:
                             logging.debug(f"Tool call: {tc.function.name} with args: {tc.function.arguments[:200]}...")
                     
                     # Validate tool call names - detect model hallucinations
-                    # Repair mismatched tool names before validating
-                    for tc in assistant_message.tool_calls:
-                        if tc.function.name not in self.valid_tool_names:
-                            repaired = self._repair_tool_call(tc.function.name)
-                            if repaired:
-                                print(f"{self.log_prefix}🔧 Auto-repaired tool name: '{tc.function.name}' -> '{repaired}'")
-                                tc.function.name = repaired
                     invalid_tool_calls = [
-                        tc.function.name for tc in assistant_message.tool_calls
+                        tc.function.name for tc in assistant_message.tool_calls 
                         if tc.function.name not in self.valid_tool_names
                     ]
+                    
                     if invalid_tool_calls:
-                        # Return helpful error to model — model can self-correct next turn
-                        available = ", ".join(sorted(self.valid_tool_names))
-                        invalid_name = invalid_tool_calls[0]
-                        invalid_preview = invalid_name[:80] + "..." if len(invalid_name) > 80 else invalid_name
-                        print(f"{self.log_prefix}⚠️  Unknown tool '{invalid_preview}' — sending error to model for self-correction")
-                        assistant_msg = self._build_assistant_message(assistant_message, finish_reason)
-                        messages.append(assistant_msg)
-                        for tc in assistant_message.tool_calls:
-                            if tc.function.name not in self.valid_tool_names:
-                                content = f"Tool '{tc.function.name}' does not exist. Available tools: {available}"
-                            else:
-                                content = f"Skipped: another tool call in this turn used an invalid name. Please retry this tool call."
-                            messages.append({
-                                "role": "tool",
-                                "tool_call_id": tc.id,
-                                "content": content,
-                            })
-                        continue
+                        # Track retries for invalid tool calls
+                        if not hasattr(self, '_invalid_tool_retries'):
+                            self._invalid_tool_retries = 0
+                        self._invalid_tool_retries += 1
+                        
+                        invalid_preview = invalid_tool_calls[0][:80] + "..." if len(invalid_tool_calls[0]) > 80 else invalid_tool_calls[0]
+                        print(f"{self.log_prefix}⚠️  Invalid tool call detected: '{invalid_preview}'")
+                        print(f"{self.log_prefix}   Valid tools: {sorted(self.valid_tool_names)}")
+                        
+                        if self._invalid_tool_retries < 3:
+                            print(f"{self.log_prefix}🔄 Retrying API call ({self._invalid_tool_retries}/3)...")
+                            # Don't add anything to messages, just retry the API call
+                            continue
+                        else:
+                            print(f"{self.log_prefix}❌ Max retries (3) for invalid tool calls exceeded. Stopping as partial.")
+                            # Return partial result - don't include the bad tool call in messages
+                            self._invalid_tool_retries = 0
+                            self._persist_session(messages, conversation_history)
+                            return {
+                                "final_response": None,
+                                "messages": messages,
+                                "api_calls": api_call_count,
+                                "completed": False,
+                                "partial": True,
+                                "error": f"Model generated invalid tool call: {invalid_preview}"
+                            }
+                    
                     # Reset retry counter on successful tool call validation
                     if hasattr(self, '_invalid_tool_retries'):
                         self._invalid_tool_retries = 0
@@ -4166,6 +4019,7 @@ class AIAgent:
                             )
                             recovery_dict = {"role": "user", "content": recovery_msg}
                             messages.append(recovery_dict)
+                            self._log_msg_to_db(recovery_dict)
                             continue
                     
                     # Reset retry counter on successful JSON validation
@@ -4187,6 +4041,7 @@ class AIAgent:
                                 print(f"  ┊ 💬 {clean}")
                     
                     messages.append(assistant_msg)
+                    self._log_msg_to_db(assistant_msg)
                     
                     self._execute_tool_calls(assistant_message, messages, effective_task_id)
 
@@ -4200,8 +4055,7 @@ class AIAgent:
                     if self.compression_enabled and self.context_compressor.should_compress():
                         messages, active_system_prompt = self._compress_context(
                             messages, system_message,
-                            approx_tokens=self.context_compressor.last_prompt_tokens,
-                            task_id=effective_task_id,
+                            approx_tokens=self.context_compressor.last_prompt_tokens
                         )
                     
                     # Save session log incrementally (so progress is visible even if interrupted)
@@ -4288,6 +4142,7 @@ class AIAgent:
                                 "finish_reason": finish_reason,
                             }
                             messages.append(empty_msg)
+                            self._log_msg_to_db(empty_msg)
                             
                             self._cleanup_task_resources(effective_task_id)
                             self._persist_session(messages, conversation_history)
@@ -4318,6 +4173,7 @@ class AIAgent:
                         codex_ack_continuations += 1
                         interim_msg = self._build_assistant_message(assistant_message, "incomplete")
                         messages.append(interim_msg)
+                        self._log_msg_to_db(interim_msg)
 
                         continue_msg = {
                             "role": "user",
@@ -4327,14 +4183,12 @@ class AIAgent:
                             ),
                         }
                         messages.append(continue_msg)
+                        self._log_msg_to_db(continue_msg)
                         self._session_messages = messages
                         self._save_session_log(messages)
                         continue
 
                     codex_ack_continuations = 0
-
-                    if truncated_response_prefix:
-                        final_response = truncated_response_prefix + final_response
                     
                     # Strip <think> blocks from user-facing response (keep raw in messages for trajectory)
                     final_response = self._strip_think_blocks(final_response).strip()
@@ -4342,6 +4196,7 @@ class AIAgent:
                     final_msg = self._build_assistant_message(assistant_message, finish_reason)
                     
                     messages.append(final_msg)
+                    self._log_msg_to_db(final_msg)
                     
                     if not self.quiet_mode:
                         print(f"🎉 Conversation completed after {api_call_count} OpenAI-compatible API call(s)")
@@ -4378,6 +4233,7 @@ class AIAgent:
                                     "content": f"Error executing tool: {error_msg}",
                                 }
                                 messages.append(err_msg)
+                                self._log_msg_to_db(err_msg)
                         pending_handled = True
                     break
                 
@@ -4390,6 +4246,7 @@ class AIAgent:
                         "content": f"[System error during processing: {error_msg}]",
                     }
                     messages.append(sys_err_msg)
+                    self._log_msg_to_db(sys_err_msg)
                 
                 # If we're near the limit, break to avoid infinite loops
                 if api_call_count >= self.max_iterations - 1:

skills/creative/DESCRIPTION.md

@@ -1,3 +0,0 @@
----
-description: Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.
----

skills/creative/ascii-video/SKILL.md

@@ -1,250 +0,0 @@
----
-name: ascii-video
-description: "Production pipeline for ASCII art video — any format. Converts video/audio/images/generative input into colored ASCII character video output (MP4, GIF, image sequence). Covers: video-to-ASCII conversion, audio-reactive music visualizers, generative ASCII art animations, hybrid video+audio reactive, text/lyrics overlays, real-time terminal rendering. Use when users request: ASCII video, text art video, terminal-style video, character art animation, retro text visualization, audio visualizer in ASCII, converting video to ASCII art, matrix-style effects, or any animated ASCII output."
----
-
-# ASCII Video Production Pipeline
-
-Full production pipeline for rendering any content as colored ASCII character video.
-
-## Modes
-
-| Mode | Input | Output | Read |
-|------|-------|--------|------|
-| **Video-to-ASCII** | Video file | ASCII recreation of source footage | `references/inputs.md` § Video Sampling |
-| **Audio-reactive** | Audio file | Generative visuals driven by audio features | `references/inputs.md` § Audio Analysis |
-| **Generative** | None (or seed params) | Procedural ASCII animation | `references/effects.md` |
-| **Hybrid** | Video + audio | ASCII video with audio-reactive overlays | Both input refs |
-| **Lyrics/text** | Audio + text/SRT | Timed text with visual effects | `references/inputs.md` § Text/Lyrics |
-| **TTS narration** | Text quotes + TTS API | Narrated testimonial/quote video with typed text | `references/inputs.md` § TTS Integration |
-
-## Stack
-
-Single self-contained Python script per project. No GPU.
-
-| Layer | Tool | Purpose |
-|-------|------|---------|
-| Core | Python 3.10+, NumPy | Math, array ops, vectorized effects |
-| Signal | SciPy | FFT, peak detection (audio modes only) |
-| Imaging | Pillow (PIL) | Font rasterization, video frame decoding, image I/O |
-| Video I/O | ffmpeg (CLI) | Decode input, encode output segments, mux audio, mix tracks |
-| Parallel | concurrent.futures / multiprocessing | N workers for batch/clip rendering |
-| TTS | ElevenLabs API (or similar) | Generate narration clips for quote/testimonial videos |
-| Optional | OpenCV | Video frame sampling, edge detection, optical flow |
-
-## Pipeline Architecture (v2)
-
-Every mode follows the same 6-stage pipeline. See `references/architecture.md` for implementation details, `references/scenes.md` for scene protocol, and `references/composition.md` for multi-grid composition and tonemap.
-
-```
-┌─────────┐   ┌──────────┐   ┌───────────┐   ┌──────────┐   ┌─────────┐   ┌────────┐
-│ 1.INPUT  │→│ 2.ANALYZE │→│ 3.SCENE_FN │→│ 4.TONEMAP │→│ 5.SHADE  │→│ 6.ENCODE│
-│ load src │  │ features  │  │ → canvas   │  │ normalize │  │ post-fx  │  │ → video │
-└─────────┘   └──────────┘   └───────────┘   └──────────┘   └─────────┘   └────────┘
-```
-
-1. **INPUT** — Load/decode source material (video frames, audio samples, images, or nothing)
-2. **ANALYZE** — Extract per-frame features (audio bands, video luminance/edges, motion vectors)
-3. **SCENE_FN** — Scene function renders directly to pixel canvas (`uint8 H,W,3`). May internally compose multiple character grids via `_render_vf()` + pixel blend modes. See `references/composition.md`
-4. **TONEMAP** — Percentile-based adaptive brightness normalization with per-scene gamma. Replaces linear brightness multipliers. See `references/composition.md` § Adaptive Tonemap
-5. **SHADE** — Apply post-processing `ShaderChain` + `FeedbackBuffer`. See `references/shaders.md`
-6. **ENCODE** — Pipe raw RGB frames to ffmpeg for H.264/GIF encoding
-
-## Creative Direction
-
-**Every project should look and feel different.** The references provide a vocabulary of building blocks — don't copy them verbatim. Combine, modify, and invent.
-
-### Aesthetic Dimensions to Vary
-
-| Dimension | Options | Reference |
-|-----------|---------|-----------|
-| **Character palette** | Density ramps, block elements, symbols, scripts (katakana, Greek, runes, braille), dots, project-specific | `architecture.md` § Character Palettes |
-| **Color strategy** | HSV (angle/distance/time/value mapped), discrete RGB palettes, monochrome, complementary, triadic, temperature | `architecture.md` § Color System |
-| **Color tint** | Warm, cool, amber, matrix green, neon pink, sepia, ice, blood, void, sunset | `shaders.md` § Color Grade |
-| **Background texture** | Sine fields, noise, smooth noise, cellular/voronoi, video source | `effects.md` § Background Fills |
-| **Primary effects** | Rings, spirals, tunnel, vortex, waves, interference, aurora, ripple, fire | `effects.md` § Radial / Wave / Fire |
-| **Particles** | Energy sparks, snow, rain, bubbles, runes, binary data, orbits, gravity wells | `effects.md` § Particle Systems |
-| **Shader mood** | Retro CRT, clean modern, glitch art, cinematic, dreamy, harsh industrial, psychedelic | `shaders.md` § Design Philosophy |
-| **Grid density** | xs(8px) through xxl(40px), mixed per layer | `architecture.md` § Grid System |
-| **Font** | Menlo, Monaco, Courier, SF Mono, JetBrains Mono, Fira Code, IBM Plex | `architecture.md` § Font Selection |
-| **Mirror mode** | None, horizontal, vertical, quad, diagonal, kaleidoscope | `shaders.md` § Mirror Effects |
-| **Transition style** | Crossfade, wipe (directional/radial), dissolve, glitch cut | `shaders.md` § Transitions |
-
-### Per-Section Variation
-
-Never use the same config for the entire video. For each section/scene/quote:
-- Choose a **different background effect** (or compose 2-3)
-- Choose a **different character palette** (match the mood)
-- Choose a **different color strategy** (or at minimum a different hue)
-- Vary **shader intensity** (more bloom during peaks, more grain during quiet)
-- Use **different particle types** if particles are active
-
-### Project-Specific Invention
-
-For every project, invent at least one of:
-- A custom character palette matching the theme
-- A custom background effect (combine/modify existing ones)
-- A custom color palette (discrete RGB set matching the brand/mood)
-- A custom particle character set
-
-## Workflow
-
-### Step 1: Determine Mode and Gather Requirements
-
-Establish with user:
-- **Input source** — file path, format, duration
-- **Mode** — which of the 6 modes above
-- **Sections** — time-mapped style changes (timestamps → effect names)
-- **Resolution** — default 1920x1080 @ 24fps; GIFs typically 640x360 @ 15fps
-- **Style direction** — dense/sparse, bright/dark, chaotic/minimal, color palette
-- **Text/branding** — easter eggs, overlays, credits, themed character sets
-- **Output format** — MP4 (default), GIF, PNG sequence
-
-### Step 2: Detect Hardware and Set Quality
-
-Before building the script, detect the user's hardware and set appropriate defaults. See `references/optimization.md` § Hardware Detection.
-
-```python
-hw = detect_hardware()
-profile = quality_profile(hw, target_duration, user_quality_pref)
-log(f"Hardware: {hw['cpu_count']} cores, {hw['mem_gb']:.1f}GB RAM")
-log(f"Render: {profile['vw']}x{profile['vh']} @{profile['fps']}fps, {profile['workers']} workers")
-```
-
-Never hardcode worker counts, resolution, or CRF. Always detect and adapt.
-
-### Step 3: Build the Script
-
-Write as a single Python file. Major components:
-
-1. **Hardware detection + quality profile** — see `references/optimization.md`
-2. **Input loader** — mode-dependent; see `references/inputs.md`
-3. **Feature analyzer** — audio FFT, video luminance, or pass-through
-4. **Grid + renderer** — multi-density character grids with bitmap cache; `_render_vf()` helper for value/hue field → canvas
-5. **Character palettes** — multiple palettes chosen per project theme; see `references/architecture.md`
-6. **Color system** — HSV + discrete RGB palettes as needed; see `references/architecture.md`
-7. **Scene functions** — each returns `canvas (uint8 H,W,3)` directly. May compose multiple grids internally via pixel blend modes. See `references/scenes.md` + `references/composition.md`
-8. **Tonemap** — adaptive brightness normalization with per-scene gamma; see `references/composition.md`
-9. **Shader pipeline** — `ShaderChain` + `FeedbackBuffer` per-section config; see `references/shaders.md`
-10. **Scene table + dispatcher** — maps time ranges to scene functions + shader/feedback configs; see `references/scenes.md`
-11. **Parallel encoder** — N-worker batch clip rendering with ffmpeg pipes
-12. **Main** — orchestrate full pipeline
-
-### Step 4: Handle Critical Bugs
-
-#### Font Cell Height (macOS Pillow)
-
-`textbbox()` returns wrong height. Use `font.getmetrics()`:
-
-```python
-ascent, descent = font.getmetrics()
-cell_height = ascent + descent  # correct
-```
-
-#### ffmpeg Pipe Deadlock
-
-Never use `stderr=subprocess.PIPE` with long-running ffmpeg. Redirect to file:
-
-```python
-stderr_fh = open(err_path, "w")
-pipe = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.DEVNULL, stderr=stderr_fh)
-```
-
-#### Brightness — Use `tonemap()`, Not Linear Multipliers
-
-ASCII on black is inherently dark. This is the #1 visual issue. **Do NOT use linear `* N` brightness multipliers** — they clip highlights and wash out the image. Instead, use the **adaptive tonemap** function from `references/composition.md`:
-
-```python
-def tonemap(canvas, gamma=0.75):
-    """Percentile-based adaptive normalization + gamma. Replaces all brightness multipliers."""
-    f = canvas.astype(np.float32)
-    lo = np.percentile(f, 1)          # black point (1st percentile)
-    hi = np.percentile(f, 99.5)       # white point (99.5th percentile)
-    if hi - lo < 1: hi = lo + 1
-    f = (f - lo) / (hi - lo)
-    f = np.clip(f, 0, 1) ** gamma     # gamma < 1 = brighter mids
-    return (f * 255).astype(np.uint8)
-```
-
-Pipeline ordering: `scene_fn() → tonemap() → FeedbackBuffer → ShaderChain → ffmpeg`
-
-Per-scene gamma overrides for destructive effects:
-- Default: `gamma=0.75`
-- Solarize scenes: `gamma=0.55` (solarize darkens above-threshold pixels)
-- Posterize scenes: `gamma=0.50` (quantization loses brightness range)
-- Already-bright scenes: `gamma=0.85`
-
-Additional brightness best practices:
-- Dense animated backgrounds — never flat black, always fill the grid
-- Vignette minimum clamped to 0.15 (not 0.12)
-- Bloom threshold lowered to 130 (not 170) so more pixels contribute to glow
-- Use `screen` blend mode (not `overlay`) when compositing dark ASCII layers — overlay squares dark values: `2 * 0.12 * 0.12 = 0.03`
-
-#### Font Compatibility
-
-Not all Unicode characters render in all fonts. Validate palettes at init:
-```python
-for c in palette:
-    img = Image.new("L", (20, 20), 0)
-    ImageDraw.Draw(img).text((0, 0), c, fill=255, font=font)
-    if np.array(img).max() == 0:
-        log(f"WARNING: char '{c}' (U+{ord(c):04X}) not in font, removing from palette")
-```
-
-### Step 4b: Per-Clip Architecture (for segmented videos)
-
-When the video has discrete segments (quotes, scenes, chapters), render each as a separate clip file. This enables:
-- Re-rendering individual clips without touching the rest (`--clip q05`)
-- Faster iteration on specific sections
-- Easy reordering or trimming in post
-
-```python
-segments = [
-    {"id": "intro", "start": 0.0, "end": 5.0, "type": "intro"},
-    {"id": "q00", "start": 5.0, "end": 12.0, "type": "quote", "qi": 0, ...},
-    {"id": "t00", "start": 12.0, "end": 13.5, "type": "transition", ...},
-    {"id": "outro", "start": 208.0, "end": 211.6, "type": "outro"},
-]
-
-from concurrent.futures import ProcessPoolExecutor, as_completed
-with ProcessPoolExecutor(max_workers=hw["workers"]) as pool:
-    futures = {pool.submit(render_clip, seg, features, path): seg["id"]
-               for seg, path in clip_args}
-    for fut in as_completed(futures):
-        fut.result()
-```
-
-CLI: `--clip q00 t00 q01` to re-render specific clips, `--list` to show segments, `--skip-render` to re-stitch only.
-
-### Step 5: Render and Iterate
-
-Performance targets per frame:
-
-| Component | Budget |
-|-----------|--------|
-| Feature extraction | 1-5ms |
-| Effect function | 2-15ms |
-| Character render | 80-150ms (bottleneck) |
-| Shader pipeline | 5-25ms |
-| **Total** | ~100-200ms/frame |
-
-**Fast iteration**: render single test frames to check brightness/layout before full render:
-```python
-canvas = render_single_frame(frame_index, features, renderer)
-Image.fromarray(canvas).save("test.png")
-```
-
-**Brightness verification**: sample 5-10 frames across video, check `mean > 8` for ASCII content.
-
-## References
-
-| File | Contents |
-|------|----------|
-| `references/architecture.md` | Grid system, font selection, character palettes (library of 20+), color system (HSV + discrete RGB), `_render_vf()` helper, compositing, v2 effect function contract |
-| `references/inputs.md` | All input sources: audio analysis, video sampling, image conversion, text/lyrics, TTS integration (ElevenLabs, voice assignment, audio mixing) |
-| `references/effects.md` | Effect building blocks: 12 value field generators (`vf_sinefield` through `vf_noise_static`), 8 hue field generators (`hf_fixed` through `hf_plasma`), radial/wave/fire effects, particles, composing guide |
-| `references/shaders.md` | 38 shader implementations (geometry, channel, color, glow, noise, pattern, tone, glitch, mirror), `ShaderChain` class, full `_apply_shader_step()` dispatch, audio-reactive scaling, transitions, tint presets |
-| `references/composition.md` | **v2 core**: pixel blend modes (20 modes with implementations), multi-grid composition, `_render_vf()` helper, adaptive `tonemap()`, per-scene gamma, `FeedbackBuffer` with spatial transforms, `PixelBlendStack` |
-| `references/scenes.md` | **v2 scene protocol**: scene function contract, `Renderer` class, `SCENES` table structure, `render_clip()` loop, beat-synced cutting, parallel rendering + pickling constraints, 4 complete scene examples, scene design checklist |
-| `references/troubleshooting.md` | NumPy broadcasting traps, blend mode pitfalls, multiprocessing/pickling issues, brightness diagnostics, ffmpeg deadlocks, font issues, performance bottlenecks, common mistakes |
-| `references/optimization.md` | Hardware detection, adaptive quality profiles (draft/preview/production/max), CLI integration, vectorized effect patterns, parallel rendering, memory management |

skills/creative/ascii-video/references/architecture.md

@@ -1,528 +0,0 @@
-# Architecture Reference
-
-## Grid System
-
-### Multi-Density Grids
-
-Pre-initialize multiple grid sizes. Switch per section for visual variety.
-
-| Key | Font Size | Grid (1920x1080) | Use |
-|-----|-----------|-------------------|-----|
-| xs | 8 | 400x108 | Ultra-dense data fields |
-| sm | 10 | 320x83 | Dense detail, rain, starfields |
-| md | 16 | 192x56 | Default balanced, transitions |
-| lg | 20 | 160x45 | Quote/lyric text (readable at 1080p) |
-| xl | 24 | 137x37 | Short quotes, large titles |
-| xxl | 40 | 80x22 | Giant text, minimal |
-
-**Grid sizing for text-heavy content**: When displaying readable text (quotes, lyrics, testimonials), use 20px (`lg`) as the primary grid. This gives 160 columns -- plenty for lines up to ~50 chars centered. For very short quotes (< 60 chars, <= 3 lines), 24px (`xl`) makes them more impactful. Only init the grids you actually use -- each grid pre-rasterizes all characters which costs ~0.3-0.5s.
-
-Grid dimensions: `cols = VW // cell_width`, `rows = VH // cell_height`.
-
-### Font Selection
-
-Don't hardcode a single font. Choose fonts to match the project's mood. Monospace fonts are required for grid alignment but vary widely in personality:
-
-| Font | Personality | Platform |
-|------|-------------|----------|
-| Menlo | Clean, neutral, Apple-native | macOS |
-| Monaco | Retro terminal, compact | macOS |
-| Courier New | Classic typewriter, wide | Cross-platform |
-| SF Mono | Modern, tight spacing | macOS |
-| Consolas | Windows native, clean | Windows |
-| JetBrains Mono | Developer, ligature-ready | Install |
-| Fira Code | Geometric, modern | Install |
-| IBM Plex Mono | Corporate, authoritative | Install |
-| Source Code Pro | Adobe, balanced | Install |
-
-**Font detection at init**: probe available fonts and fall back gracefully:
-
-```python
-import platform
-
-def find_font(preferences):
-    """Try fonts in order, return first that exists."""
-    for name, path in preferences:
-        if os.path.exists(path):
-            return path
-    raise FileNotFoundError(f"No monospace font found. Tried: {[p for _,p in preferences]}")
-
-FONT_PREFS_MACOS = [
-    ("Menlo", "/System/Library/Fonts/Menlo.ttc"),
-    ("Monaco", "/System/Library/Fonts/Monaco.ttf"),
-    ("SF Mono", "/System/Library/Fonts/SFNSMono.ttf"),
-    ("Courier", "/System/Library/Fonts/Courier.ttc"),
-]
-FONT_PREFS_LINUX = [
-    ("DejaVu Sans Mono", "/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf"),
-    ("Liberation Mono", "/usr/share/fonts/truetype/liberation/LiberationMono-Regular.ttf"),
-    ("Noto Sans Mono", "/usr/share/fonts/truetype/noto/NotoSansMono-Regular.ttf"),
-    ("Ubuntu Mono", "/usr/share/fonts/truetype/ubuntu/UbuntuMono-R.ttf"),
-]
-FONT_PREFS = FONT_PREFS_MACOS if platform.system() == "Darwin" else FONT_PREFS_LINUX
-```
-
-**Multi-font rendering**: use different fonts for different layers (e.g., monospace for background, a bolder variant for overlay text). Each GridLayer owns its own font:
-
-```python
-grid_bg = GridLayer(find_font(FONT_PREFS), 16)       # background
-grid_text = GridLayer(find_font(BOLD_PREFS), 20)      # readable text
-```
-
-### Collecting All Characters
-
-Before initializing grids, gather all characters that need bitmap pre-rasterization:
-
-```python
-all_chars = set()
-for pal in [PAL_DEFAULT, PAL_DENSE, PAL_BLOCKS, PAL_RUNE, PAL_KATA,
-            PAL_GREEK, PAL_MATH, PAL_DOTS, PAL_BRAILLE, PAL_STARS,
-            PAL_BINARY, PAL_MUSIC, PAL_BOX, PAL_CIRCUIT, PAL_ARROWS,
-            PAL_HERMES]:  # ... all palettes used in project
-    all_chars.update(pal)
-# Add any overlay text characters
-all_chars.update("ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789 .,-:;!?/|")
-all_chars.discard(" ")  # space is never rendered
-```
-
-### GridLayer Initialization
-
-Each grid pre-computes coordinate arrays for vectorized effect math:
-
-```python
-class GridLayer:
-    def __init__(self, font_path, font_size):
-        self.font = ImageFont.truetype(font_path, font_size)
-        asc, desc = self.font.getmetrics()
-        bbox = self.font.getbbox("M")
-        self.cw = bbox[2] - bbox[0]  # character cell width
-        self.ch = asc + desc  # CRITICAL: not textbbox height
-
-        self.cols = VW // self.cw
-        self.rows = VH // self.ch
-        self.ox = (VW - self.cols * self.cw) // 2  # centering
-        self.oy = (VH - self.rows * self.ch) // 2
-
-        # Index arrays
-        self.rr = np.arange(self.rows, dtype=np.float32)[:, None]
-        self.cc = np.arange(self.cols, dtype=np.float32)[None, :]
-
-        # Polar coordinates (aspect-corrected)
-        cx, cy = self.cols / 2.0, self.rows / 2.0
-        asp = self.cw / self.ch
-        self.dx = self.cc - cx
-        self.dy = (self.rr - cy) * asp
-        self.dist = np.sqrt(self.dx**2 + self.dy**2)
-        self.angle = np.arctan2(self.dy, self.dx)
-
-        # Normalized (0-1 range) -- for distance falloff
-        self.dx_n = (self.cc - cx) / max(self.cols, 1)
-        self.dy_n = (self.rr - cy) / max(self.rows, 1) * asp
-        self.dist_n = np.sqrt(self.dx_n**2 + self.dy_n**2)
-
-        # Pre-rasterize all characters to float32 bitmaps
-        self.bm = {}
-        for c in all_chars:
-            img = Image.new("L", (self.cw, self.ch), 0)
-            ImageDraw.Draw(img).text((0, 0), c, fill=255, font=self.font)
-            self.bm[c] = np.array(img, dtype=np.float32) / 255.0
-```
-
-### Character Render Loop
-
-The bottleneck. Composites pre-rasterized bitmaps onto pixel canvas:
-
-```python
-def render(self, chars, colors, canvas=None):
-    if canvas is None:
-        canvas = np.zeros((VH, VW, 3), dtype=np.uint8)
-    for row in range(self.rows):
-        y = self.oy + row * self.ch
-        if y + self.ch > VH: break
-        for col in range(self.cols):
-            c = chars[row, col]
-            if c == " ": continue
-            x = self.ox + col * self.cw
-            if x + self.cw > VW: break
-            a = self.bm[c]  # float32 bitmap
-            canvas[y:y+self.ch, x:x+self.cw] = np.maximum(
-                canvas[y:y+self.ch, x:x+self.cw],
-                (a[:, :, None] * colors[row, col]).astype(np.uint8))
-    return canvas
-```
-
-Use `np.maximum` for additive blending (brighter chars overwrite dimmer ones, never darken).
-
-### Multi-Layer Rendering
-
-Render multiple grids onto the same canvas for depth:
-
-```python
-canvas = np.zeros((VH, VW, 3), dtype=np.uint8)
-canvas = grid_lg.render(bg_chars, bg_colors, canvas)   # background layer
-canvas = grid_md.render(main_chars, main_colors, canvas)  # main layer
-canvas = grid_sm.render(detail_chars, detail_colors, canvas)  # detail overlay
-```
-
----
-
-## Character Palettes
-
-### Design Principles
-
-Character palettes are the primary visual texture of ASCII video. They control not just brightness mapping but the entire visual feel. Design palettes intentionally:
-
-- **Visual weight**: characters sorted by the amount of ink/pixels they fill. Space is always index 0.
-- **Coherence**: characters within a palette should belong to the same visual family.
-- **Density curve**: the brightness-to-character mapping is nonlinear. Dense palettes (many chars) give smoother gradients; sparse palettes (5-8 chars) give posterized/graphic looks.
-- **Rendering compatibility**: every character in the palette must exist in the font. Test at init and remove missing glyphs.
-
-### Palette Library
-
-Organized by visual family. Mix and match per project -- don't default to PAL_DEFAULT for everything.
-
-#### Density / Brightness Palettes
-```python
-PAL_DEFAULT  = " .`'-:;!><=+*^~?/|(){}[]#&$@%"       # classic ASCII art
-PAL_DENSE    = " .:;+=xX$#@\u2588"                          # simple 11-level ramp
-PAL_MINIMAL  = " .:-=+#@"                               # 8-level, graphic
-PAL_BINARY   = " \u2588"                                      # 2-level, extreme contrast
-PAL_GRADIENT = " \u2591\u2592\u2593\u2588"                              # 4-level block gradient
-```
-
-#### Unicode Block Elements
-```python
-PAL_BLOCKS   = " \u2591\u2592\u2593\u2588\u2584\u2580\u2590\u258c"                 # standard blocks
-PAL_BLOCKS_EXT = " \u2596\u2597\u2598\u2599\u259a\u259b\u259c\u259d\u259e\u259f\u2591\u2592\u2593\u2588"  # quadrant blocks (more detail)
-PAL_SHADE    = " \u2591\u2592\u2593\u2588\u2587\u2586\u2585\u2584\u2583\u2582\u2581"          # vertical fill progression
-```
-
-#### Symbolic / Thematic
-```python
-PAL_MATH     = " \u00b7\u2218\u2219\u2022\u00b0\u00b1\u2213\u00d7\u00f7\u2248\u2260\u2261\u2264\u2265\u221e\u222b\u2211\u220f\u221a\u2207\u2202\u2206\u03a9"    # math symbols
-PAL_BOX      = " \u2500\u2502\u250c\u2510\u2514\u2518\u251c\u2524\u252c\u2534\u253c\u2550\u2551\u2554\u2557\u255a\u255d\u2560\u2563\u2566\u2569\u256c"          # box drawing
-PAL_CIRCUIT  = " .\u00b7\u2500\u2502\u250c\u2510\u2514\u2518\u253c\u25cb\u25cf\u25a1\u25a0\u2206\u2207\u2261"                 # circuit board
-PAL_RUNE     = " .\u16a0\u16a2\u16a6\u16b1\u16b7\u16c1\u16c7\u16d2\u16d6\u16da\u16de\u16df"                   # elder futhark runes
-PAL_ALCHEMIC = " \u2609\u263d\u2640\u2642\u2643\u2644\u2645\u2646\u2647\u2648\u2649\u264a\u264b"            # planetary/alchemical symbols
-PAL_ZODIAC   = " \u2648\u2649\u264a\u264b\u264c\u264d\u264e\u264f\u2650\u2651\u2652\u2653"            # zodiac
-PAL_ARROWS   = " \u2190\u2191\u2192\u2193\u2194\u2195\u2196\u2197\u2198\u2199\u21a9\u21aa\u21bb\u27a1"             # directional arrows
-PAL_MUSIC    = " \u266a\u266b\u266c\u2669\u266d\u266e\u266f\u25cb\u25cf"                       # musical notation
-```
-
-#### Script / Writing System
-```python
-PAL_KATA     = " \u00b7\uff66\uff67\uff68\uff69\uff6a\uff6b\uff6c\uff6d\uff6e\uff6f\uff70\uff71\uff72\uff73\uff74\uff75\uff76\uff77"          # katakana halfwidth (matrix rain)
-PAL_GREEK    = " \u03b1\u03b2\u03b3\u03b4\u03b5\u03b6\u03b7\u03b8\u03b9\u03ba\u03bb\u03bc\u03bd\u03be\u03c0\u03c1\u03c3\u03c4\u03c6\u03c8\u03c9"    # Greek lowercase
-PAL_CYRILLIC = " \u0430\u0431\u0432\u0433\u0434\u0435\u0436\u0437\u0438\u043a\u043b\u043c\u043d\u043e\u043f\u0440\u0441\u0442\u0443\u0444\u0445\u0446\u0447\u0448"  # Cyrillic lowercase
-PAL_ARABIC   = " \u0627\u0628\u062a\u062b\u062c\u062d\u062e\u062f\u0630\u0631\u0632\u0633\u0634\u0635\u0636\u0637"       # Arabic letters (isolated forms)
-```
-
-#### Dot / Point Progressions
-```python
-PAL_DOTS     = " \u22c5\u2218\u2219\u25cf\u25c9\u25ce\u25c6\u2726\u2605"                   # dot size progression
-PAL_BRAILLE  = " \u2801\u2802\u2803\u2804\u2805\u2806\u2807\u2808\u2809\u280a\u280b\u280c\u280d\u280e\u280f\u2810\u2811\u2812\u2813\u2814\u2815\u2816\u2817\u2818\u2819\u281a\u281b\u281c\u281d\u281e\u281f\u283f"  # braille patterns
-PAL_STARS    = " \u00b7\u2727\u2726\u2729\u2728\u2605\u2736\u2733\u2738"               # star progression
-```
-
-#### Project-Specific (examples -- invent new ones per project)
-```python
-PAL_HERMES   = " .\u00b7~=\u2248\u221e\u26a1\u263f\u2726\u2605\u2295\u25ca\u25c6\u25b2\u25bc\u25cf\u25a0"   # mythology/tech blend
-PAL_OCEAN    = " ~\u2248\u2248\u2248\u223c\u2307\u2248\u224b\u224c\u2248"                       # water/wave characters
-PAL_ORGANIC  = " .\u00b0\u2218\u2022\u25e6\u25c9\u2742\u273f\u2741\u2743"                 # growing/botanical
-PAL_MACHINE  = " _\u2500\u2502\u250c\u2510\u253c\u2261\u25a0\u2588\u2593\u2592\u2591"             # mechanical/industrial
-```
-
-### Creating Custom Palettes
-
-When designing for a project, build palettes from the content's theme:
-
-1. **Choose a visual family** (dots, blocks, symbols, script)
-2. **Sort by visual weight** -- render each char at target font size, count lit pixels, sort ascending
-3. **Test at target grid size** -- some chars collapse to blobs at small sizes
-4. **Validate in font** -- remove chars the font can't render:
-
-```python
-def validate_palette(pal, font):
-    """Remove characters the font can't render."""
-    valid = []
-    for c in pal:
-        if c == " ":
-            valid.append(c)
-            continue
-        img = Image.new("L", (20, 20), 0)
-        ImageDraw.Draw(img).text((0, 0), c, fill=255, font=font)
-        if np.array(img).max() > 0:  # char actually rendered something
-            valid.append(c)
-    return "".join(valid)
-```
-
-### Mapping Values to Characters
-
-```python
-def val2char(v, mask, pal=PAL_DEFAULT):
-    """Map float array (0-1) to character array using palette."""
-    n = len(pal)
-    idx = np.clip((v * n).astype(int), 0, n - 1)
-    out = np.full(v.shape, " ", dtype="U1")
-    for i, ch in enumerate(pal):
-        out[mask & (idx == i)] = ch
-    return out
-```
-
-**Nonlinear mapping** for different visual curves:
-
-```python
-def val2char_gamma(v, mask, pal, gamma=1.0):
-    """Gamma-corrected palette mapping. gamma<1 = brighter, gamma>1 = darker."""
-    v_adj = np.power(np.clip(v, 0, 1), gamma)
-    return val2char(v_adj, mask, pal)
-
-def val2char_step(v, mask, pal, thresholds):
-    """Custom threshold mapping. thresholds = list of float breakpoints."""
-    out = np.full(v.shape, pal[0], dtype="U1")
-    for i, thr in enumerate(thresholds):
-        out[mask & (v > thr)] = pal[min(i + 1, len(pal) - 1)]
-    return out
-```
-
----
-
-## Color System
-
-### HSV->RGB (Vectorized)
-
-All color computation in HSV for intuitive control, converted at render time:
-
-```python
-def hsv2rgb(h, s, v):
-    """Vectorized HSV->RGB. h,s,v are numpy arrays. Returns (R,G,B) uint8 arrays."""
-    h = h % 1.0
-    c = v * s; x = c * (1 - np.abs((h*6) % 2 - 1)); m = v - c
-    # ... 6 sector assignment ...
-    return (np.clip((r+m)*255, 0, 255).astype(np.uint8),
-            np.clip((g+m)*255, 0, 255).astype(np.uint8),
-            np.clip((b+m)*255, 0, 255).astype(np.uint8))
-```
-
-### Color Mapping Strategies
-
-Don't default to a single strategy. Choose based on the visual intent:
-
-| Strategy | Hue source | Effect | Good for |
-|----------|------------|--------|----------|
-| Angle-mapped | `g.angle / (2*pi)` | Rainbow around center | Radial effects, kaleidoscopes |
-| Distance-mapped | `g.dist_n * 0.3` | Gradient from center | Tunnels, depth effects |
-| Frequency-mapped | `f["cent"] * 0.2` | Timbral color shifting | Audio-reactive |
-| Value-mapped | `val * 0.15` | Brightness-dependent hue | Fire, heat maps |
-| Time-cycled | `t * rate` | Slow color rotation | Ambient, chill |
-| Source-sampled | Video frame pixel colors | Preserve original color | Video-to-ASCII |
-| Palette-indexed | Discrete color lookup | Flat graphic style | Retro, pixel art |
-| Temperature | Blend between warm/cool | Emotional tone | Mood-driven scenes |
-| Complementary | `hue` and `hue + 0.5` | High contrast | Bold, dramatic |
-| Triadic | `hue`, `hue + 0.33`, `hue + 0.66` | Vibrant, balanced | Psychedelic |
-| Analogous | `hue +/- 0.08` | Harmonious, subtle | Elegant, cohesive |
-| Monochrome | Fixed hue, vary S and V | Restrained, focused | Noir, minimal |
-
-### Color Palettes (Discrete RGB)
-
-For non-HSV workflows -- direct RGB color sets for graphic/retro looks:
-
-```python
-# Named color palettes -- use for flat/graphic styles or per-character coloring
-COLORS_NEON = [(255,0,102), (0,255,153), (102,0,255), (255,255,0), (0,204,255)]
-COLORS_PASTEL = [(255,179,186), (255,223,186), (255,255,186), (186,255,201), (186,225,255)]
-COLORS_MONO_GREEN = [(0,40,0), (0,80,0), (0,140,0), (0,200,0), (0,255,0)]
-COLORS_MONO_AMBER = [(40,20,0), (80,50,0), (140,90,0), (200,140,0), (255,191,0)]
-COLORS_CYBERPUNK = [(255,0,60), (0,255,200), (180,0,255), (255,200,0)]
-COLORS_VAPORWAVE = [(255,113,206), (1,205,254), (185,103,255), (5,255,161)]
-COLORS_EARTH = [(86,58,26), (139,90,43), (189,154,91), (222,193,136), (245,230,193)]
-COLORS_ICE = [(200,230,255), (150,200,240), (100,170,230), (60,130,210), (30,80,180)]
-COLORS_BLOOD = [(80,0,0), (140,10,10), (200,20,20), (255,50,30), (255,100,80)]
-COLORS_FOREST = [(10,30,10), (20,60,15), (30,100,20), (50,150,30), (80,200,50)]
-
-def rgb_palette_map(val, mask, palette):
-    """Map float array (0-1) to RGB colors from a discrete palette."""
-    n = len(palette)
-    idx = np.clip((val * n).astype(int), 0, n - 1)
-    R = np.zeros(val.shape, dtype=np.uint8)
-    G = np.zeros(val.shape, dtype=np.uint8)
-    B = np.zeros(val.shape, dtype=np.uint8)
-    for i, (r, g, b) in enumerate(palette):
-        m = mask & (idx == i)
-        R[m] = r; G[m] = g; B[m] = b
-    return R, G, B
-```
-
-### Compositing Helpers
-
-```python
-def mkc(R, G, B, rows, cols):
-    """Pack 3 uint8 arrays into (rows, cols, 3) color array."""
-    o = np.zeros((rows, cols, 3), dtype=np.uint8)
-    o[:,:,0] = R; o[:,:,1] = G; o[:,:,2] = B
-    return o
-
-def layer_over(base_ch, base_co, top_ch, top_co):
-    """Composite top layer onto base. Non-space chars overwrite."""
-    m = top_ch != " "
-    base_ch[m] = top_ch[m]; base_co[m] = top_co[m]
-    return base_ch, base_co
-
-def layer_blend(base_co, top_co, alpha):
-    """Alpha-blend top color layer onto base. alpha is float array (0-1) or scalar."""
-    if isinstance(alpha, (int, float)):
-        alpha = np.full(base_co.shape[:2], alpha, dtype=np.float32)
-    a = alpha[:,:,None]
-    return np.clip(base_co * (1 - a) + top_co * a, 0, 255).astype(np.uint8)
-
-def stamp(ch, co, text, row, col, color=(255,255,255)):
-    """Write text string at position."""
-    for i, c in enumerate(text):
-        cc = col + i
-        if 0 <= row < ch.shape[0] and 0 <= cc < ch.shape[1]:
-            ch[row, cc] = c; co[row, cc] = color
-```
-
----
-
-## Section System
-
-Map time ranges to effect functions + shader configs + grid sizes:
-
-```python
-SECTIONS = [
-    (0.0, "void"), (3.94, "starfield"), (21.0, "matrix"),
-    (46.0, "drop"), (130.0, "glitch"), (187.0, "outro"),
-]
-
-FX_DISPATCH = {"void": fx_void, "starfield": fx_starfield, ...}
-SECTION_FX = {"void": {"vignette": 0.3, "bloom": 170}, ...}
-SECTION_GRID = {"void": "md", "starfield": "sm", "drop": "lg", ...}
-SECTION_MIRROR = {"drop": "h", "bass_rings": "quad"}
-
-def get_section(t):
-    sec = SECTIONS[0][1]
-    for ts, name in SECTIONS:
-        if t >= ts: sec = name
-    return sec
-```
-
----
-
-## Parallel Encoding
-
-Split frames across N workers. Each pipes raw RGB to its own ffmpeg subprocess:
-
-```python
-def render_batch(batch_id, frame_start, frame_end, features, seg_path):
-    r = Renderer()
-    cmd = ["ffmpeg", "-y", "-f", "rawvideo", "-pix_fmt", "rgb24",
-           "-s", f"{VW}x{VH}", "-r", str(FPS), "-i", "pipe:0",
-           "-c:v", "libx264", "-preset", "fast", "-crf", "18",
-           "-pix_fmt", "yuv420p", seg_path]
-
-    # CRITICAL: stderr to file, not pipe
-    stderr_fh = open(os.path.join(workdir, f"err_{batch_id:02d}.log"), "w")
-    pipe = subprocess.Popen(cmd, stdin=subprocess.PIPE,
-                            stdout=subprocess.DEVNULL, stderr=stderr_fh)
-
-    for fi in range(frame_start, frame_end):
-        t = fi / FPS
-        sec = get_section(t)
-        f = {k: float(features[k][fi]) for k in features}
-        ch, co = FX_DISPATCH[sec](r, f, t)
-        canvas = r.render(ch, co)
-        canvas = apply_mirror(canvas, sec, f)
-        canvas = apply_shaders(canvas, sec, f, t)
-        pipe.stdin.write(canvas.tobytes())
-
-    pipe.stdin.close()
-    pipe.wait()
-    stderr_fh.close()
-```
-
-Concatenate segments + mux audio:
-
-```python
-# Write concat file
-with open(concat_path, "w") as cf:
-    for seg in segments:
-        cf.write(f"file '{seg}'\n")
-
-subprocess.run(["ffmpeg", "-y", "-f", "concat", "-safe", "0", "-i", concat_path,
-                "-i", audio_path, "-c:v", "copy", "-c:a", "aac", "-b:a", "192k",
-                "-shortest", output_path])
-```
-
-## Effect Function Contract
-
-### v2 Protocol (Current)
-
-Every scene function: `(renderer, features_dict, time_float, state_dict) -> canvas_uint8`
-
-```python
-def fx_example(r, f, t, S):
-    """Scene function returns a full pixel canvas (uint8 H,W,3).
-    Scenes have full control over multi-grid rendering and pixel-level composition.
-    """
-    # Render multiple layers at different grid densities
-    canvas_a = _render_vf(r, "md", vf_plasma, hf_angle(0.0), PAL_DENSE, f, t, S)
-    canvas_b = _render_vf(r, "sm", vf_vortex, hf_time_cycle(0.1), PAL_RUNE, f, t, S)
-
-    # Pixel-level blend
-    result = blend_canvas(canvas_a, canvas_b, "screen", 0.8)
-    return result
-```
-
-See `references/scenes.md` for the full scene protocol, the Renderer class, `_render_vf()` helper, and complete scene examples.
-
-See `references/composition.md` for blend modes, tone mapping, feedback buffers, and multi-grid composition.
-
-### v1 Protocol (Legacy)
-
-Simple scenes that use a single grid can still return `(chars, colors)` and let the caller handle rendering, but the v2 canvas protocol is preferred for all new code.
-
-```python
-def fx_simple(r, f, t, S):
-    g = r.get_grid("md")
-    val = np.sin(g.dist * 0.1 - t * 3) * f.get("bass", 0.3) * 2
-    val = np.clip(val, 0, 1); mask = val > 0.03
-    ch = val2char(val, mask, PAL_DEFAULT)
-    R, G, B = hsv2rgb(np.full_like(val, 0.6), np.full_like(val, 0.7), val)
-    co = mkc(R, G, B, g.rows, g.cols)
-    return g.render(ch, co)  # returns canvas directly
-```
-
-### Persistent State
-
-Effects that need state across frames (particles, rain columns) use the `S` dict parameter (which is `r.S` — same object, but passed explicitly for clarity):
-
-```python
-def fx_with_state(r, f, t, S):
-    if "particles" not in S:
-        S["particles"] = initialize_particles()
-    update_particles(S["particles"])
-    # ...
-```
-
-State persists across frames within a single scene/clip. Each worker process (and each scene) gets its own independent state.
-
-### Helper Functions
-
-```python
-def hsv2rgb_scalar(h, s, v):
-    """Single-value HSV to RGB. Returns (R, G, B) tuple of ints 0-255."""
-    h = h % 1.0
-    c = v * s; x = c * (1 - abs((h * 6) % 2 - 1)); m = v - c
-    if h * 6 < 1:   r, g, b = c, x, 0
-    elif h * 6 < 2:  r, g, b = x, c, 0
-    elif h * 6 < 3:  r, g, b = 0, c, x
-    elif h * 6 < 4:  r, g, b = 0, x, c
-    elif h * 6 < 5:  r, g, b = x, 0, c
-    else:             r, g, b = c, 0, x
-    return (int((r+m)*255), int((g+m)*255), int((b+m)*255))
-
-def log(msg):
-    """Print timestamped log message."""
-    print(msg, flush=True)
-```

skills/creative/ascii-video/references/composition.md

@@ -1,476 +0,0 @@
-# Composition & Brightness Reference
-
-The composable system is the core of visual complexity. It operates at three levels: pixel-level blend modes, multi-grid composition, and adaptive brightness management. This document covers all three.
-
-## Pixel-Level Blend Modes
-
-### The `blend_canvas()` Function
-
-All blending operates on full pixel canvases (`uint8 H,W,3`). Internally converts to float32 [0,1] for precision, blends, lerps by opacity, converts back.
-
-```python
-def blend_canvas(base, top, mode="normal", opacity=1.0):
-    af = base.astype(np.float32) / 255.0
-    bf = top.astype(np.float32) / 255.0
-    fn = BLEND_MODES.get(mode, BLEND_MODES["normal"])
-    result = fn(af, bf)
-    if opacity < 1.0:
-        result = af * (1 - opacity) + result * opacity
-    return np.clip(result * 255, 0, 255).astype(np.uint8)
-```
-
-### 20 Blend Modes
-
-```python
-BLEND_MODES = {
-    # Basic arithmetic
-    "normal":       lambda a, b: b,
-    "add":          lambda a, b: np.clip(a + b, 0, 1),
-    "subtract":     lambda a, b: np.clip(a - b, 0, 1),
-    "multiply":     lambda a, b: a * b,
-    "screen":       lambda a, b: 1 - (1 - a) * (1 - b),
-
-    # Contrast
-    "overlay":      lambda a, b: np.where(a < 0.5, 2*a*b, 1 - 2*(1-a)*(1-b)),
-    "softlight":    lambda a, b: (1 - 2*b)*a*a + 2*b*a,
-    "hardlight":    lambda a, b: np.where(b < 0.5, 2*a*b, 1 - 2*(1-a)*(1-b)),
-
-    # Difference
-    "difference":   lambda a, b: np.abs(a - b),
-    "exclusion":    lambda a, b: a + b - 2*a*b,
-
-    # Dodge / burn
-    "colordodge":   lambda a, b: np.clip(a / (1 - b + 1e-6), 0, 1),
-    "colorburn":    lambda a, b: np.clip(1 - (1 - a) / (b + 1e-6), 0, 1),
-
-    # Light
-    "linearlight":  lambda a, b: np.clip(a + 2*b - 1, 0, 1),
-    "vividlight":   lambda a, b: np.where(b < 0.5,
-                        np.clip(1 - (1-a)/(2*b + 1e-6), 0, 1),
-                        np.clip(a / (2*(1-b) + 1e-6), 0, 1)),
-    "pin_light":    lambda a, b: np.where(b < 0.5,
-                        np.minimum(a, 2*b), np.maximum(a, 2*b - 1)),
-    "hard_mix":     lambda a, b: np.where(a + b >= 1.0, 1.0, 0.0),
-
-    # Compare
-    "lighten":      lambda a, b: np.maximum(a, b),
-    "darken":       lambda a, b: np.minimum(a, b),
-
-    # Grain
-    "grain_extract": lambda a, b: np.clip(a - b + 0.5, 0, 1),
-    "grain_merge":  lambda a, b: np.clip(a + b - 0.5, 0, 1),
-}
-```
-
-### Blend Mode Selection Guide
-
-**Modes that brighten** (safe for dark inputs):
-- `screen` — always brightens. Two 50% gray layers screen to 75%. The go-to safe blend.
-- `add` — simple addition, clips at white. Good for sparkles, glows, particle overlays.
-- `colordodge` — extreme brightening at overlap zones. Can blow out. Use low opacity (0.3-0.5).
-- `linearlight` — aggressive brightening. Similar to add but with offset.
-
-**Modes that darken** (avoid with dark inputs):
-- `multiply` — darkens everything. Only use when both layers are already bright.
-- `overlay` — darkens when base < 0.5, brightens when base > 0.5. Crushes dark inputs: `2 * 0.12 * 0.12 = 0.03`. Use `screen` instead for dark material.
-- `colorburn` — extreme darkening at overlap zones.
-
-**Modes that create contrast**:
-- `softlight` — gentle contrast. Good for subtle texture overlay.
-- `hardlight` — strong contrast. Like overlay but keyed on the top layer.
-- `vividlight` — very aggressive contrast. Use sparingly.
-
-**Modes that create color effects**:
-- `difference` — XOR-like patterns. Two identical layers difference to black; offset layers create wild colors. Great for psychedelic looks.
-- `exclusion` — softer version of difference. Creates complementary color patterns.
-- `hard_mix` — posterizes to pure black/white/saturated color at intersections.
-
-**Modes for texture blending**:
-- `grain_extract` / `grain_merge` — extract a texture from one layer, apply it to another.
-
-### Multi-Layer Chaining
-
-```python
-# Pattern: render layers -> blend sequentially
-canvas_a = _render_vf(r, "md", vf_plasma, hf_angle(0.0), PAL_DENSE, f, t, S)
-canvas_b = _render_vf(r, "sm", vf_vortex, hf_time_cycle(0.1), PAL_RUNE, f, t, S)
-canvas_c = _render_vf(r, "lg", vf_rings, hf_distance(), PAL_BLOCKS, f, t, S)
-
-result = blend_canvas(canvas_a, canvas_b, "screen", 0.8)
-result = blend_canvas(result, canvas_c, "difference", 0.6)
-```
-
-Order matters: `screen(A, B)` is commutative, but `difference(screen(A,B), C)` differs from `difference(A, screen(B,C))`.
-
----
-
-## Multi-Grid Composition
-
-This is the core visual technique. Rendering the same conceptual scene at different grid densities (character sizes) creates natural texture interference, because characters at different scales overlap at different spatial frequencies.
-
-### Why It Works
-
-- `sm` grid (10pt font): 320x83 characters. Fine detail, dense texture.
-- `md` grid (16pt): 192x56 characters. Medium density.
-- `lg` grid (20pt): 160x45 characters. Coarse, chunky characters.
-
-When you render a plasma field on `sm` and a vortex on `lg`, then screen-blend them, the fine plasma texture shows through the gaps in the coarse vortex characters. The result has more visual complexity than either layer alone.
-
-### The `_render_vf()` Helper
-
-This is the workhorse function. It takes a value field + hue field + palette + grid, renders to a complete pixel canvas:
-
-```python
-def _render_vf(r, grid_key, val_fn, hue_fn, pal, f, t, S, sat=0.8, threshold=0.03):
-    """Render a value field + hue field to a pixel canvas via a named grid.
-
-    Args:
-        r: Renderer instance (has .get_grid())
-        grid_key: "xs", "sm", "md", "lg", "xl", "xxl"
-        val_fn: (g, f, t, S) -> float32 [0,1] array (rows, cols)
-        hue_fn: callable (g, f, t, S) -> float32 hue array, OR float scalar
-        pal: character palette string
-        f: feature dict
-        t: time in seconds
-        S: persistent state dict
-        sat: HSV saturation (0-1)
-        threshold: minimum value to render (below = space)
-
-    Returns:
-        uint8 array (VH, VW, 3) — full pixel canvas
-    """
-    g = r.get_grid(grid_key)
-    val = np.clip(val_fn(g, f, t, S), 0, 1)
-    mask = val > threshold
-    ch = val2char(val, mask, pal)
-
-    # Hue: either a callable or a fixed float
-    if callable(hue_fn):
-        h = hue_fn(g, f, t, S) % 1.0
-    else:
-        h = np.full((g.rows, g.cols), float(hue_fn), dtype=np.float32)
-
-    # CRITICAL: broadcast to full shape and copy (see Troubleshooting)
-    h = np.broadcast_to(h, (g.rows, g.cols)).copy()
-
-    R, G, B = hsv2rgb(h, np.full_like(val, sat), val)
-    co = mkc(R, G, B, g.rows, g.cols)
-    return g.render(ch, co)
-```
-
-### Grid Combination Strategies
-
-| Combination | Effect | Good For |
-|-------------|--------|----------|
-| `sm` + `lg` | Maximum contrast between fine detail and chunky blocks | Bold, graphic looks |
-| `sm` + `md` | Subtle texture layering, similar scales | Organic, flowing looks |
-| `md` + `lg` + `xs` | Three-scale interference, maximum complexity | Psychedelic, dense |
-| `sm` + `sm` (different effects) | Same scale, pattern interference only | Moire, interference |
-
-### Complete Multi-Grid Scene Example
-
-```python
-def fx_psychedelic(r, f, t, S):
-    """Three-layer multi-grid scene with beat-reactive kaleidoscope."""
-    # Layer A: plasma on medium grid with rainbow hue
-    canvas_a = _render_vf(r, "md",
-        lambda g, f, t, S: vf_plasma(g, f, t, S) * 1.3,
-        hf_angle(0.0), PAL_DENSE, f, t, S, sat=0.8)
-
-    # Layer B: vortex on small grid with cycling hue
-    canvas_b = _render_vf(r, "sm",
-        lambda g, f, t, S: vf_vortex(g, f, t, S, twist=5.0) * 1.2,
-        hf_time_cycle(0.1), PAL_RUNE, f, t, S, sat=0.7)
-
-    # Layer C: rings on large grid with distance hue
-    canvas_c = _render_vf(r, "lg",
-        lambda g, f, t, S: vf_rings(g, f, t, S, n_base=8, spacing_base=3) * 1.4,
-        hf_distance(0.3, 0.02), PAL_BLOCKS, f, t, S, sat=0.9)
-
-    # Blend: A screened with B, then difference with C
-    result = blend_canvas(canvas_a, canvas_b, "screen", 0.8)
-    result = blend_canvas(result, canvas_c, "difference", 0.6)
-
-    # Beat-triggered kaleidoscope
-    if f.get("bdecay", 0) > 0.3:
-        result = sh_kaleidoscope(result.copy(), folds=6)
-
-    return result
-```
-
----
-
-## Adaptive Tone Mapping
-
-### The Brightness Problem
-
-ASCII characters are small bright dots on a black background. Most pixels in any frame are background (black). This means:
-- Mean frame brightness is inherently low (often 5-30 out of 255)
-- Different effect combinations produce wildly different brightness levels
-- A spiral scene might be 50 mean, while a fire scene is 9 mean
-- Linear multipliers (e.g., `canvas * 2.0`) either leave dark scenes dark or blow out bright scenes
-
-### The `tonemap()` Function
-
-Replaces linear brightness multipliers with adaptive per-frame normalization + gamma correction:
-
-```python
-def tonemap(canvas, target_mean=90, gamma=0.75, black_point=2, white_point=253):
-    """Adaptive tone-mapping: normalizes + gamma-corrects so no frame is
-    fully dark or washed out.
-
-    1. Compute 1st and 99.5th percentile (ignores outlier pixels)
-    2. Stretch that range to [0, 1]
-    3. Apply gamma curve (< 1 lifts shadows, > 1 darkens)
-    4. Rescale to [black_point, white_point]
-    """
-    f = canvas.astype(np.float32)
-    lo = np.percentile(f, 1)
-    hi = np.percentile(f, 99.5)
-    if hi - lo < 10:
-        hi = max(hi, lo + 10)  # near-uniform frame fallback
-    f = np.clip((f - lo) / (hi - lo), 0.0, 1.0)
-    f = np.power(f, gamma)
-    f = f * (white_point - black_point) + black_point
-    return np.clip(f, 0, 255).astype(np.uint8)
-```
-
-### Why Gamma, Not Linear
-
-Linear multiplier `* 2.0`:
-```
-input 10  -> output 20   (still dark)
-input 100 -> output 200  (ok)
-input 200 -> output 255  (clipped, lost detail)
-```
-
-Gamma 0.75 after normalization:
-```
-input 0.04 -> output 0.08 (lifted from invisible to visible)
-input 0.39 -> output 0.50 (moderate lift)
-input 0.78 -> output 0.84 (gentle lift, no clipping)
-```
-
-Gamma < 1 compresses the highlights and expands the shadows. This is exactly what we need: lift dark ASCII content into visibility without blowing out the bright parts.
-
-### Pipeline Ordering
-
-The pipeline in `render_clip()` is:
-
-```
-scene_fn(r, f, t, S)  ->  canvas
-         |
-    tonemap(canvas, gamma=scene_gamma)
-         |
-    FeedbackBuffer.apply(canvas, ...)
-         |
-    ShaderChain.apply(canvas, f=f, t=t)
-         |
-    ffmpeg pipe
-```
-
-Tonemap runs BEFORE feedback and shaders. This means:
-- Feedback operates on normalized data (consistent behavior regardless of scene brightness)
-- Shaders like solarize, posterize, contrast operate on properly-ranged data
-- The brightness shader in the chain is no longer needed (tonemap handles it)
-
-### Per-Scene Gamma Tuning
-
-Default gamma is 0.75. Scenes that apply destructive post-processing need more aggressive lift because the destruction happens after tonemap:
-
-| Scene Type | Recommended Gamma | Why |
-|------------|-------------------|-----|
-| Standard effects | 0.75 | Default, works for most scenes |
-| Solarize post-process | 0.50-0.60 | Solarize inverts bright pixels, reducing overall brightness |
-| Posterize post-process | 0.50-0.55 | Posterize quantizes, often crushing mid-values to black |
-| Heavy difference blending | 0.60-0.70 | Difference mode creates many near-zero pixels |
-| Already bright scenes | 0.85-1.0 | Don't over-boost scenes that are naturally bright |
-
-Configure via the scene table:
-
-```python
-SCENES = [
-    {"start": 9.17, "end": 11.25, "name": "fire", "gamma": 0.55,
-     "fx": fx_fire, "shaders": [("solarize", {"threshold": 200}), ...]},
-    {"start": 25.96, "end": 27.29, "name": "diamond", "gamma": 0.5,
-     "fx": fx_diamond, "shaders": [("bloom", {"thr": 90}), ...]},
-]
-```
-
-### Brightness Verification
-
-After rendering, spot-check frame brightness:
-
-```python
-# In test-frame mode
-canvas = scene["fx"](r, feat, t, r.S)
-canvas = tonemap(canvas, gamma=scene.get("gamma", 0.75))
-chain = ShaderChain()
-for sn, kw in scene.get("shaders", []):
-    chain.add(sn, **kw)
-canvas = chain.apply(canvas, f=feat, t=t)
-print(f"Mean brightness: {canvas.astype(float).mean():.1f}, max: {canvas.max()}")
-```
-
-Target ranges after tonemap + shaders:
-- Quiet/ambient scenes: mean 30-60
-- Active scenes: mean 40-100
-- Climax/peak scenes: mean 60-150
-- If mean < 20: gamma is too high or a shader is destroying brightness
-- If mean > 180: gamma is too low or add is stacking too much
-
----
-
-## FeedbackBuffer Spatial Transforms
-
-The feedback buffer stores the previous frame and blends it into the current frame with decay. Spatial transforms applied to the buffer before blending create the illusion of motion in the feedback trail.
-
-### Implementation
-
-```python
-class FeedbackBuffer:
-    def __init__(self):
-        self.buf = None
-
-    def apply(self, canvas, decay=0.85, blend="screen", opacity=0.5,
-              transform=None, transform_amt=0.02, hue_shift=0.0):
-        if self.buf is None:
-            self.buf = canvas.astype(np.float32) / 255.0
-            return canvas
-
-        # Decay old buffer
-        self.buf *= decay
-
-        # Spatial transform
-        if transform:
-            self.buf = self._transform(self.buf, transform, transform_amt)
-
-        # Hue shift the feedback for rainbow trails
-        if hue_shift > 0:
-            self.buf = self._hue_shift(self.buf, hue_shift)
-
-        # Blend feedback into current frame
-        result = blend_canvas(canvas,
-                              np.clip(self.buf * 255, 0, 255).astype(np.uint8),
-                              blend, opacity)
-
-        # Update buffer with current frame
-        self.buf = result.astype(np.float32) / 255.0
-        return result
-
-    def _transform(self, buf, transform, amt):
-        h, w = buf.shape[:2]
-        if transform == "zoom":
-            # Zoom in: sample from slightly inside (creates expanding tunnel)
-            m = int(h * amt); n = int(w * amt)
-            if m > 0 and n > 0:
-                cropped = buf[m:-m or None, n:-n or None]
-                # Resize back to full (nearest-neighbor for speed)
-                buf = np.array(Image.fromarray(
-                    np.clip(cropped * 255, 0, 255).astype(np.uint8)
-                ).resize((w, h), Image.NEAREST)).astype(np.float32) / 255.0
-        elif transform == "shrink":
-            # Zoom out: pad edges, shrink center
-            m = int(h * amt); n = int(w * amt)
-            small = np.array(Image.fromarray(
-                np.clip(buf * 255, 0, 255).astype(np.uint8)
-            ).resize((w - 2*n, h - 2*m), Image.NEAREST))
-            new = np.zeros((h, w, 3), dtype=np.uint8)
-            new[m:m+small.shape[0], n:n+small.shape[1]] = small
-            buf = new.astype(np.float32) / 255.0
-        elif transform == "rotate_cw":
-            # Small clockwise rotation via affine
-            angle = amt * 10  # amt=0.005 -> 0.05 degrees per frame
-            cy, cx = h / 2, w / 2
-            Y = np.arange(h, dtype=np.float32)[:, None]
-            X = np.arange(w, dtype=np.float32)[None, :]
-            cos_a, sin_a = np.cos(angle), np.sin(angle)
-            sx = (X - cx) * cos_a + (Y - cy) * sin_a + cx
-            sy = -(X - cx) * sin_a + (Y - cy) * cos_a + cy
-            sx = np.clip(sx.astype(int), 0, w - 1)
-            sy = np.clip(sy.astype(int), 0, h - 1)
-            buf = buf[sy, sx]
-        elif transform == "rotate_ccw":
-            angle = -amt * 10
-            cy, cx = h / 2, w / 2
-            Y = np.arange(h, dtype=np.float32)[:, None]
-            X = np.arange(w, dtype=np.float32)[None, :]
-            cos_a, sin_a = np.cos(angle), np.sin(angle)
-            sx = (X - cx) * cos_a + (Y - cy) * sin_a + cx
-            sy = -(X - cx) * sin_a + (Y - cy) * cos_a + cy
-            sx = np.clip(sx.astype(int), 0, w - 1)
-            sy = np.clip(sy.astype(int), 0, h - 1)
-            buf = buf[sy, sx]
-        elif transform == "shift_up":
-            pixels = max(1, int(h * amt))
-            buf = np.roll(buf, -pixels, axis=0)
-            buf[-pixels:] = 0  # black fill at bottom
-        elif transform == "shift_down":
-            pixels = max(1, int(h * amt))
-            buf = np.roll(buf, pixels, axis=0)
-            buf[:pixels] = 0
-        elif transform == "mirror_h":
-            buf = buf[:, ::-1]
-        return buf
-
-    def _hue_shift(self, buf, amount):
-        """Rotate hues of the feedback buffer. Operates on float32 [0,1]."""
-        rgb = np.clip(buf * 255, 0, 255).astype(np.uint8)
-        hsv = np.zeros_like(buf)
-        # Simple approximate RGB->HSV->shift->RGB
-        r, g, b = buf[:,:,0], buf[:,:,1], buf[:,:,2]
-        mx = np.maximum(np.maximum(r, g), b)
-        mn = np.minimum(np.minimum(r, g), b)
-        delta = mx - mn + 1e-10
-        # Hue
-        h = np.where(mx == r, ((g - b) / delta) % 6,
-            np.where(mx == g, (b - r) / delta + 2, (r - g) / delta + 4))
-        h = (h / 6 + amount) % 1.0
-        # Reconstruct with shifted hue (simplified)
-        s = delta / (mx + 1e-10)
-        v = mx
-        c = v * s; x = c * (1 - np.abs((h * 6) % 2 - 1)); m = v - c
-        ro = np.zeros_like(h); go = np.zeros_like(h); bo = np.zeros_like(h)
-        for lo, hi, rv, gv, bv in [(0,1,c,x,0),(1,2,x,c,0),(2,3,0,c,x),
-                                     (3,4,0,x,c),(4,5,x,0,c),(5,6,c,0,x)]:
-            mask = ((h*6) >= lo) & ((h*6) < hi)
-            ro[mask] = rv[mask] if not isinstance(rv, (int,float)) else rv
-            go[mask] = gv[mask] if not isinstance(gv, (int,float)) else gv
-            bo[mask] = bv[mask] if not isinstance(bv, (int,float)) else bv
-        return np.stack([ro+m, go+m, bo+m], axis=2)
-```
-
-### Feedback Presets
-
-| Preset | Config | Visual Effect |
-|--------|--------|---------------|
-| Infinite zoom tunnel | `decay=0.8, blend="screen", transform="zoom", transform_amt=0.015` | Expanding ring patterns |
-| Rainbow trails | `decay=0.7, blend="screen", transform="zoom", transform_amt=0.01, hue_shift=0.02` | Psychedelic color trails |
-| Ghostly echo | `decay=0.9, blend="add", opacity=0.15, transform="shift_up", transform_amt=0.01` | Faint upward smearing |
-| Kaleidoscopic recursion | `decay=0.75, blend="screen", transform="rotate_cw", transform_amt=0.005, hue_shift=0.01` | Rotating mandala feedback |
-| Color evolution | `decay=0.8, blend="difference", opacity=0.4, hue_shift=0.03` | Frame-to-frame color XOR |
-| Rising heat haze | `decay=0.5, blend="add", opacity=0.2, transform="shift_up", transform_amt=0.02` | Hot air shimmer |
-
----
-
-## PixelBlendStack
-
-Higher-level wrapper for multi-layer compositing:
-
-```python
-class PixelBlendStack:
-    def __init__(self):
-        self.layers = []
-
-    def add(self, canvas, mode="normal", opacity=1.0):
-        self.layers.append((canvas, mode, opacity))
-        return self
-
-    def composite(self):
-        if not self.layers:
-            return np.zeros((VH, VW, 3), dtype=np.uint8)
-        result = self.layers[0][0]
-        for canvas, mode, opacity in self.layers[1:]:
-            result = blend_canvas(result, canvas, mode, opacity)
-        return result
-```

skills/creative/ascii-video/references/effects.md

@@ -1,893 +0,0 @@
-# Effect Catalog
-
-Effect building blocks that produce visual patterns. In v2, these are used **inside scene functions** that return a pixel canvas directly. The building blocks below operate on grid coordinate arrays and produce `(chars, colors)` or value/hue fields that the scene function renders to canvas via `_render_vf()`. See `composition.md` for the v2 rendering pattern and `scenes.md` for scene function examples.
-
-## Design Philosophy
-
-Effects are the creative core. Don't copy these verbatim for every project -- use them as **building blocks** and **combine, modify, and invent** new ones. Every project should feel distinct.
-
-Key principles:
-- **Layer multiple effects** rather than using a single monolithic function
-- **Parameterize everything** -- hue, speed, density, amplitude should all be arguments
-- **React to features** -- audio/video features should modulate at least 2-3 parameters per effect
-- **Vary per section** -- never use the same effect config for the entire video
-- **Invent project-specific effects** -- the catalog below is a starting vocabulary, not a fixed set
-
----
-
-## Background Fills
-
-Every effect should start with a background. Never leave flat black.
-
-### Animated Sine Field (General Purpose)
-```python
-def bg_sinefield(g, f, t, hue=0.6, bri=0.5, pal=PAL_DEFAULT,
-                 freq=(0.13, 0.17, 0.07, 0.09), speed=(0.5, -0.4, -0.3, 0.2)):
-    """Layered sine field. Adjust freq/speed tuples for different textures."""
-    v1 = np.sin(g.cc*freq[0] + t*speed[0]) * np.sin(g.rr*freq[1] - t*speed[1]) * 0.5 + 0.5
-    v2 = np.sin(g.cc*freq[2] - t*speed[2] + g.rr*freq[3]) * 0.4 + 0.5
-    v3 = np.sin(g.dist_n*5 + t*0.2) * 0.3 + 0.4
-    v4 = np.cos(g.angle*3 - t*0.6) * 0.15 + 0.5
-    val = np.clip((v1*0.3 + v2*0.25 + v3*0.25 + v4*0.2) * bri * (0.6 + f["rms"]*0.6), 0.06, 1)
-    mask = val > 0.03
-    ch = val2char(val, mask, pal)
-    h = np.full_like(val, hue) + f.get("cent", 0.5)*0.1 + val*0.08
-    R, G, B = hsv2rgb(h, np.clip(0.35+f.get("flat",0.4)*0.4, 0, 1) * np.ones_like(val), val)
-    return ch, mkc(R, G, B, g.rows, g.cols)
-```
-
-### Video-Source Background
-```python
-def bg_video(g, frame_rgb, pal=PAL_DEFAULT, brightness=0.5):
-    small = np.array(Image.fromarray(frame_rgb).resize((g.cols, g.rows)))
-    lum = np.mean(small, axis=2) / 255.0 * brightness
-    mask = lum > 0.02
-    ch = val2char(lum, mask, pal)
-    co = np.clip(small * np.clip(lum[:,:,None]*1.5+0.3, 0.3, 1), 0, 255).astype(np.uint8)
-    return ch, co
-```
-
-### Noise / Static Field
-```python
-def bg_noise(g, f, t, pal=PAL_BLOCKS, density=0.3, hue_drift=0.02):
-    val = np.random.random((g.rows, g.cols)).astype(np.float32) * density * (0.5 + f["rms"]*0.5)
-    val = np.clip(val, 0, 1); mask = val > 0.02
-    ch = val2char(val, mask, pal)
-    R, G, B = hsv2rgb(np.full_like(val, t*hue_drift % 1), np.full_like(val, 0.3), val)
-    return ch, mkc(R, G, B, g.rows, g.cols)
-```
-
-### Perlin-Like Smooth Noise
-```python
-def bg_smooth_noise(g, f, t, hue=0.5, bri=0.5, pal=PAL_DOTS, octaves=3):
-    """Layered sine approximation of Perlin noise. Cheap, smooth, organic."""
-    val = np.zeros((g.rows, g.cols), dtype=np.float32)
-    for i in range(octaves):
-        freq = 0.05 * (2 ** i)
-        amp = 0.5 / (i + 1)
-        phase = t * (0.3 + i * 0.2)
-        val += np.sin(g.cc * freq + phase) * np.cos(g.rr * freq * 0.7 - phase * 0.5) * amp
-    val = np.clip(val * 0.5 + 0.5, 0, 1) * bri
-    mask = val > 0.03
-    ch = val2char(val, mask, pal)
-    h = np.full_like(val, hue) + val * 0.1
-    R, G, B = hsv2rgb(h, np.full_like(val, 0.5), val)
-    return ch, mkc(R, G, B, g.rows, g.cols)
-```
-
-### Cellular / Voronoi Approximation
-```python
-def bg_cellular(g, f, t, n_centers=12, hue=0.5, bri=0.6, pal=PAL_BLOCKS):
-    """Voronoi-like cells using distance to nearest of N moving centers."""
-    rng = np.random.RandomState(42)  # deterministic centers
-    cx = (rng.rand(n_centers) * g.cols).astype(np.float32)
-    cy = (rng.rand(n_centers) * g.rows).astype(np.float32)
-    # Animate centers
-    cx_t = cx + np.sin(t * 0.5 + np.arange(n_centers) * 0.7) * 5
-    cy_t = cy + np.cos(t * 0.4 + np.arange(n_centers) * 0.9) * 3
-    # Min distance to any center
-    min_d = np.full((g.rows, g.cols), 999.0, dtype=np.float32)
-    for i in range(n_centers):
-        d = np.sqrt((g.cc - cx_t[i])**2 + (g.rr - cy_t[i])**2)
-        min_d = np.minimum(min_d, d)
-    val = np.clip(1.0 - min_d / (g.cols * 0.3), 0, 1) * bri
-    # Cell edges (where distance is near-equal between two centers)
-    # ... second-nearest trick for edge highlighting
-    mask = val > 0.03
-    ch = val2char(val, mask, pal)
-    R, G, B = hsv2rgb(np.full_like(val, hue) + min_d * 0.005, np.full_like(val, 0.5), val)
-    return ch, mkc(R, G, B, g.rows, g.cols)
-```
-
----
-
-## Radial Effects
-
-### Concentric Rings
-Bass/sub-driven pulsing rings from center. Scale ring count and thickness with bass energy.
-```python
-def eff_rings(g, f, t, hue=0.5, n_base=6, pal=PAL_DEFAULT):
-    n_rings = int(n_base + f["sub_r"] * 25 + f["bass"] * 10)
-    spacing = 2 + f["bass_r"] * 7 + f["rms"] * 3
-    ring_cv = np.zeros((g.rows, g.cols), dtype=np.float32)
-    for ri in range(n_rings):
-        rad = (ri+1) * spacing + f["bdecay"] * 15
-        wobble = f["mid_r"]*5*np.sin(g.angle*3 + t*4) + f["hi_r"]*3*np.sin(g.angle*7 - t*6)
-        rd = np.abs(g.dist - rad - wobble)
-        th = 1 + f["sub"] * 3
-        ring_cv = np.maximum(ring_cv, np.clip((1 - rd/th) * (0.4 + f["bass"]*0.8), 0, 1))
-    # Color by angle + distance for rainbow rings
-    h = g.angle/(2*np.pi) + g.dist*0.005 + f["sub_r"]*0.2
-    return ring_cv, h
-```
-
-### Radial Rays
-```python
-def eff_rays(g, f, t, n_base=8, hue=0.5):
-    n_rays = int(n_base + f["hi_r"] * 25)
-    ray = np.clip(np.cos(g.angle*n_rays + t*3) * f["bdecay"]*0.6 * (1-g.dist_n), 0, 0.7)
-    return ray
-```
-
-### Spiral Arms (Logarithmic)
-```python
-def eff_spiral(g, f, t, n_arms=3, tightness=2.5, hue=0.5):
-    arm_cv = np.zeros((g.rows, g.cols), dtype=np.float32)
-    for ai in range(n_arms):
-        offset = ai * 2*np.pi / n_arms
-        log_r = np.log(g.dist + 1) * tightness
-        arm_phase = g.angle + offset - log_r + t * 0.8
-        arm_val = np.clip(np.cos(arm_phase * n_arms) * 0.6 + 0.2, 0, 1)
-        arm_val *= (0.4 + f["rms"]*0.6) * np.clip(1 - g.dist_n*0.5, 0.2, 1)
-        arm_cv = np.maximum(arm_cv, arm_val)
-    return arm_cv
-```
-
-### Center Glow / Pulse
-```python
-def eff_glow(g, f, t, intensity=0.6, spread=2.0):
-    return np.clip(intensity * np.exp(-g.dist_n * spread) * (0.5 + f["rms"]*2 + np.sin(t*1.2)*0.2), 0, 0.9)
-```
-
-### Tunnel / Depth
-```python
-def eff_tunnel(g, f, t, speed=3.0, complexity=6):
-    tunnel_d = 1.0 / (g.dist_n + 0.1)
-    v1 = np.sin(tunnel_d*2 - t*speed) * 0.45 + 0.55
-    v2 = np.sin(g.angle*complexity + tunnel_d*1.5 - t*2) * 0.35 + 0.55
-    return v1 * 0.5 + v2 * 0.5
-```
-
-### Vortex (Rotating Distortion)
-```python
-def eff_vortex(g, f, t, twist=3.0, pulse=True):
-    """Twisting radial pattern -- distance modulates angle."""
-    twisted = g.angle + g.dist_n * twist * np.sin(t * 0.5)
-    val = np.sin(twisted * 4 - t * 2) * 0.5 + 0.5
-    if pulse:
-        val *= 0.5 + f.get("bass", 0.3) * 0.8
-    return np.clip(val, 0, 1)
-```
-
----
-
-## Wave Effects
-
-### Multi-Band Frequency Waves
-Each frequency band draws its own wave at different spatial/temporal frequencies:
-```python
-def eff_freq_waves(g, f, t, bands=None):
-    if bands is None:
-        bands = [("sub",0.06,1.2,0.0), ("bass",0.10,2.0,0.08), ("lomid",0.15,3.0,0.16),
-                 ("mid",0.22,4.5,0.25), ("himid",0.32,6.5,0.4), ("hi",0.45,8.5,0.55)]
-    mid = g.rows / 2.0
-    composite = np.zeros((g.rows, g.cols), dtype=np.float32)
-    for band_key, sf, tf, hue_base in bands:
-        amp = f.get(band_key, 0.3) * g.rows * 0.4
-        y_wave = mid - np.sin(g.cc*sf + t*tf) * amp
-        y_wave += np.sin(g.cc*sf*2.3 + t*tf*1.7) * amp * 0.2  # harmonic
-        dist = np.abs(g.rr - y_wave)
-        thickness = 2 + f.get(band_key, 0.3) * 5
-        intensity = np.clip((1 - dist/thickness) * f.get(band_key, 0.3) * 1.5, 0, 1)
-        composite = np.maximum(composite, intensity)
-    return composite
-```
-
-### Interference Pattern
-6-8 overlapping sine waves creating moire-like patterns:
-```python
-def eff_interference(g, f, t, n_waves=5):
-    """Parametric interference -- vary n_waves for complexity."""
-    # Each wave has different orientation, frequency, and feature driver
-    drivers = ["mid_r", "himid_r", "bass_r", "lomid_r", "hi_r"]
-    vals = np.zeros((g.rows, g.cols), dtype=np.float32)
-    for i in range(min(n_waves, len(drivers))):
-        angle = i * np.pi / n_waves  # spread orientations
-        freq = 0.06 + i * 0.03
-        sp = 0.5 + i * 0.3
-        proj = g.cc * np.cos(angle) + g.rr * np.sin(angle)
-        vals += np.sin(proj * freq + t * sp) * f.get(drivers[i], 0.3) * 2.5
-    return np.clip(vals * 0.12 + 0.45, 0.1, 1)
-```
-
-### Aurora / Horizontal Bands
-```python
-def eff_aurora(g, f, t, hue=0.4, n_bands=3):
-    val = np.zeros((g.rows, g.cols), dtype=np.float32)
-    for i in range(n_bands):
-        freq_r = 0.08 + i * 0.04
-        freq_c = 0.012 + i * 0.008
-        sp_r = 0.7 + i * 0.3
-        sp_c = 0.18 + i * 0.12
-        val += np.sin(g.rr*freq_r + t*sp_r) * np.sin(g.cc*freq_c + t*sp_c) * (0.6 / n_bands)
-    return np.clip(val * (f.get("lomid_r", 0.3)*3 + 0.2), 0, 0.7)
-```
-
-### Ripple (Point-Source Waves)
-```python
-def eff_ripple(g, f, t, sources=None, freq=0.3, damping=0.02):
-    """Concentric ripples from point sources. Sources = [(row_frac, col_frac), ...]"""
-    if sources is None:
-        sources = [(0.5, 0.5)]  # center
-    val = np.zeros((g.rows, g.cols), dtype=np.float32)
-    for ry, rx in sources:
-        dy = g.rr - g.rows * ry
-        dx = g.cc - g.cols * rx
-        d = np.sqrt(dy**2 + dx**2)
-        val += np.sin(d * freq - t * 4) * np.exp(-d * damping) * 0.5
-    return np.clip(val + 0.5, 0, 1)
-```
-
----
-
-## Particle Systems
-
-### General Pattern
-All particle systems use persistent state:
-```python
-S = state  # dict persisted across frames
-if "px" not in S:
-    S["px"]=[]; S["py"]=[]; S["vx"]=[]; S["vy"]=[]; S["life"]=[]; S["char"]=[]
-
-# Emit new particles (on beat, continuously, or on trigger)
-# Update: position += velocity, apply forces, decay life
-# Draw: map to grid, set char/color based on life
-# Cull: remove dead, cap total count
-```
-
-### Particle Character Sets
-
-Don't hardcode particle chars. Choose per project/mood:
-
-```python
-# Energy / explosive
-PART_ENERGY  = list("*+#@\u26a1\u2726\u2605\u2588\u2593")
-PART_SPARK   = list("\u00b7\u2022\u25cf\u2605\u2736*+")
-# Organic / natural
-PART_LEAF    = list("\u2740\u2741\u2742\u2743\u273f\u2618\u2022")
-PART_SNOW    = list("\u2744\u2745\u2746\u00b7\u2022*\u25cb")
-PART_RAIN    = list("|\u2502\u2503\u2551/\\")
-PART_BUBBLE  = list("\u25cb\u25ce\u25c9\u25cf\u2218\u2219\u00b0")
-# Data / tech
-PART_DATA    = list("01{}[]<>|/\\")
-PART_HEX     = list("0123456789ABCDEF")
-PART_BINARY  = list("01")
-# Mystical
-PART_RUNE    = list("\u16a0\u16a2\u16a6\u16b1\u16b7\u16c1\u16c7\u16d2\u16d6\u16da\u16de\u16df\u2726\u2605")
-PART_ZODIAC  = list("\u2648\u2649\u264a\u264b\u264c\u264d\u264e\u264f\u2650\u2651\u2652\u2653")
-# Minimal
-PART_DOT     = list("\u00b7\u2022\u25cf")
-PART_DASH    = list("-=~\u2500\u2550")
-```
-
-### Explosion (Beat-Triggered)
-```python
-def emit_explosion(S, f, center_r, center_c, char_set=PART_ENERGY, count_base=80):
-    if f.get("beat", 0) > 0:
-        for _ in range(int(count_base + f["rms"]*150)):
-            ang = random.uniform(0, 2*math.pi)
-            sp = random.uniform(1, 9) * (0.5 + f.get("sub_r", 0.3)*2)
-            S["px"].append(float(center_c))
-            S["py"].append(float(center_r))
-            S["vx"].append(math.cos(ang)*sp*2.5)
-            S["vy"].append(math.sin(ang)*sp)
-            S["life"].append(1.0)
-            S["char"].append(random.choice(char_set))
-# Update: gravity on vy += 0.03, life -= 0.015
-# Color: life * 255 for brightness, hue fade controlled by caller
-```
-
-### Rising Embers
-```python
-# Emit: sy = rows-1, vy = -random.uniform(1,5), vx = random.uniform(-1.5,1.5)
-# Update: vx += random jitter * 0.3, life -= 0.01
-# Cap at ~1500 particles
-```
-
-### Dissolving Cloud
-```python
-# Init: N=600 particles spread across screen
-# Update: slow upward drift, fade life progressively
-# life -= 0.002 * (1 + elapsed * 0.05)  # accelerating fade
-```
-
-### Starfield (3D Projection)
-```python
-# N stars with (sx, sy, sz) in normalized coords
-# Move: sz -= speed (stars approach camera)
-# Project: px = cx + sx/sz * cx, py = cy + sy/sz * cy
-# Reset stars that pass camera (sz <= 0.01)
-# Brightness = (1 - sz), draw streaks behind bright stars
-```
-
-### Orbit (Circular/Elliptical Motion)
-```python
-def emit_orbit(S, n=20, radius=15, speed=1.0, char_set=PART_DOT):
-    """Particles orbiting a center point."""
-    for i in range(n):
-        angle = i * 2 * math.pi / n
-        S["px"].append(0.0); S["py"].append(0.0)  # will be computed from angle
-        S["vx"].append(angle)  # store angle as "vx" for orbit
-        S["vy"].append(radius + random.uniform(-2, 2))  # store radius
-        S["life"].append(1.0)
-        S["char"].append(random.choice(char_set))
-# Update: angle += speed * dt, px = cx + radius * cos(angle), py = cy + radius * sin(angle)
-```
-
-### Gravity Well
-```python
-# Particles attracted toward one or more gravity points
-# Update: compute force vector toward each well, apply as acceleration
-# Particles that reach well center respawn at edges
-```
-
----
-
-## Rain / Matrix Effects
-
-### Column Rain (Vectorized)
-```python
-def eff_matrix_rain(g, f, t, state, hue=0.33, bri=0.6, pal=PAL_KATA,
-                    speed_base=0.5, speed_beat=3.0):
-    """Vectorized matrix rain. state dict persists column positions."""
-    if "ry" not in state or len(state["ry"]) != g.cols:
-        state["ry"] = np.random.uniform(-g.rows, g.rows, g.cols).astype(np.float32)
-        state["rsp"] = np.random.uniform(0.3, 2.0, g.cols).astype(np.float32)
-        state["rln"] = np.random.randint(8, 40, g.cols)
-        state["rch"] = np.random.randint(0, len(pal), (g.rows, g.cols))  # pre-assign chars
-
-    speed_mult = speed_base + f.get("bass", 0.3)*speed_beat + f.get("sub_r", 0.3)*3
-    if f.get("beat", 0) > 0: speed_mult *= 2.5
-    state["ry"] += state["rsp"] * speed_mult
-
-    # Reset columns that fall past bottom
-    rst = (state["ry"] - state["rln"]) > g.rows
-    state["ry"][rst] = np.random.uniform(-25, -2, rst.sum())
-
-    # Vectorized draw using fancy indexing
-    ch = np.full((g.rows, g.cols), " ", dtype="U1")
-    co = np.zeros((g.rows, g.cols, 3), dtype=np.uint8)
-    heads = state["ry"].astype(int)
-    for c in range(g.cols):
-        head = heads[c]
-        trail_len = state["rln"][c]
-        for i in range(trail_len):
-            row = head - i
-            if 0 <= row < g.rows:
-                fade = 1.0 - i / trail_len
-                ci = state["rch"][row, c] % len(pal)
-                ch[row, c] = pal[ci]
-                v = fade * bri * 255
-                if i == 0:  # head is bright white-ish
-                    co[row, c] = (int(v*0.9), int(min(255, v*1.1)), int(v*0.9))
-                else:
-                    R, G, B = hsv2rgb_single(hue, 0.7, fade * bri)
-                    co[row, c] = (R, G, B)
-    return ch, co, state
-```
-
----
-
-## Glitch / Data Effects
-
-### Horizontal Band Displacement
-```python
-def eff_glitch_displace(ch, co, f, intensity=1.0):
-    n_bands = int(8 + f.get("flux", 0.3)*25 + f.get("bdecay", 0)*15) * intensity
-    for _ in range(int(n_bands)):
-        y = random.randint(0, ch.shape[0]-1)
-        h = random.randint(1, int(3 + f.get("sub", 0.3)*8))
-        shift = int((random.random()-0.5) * f.get("rms", 0.3)*40 + f.get("bdecay", 0)*20*(random.random()-0.5))
-        if shift != 0:
-            for row in range(h):
-                rr = y + row
-                if 0 <= rr < ch.shape[0]:
-                    ch[rr] = np.roll(ch[rr], shift)
-                    co[rr] = np.roll(co[rr], shift, axis=0)
-    return ch, co
-```
-
-### Block Corruption
-```python
-def eff_block_corrupt(ch, co, f, char_pool=None, count_base=20):
-    if char_pool is None:
-        char_pool = list(PAL_BLOCKS[4:] + PAL_KATA[2:8])
-    for _ in range(int(count_base + f.get("flux", 0.3)*60 + f.get("bdecay", 0)*40)):
-        bx = random.randint(0, max(1, ch.shape[1]-6))
-        by = random.randint(0, max(1, ch.shape[0]-4))
-        bw, bh = random.randint(2,6), random.randint(1,4)
-        block_char = random.choice(char_pool)
-        # Fill rectangle with single char and random color
-        for r in range(bh):
-            for c in range(bw):
-                rr, cc = by+r, bx+c
-                if 0 <= rr < ch.shape[0] and 0 <= cc < ch.shape[1]:
-                    ch[rr, cc] = block_char
-                    co[rr, cc] = (random.randint(100,255), random.randint(0,100), random.randint(0,80))
-    return ch, co
-```
-
-### Scan Bars (Vertical)
-```python
-def eff_scanbars(ch, co, f, t, n_base=4, chars="|\u2551|!1l"):
-    for bi in range(int(n_base + f.get("himid_r", 0.3)*12)):
-        sx = int((t*50*(1+bi*0.3) + bi*37) % ch.shape[1])
-        for rr in range(ch.shape[0]):
-            if random.random() < 0.7:
-                ch[rr, sx] = random.choice(chars)
-    return ch, co
-```
-
-### Error Messages
-```python
-# Parameterize the error vocabulary per project:
-ERRORS_TECH = ["SEGFAULT","0xDEADBEEF","BUFFER_OVERRUN","PANIC!","NULL_PTR",
-               "CORRUPT","SIGSEGV","ERR_OVERFLOW","STACK_SMASH","BAD_ALLOC"]
-ERRORS_COSMIC = ["VOID_BREACH","ENTROPY_MAX","SINGULARITY","DIMENSION_FAULT",
-                 "REALITY_ERR","TIME_PARADOX","DARK_MATTER_LEAK","QUANTUM_DECOHERE"]
-ERRORS_ORGANIC = ["CELL_DIVISION_ERR","DNA_MISMATCH","MUTATION_OVERFLOW",
-                  "NEURAL_DEADLOCK","SYNAPSE_TIMEOUT","MEMBRANE_BREACH"]
-```
-
-### Hex Data Stream
-```python
-hex_str = "".join(random.choice("0123456789ABCDEF") for _ in range(random.randint(8,20)))
-stamp(ch, co, hex_str, rand_row, rand_col, (0, 160, 80))
-```
-
----
-
-## Spectrum / Visualization
-
-### Mirrored Spectrum Bars
-```python
-def eff_spectrum(g, f, t, n_bars=64, pal=PAL_BLOCKS, mirror=True):
-    bar_w = max(1, g.cols // n_bars); mid = g.rows // 2
-    band_vals = np.array([f.get("sub",0.3), f.get("bass",0.3), f.get("lomid",0.3),
-                          f.get("mid",0.3), f.get("himid",0.3), f.get("hi",0.3)])
-    ch = np.full((g.rows, g.cols), " ", dtype="U1")
-    co = np.zeros((g.rows, g.cols, 3), dtype=np.uint8)
-    for b in range(n_bars):
-        frac = b / n_bars
-        fi = frac * 5; lo_i = int(fi); hi_i = min(lo_i+1, 5)
-        bval = min(1, (band_vals[lo_i]*(1-fi%1) + band_vals[hi_i]*(fi%1)) * 1.8)
-        height = int(bval * (g.rows//2 - 2))
-        for dy in range(height):
-            hue = (f.get("cent",0.5)*0.3 + frac*0.3 + dy/max(height,1)*0.15) % 1.0
-            ci = pal[min(int(dy/max(height,1)*len(pal)*0.7+len(pal)*0.2), len(pal)-1)]
-            for dc in range(bar_w - (1 if bar_w > 2 else 0)):
-                cc = b*bar_w + dc
-                if 0 <= cc < g.cols:
-                    rows_to_draw = [mid - dy, mid + dy] if mirror else [g.rows - 1 - dy]
-                    for row in rows_to_draw:
-                        if 0 <= row < g.rows:
-                            ch[row, cc] = ci
-                            co[row, cc] = hsv_to_rgb_single(hue, 0.85, 0.5+dy/max(height,1)*0.5)
-    return ch, co
-```
-
-### Waveform
-```python
-def eff_waveform(g, f, t, row_offset=-5, hue=0.1):
-    ch = np.full((g.rows, g.cols), " ", dtype="U1")
-    co = np.zeros((g.rows, g.cols, 3), dtype=np.uint8)
-    for c in range(g.cols):
-        wv = (math.sin(c*0.15+t*5)*f.get("bass",0.3)*0.5
-            + math.sin(c*0.3+t*8)*f.get("mid",0.3)*0.3
-            + math.sin(c*0.6+t*12)*f.get("hi",0.3)*0.15)
-        wr = g.rows + row_offset + int(wv * 4)
-        if 0 <= wr < g.rows:
-            ch[wr, c] = "~"
-            v = int(120 + f.get("rms",0.3)*135)
-            co[wr, c] = [v, int(v*0.7), int(v*0.4)]
-    return ch, co
-```
-
----
-
-## Fire / Lava
-
-### Fire Columns
-```python
-def eff_fire(g, f, t, n_base=20, hue_base=0.02, hue_range=0.12, pal=PAL_BLOCKS):
-    n_cols = int(n_base + f.get("bass",0.3)*30 + f.get("sub_r",0.3)*20)
-    ch = np.full((g.rows, g.cols), " ", dtype="U1")
-    co = np.zeros((g.rows, g.cols, 3), dtype=np.uint8)
-    for fi in range(n_cols):
-        fx_c = int((fi*g.cols/n_cols + np.sin(t*2+fi*0.7)*3) % g.cols)
-        height = int((f.get("bass",0.3)*0.4 + f.get("sub_r",0.3)*0.3 + f.get("rms",0.3)*0.3) * g.rows * 0.7)
-        for dy in range(min(height, g.rows)):
-            fr = g.rows - 1 - dy
-            frac = dy / max(height, 1)
-            bri = max(0.1, (1 - frac*0.6) * (0.5 + f.get("rms",0.3)*0.5))
-            hue = hue_base + frac * hue_range
-            ci = "\u2588" if frac<0.2 else ("\u2593" if frac<0.4 else ("\u2592" if frac<0.6 else "\u2591"))
-            ch[fr, fx_c] = ci
-            R, G, B = hsv2rgb_single(hue, 0.9, bri)
-            co[fr, fx_c] = (R, G, B)
-    return ch, co
-```
-
-### Ice / Cold Fire (same structure, different hue range)
-```python
-# hue_base=0.55, hue_range=0.15 -- blue to cyan
-# Lower intensity, slower movement
-```
-
----
-
-## Text Overlays
-
-### Scrolling Ticker
-```python
-def eff_ticker(ch, co, t, text, row, speed=15, color=(80, 100, 140)):
-    off = int(t * speed) % max(len(text), 1)
-    doubled = text + "   " + text
-    stamp(ch, co, doubled[off:off+ch.shape[1]], row, 0, color)
-```
-
-### Beat-Triggered Words
-```python
-def eff_beat_words(ch, co, f, words, row_center=None, color=(255,240,220)):
-    if f.get("beat", 0) > 0:
-        w = random.choice(words)
-        r = (row_center or ch.shape[0]//2) + random.randint(-5,5)
-        stamp(ch, co, w, r, (ch.shape[1]-len(w))//2, color)
-```
-
-### Fading Message Sequence
-```python
-def eff_fading_messages(ch, co, t, elapsed, messages, period=4.0, color_base=(220,220,220)):
-    msg_idx = int(elapsed / period) % len(messages)
-    phase = elapsed % period
-    fade = max(0, min(1.0, phase) * min(1.0, period - phase))
-    if fade > 0.05:
-        v = fade
-        msg = messages[msg_idx]
-        cr, cg, cb = [int(c * v) for c in color_base]
-        stamp(ch, co, msg, ch.shape[0]//2, (ch.shape[1]-len(msg))//2, (cr, cg, cb))
-```
-
----
-
-## Screen Shake
-Shift entire char/color arrays on beat:
-```python
-def eff_shake(ch, co, f, x_amp=6, y_amp=3):
-    shake_x = int(f.get("sub",0.3)*x_amp*(random.random()-0.5)*2 + f.get("bdecay",0)*4*(random.random()-0.5)*2)
-    shake_y = int(f.get("bass",0.3)*y_amp*(random.random()-0.5)*2)
-    if abs(shake_x) > 0:
-        ch = np.roll(ch, shake_x, axis=1)
-        co = np.roll(co, shake_x, axis=1)
-    if abs(shake_y) > 0:
-        ch = np.roll(ch, shake_y, axis=0)
-        co = np.roll(co, shake_y, axis=0)
-    return ch, co
-```
-
----
-
-## Composable Effect System
-
-The real creative power comes from **composition**. There are three levels:
-
-### Level 1: Character-Level Layering
-
-Stack multiple effects as `(chars, colors)` layers:
-
-```python
-class LayerStack(EffectNode):
-    """Render effects bottom-to-top with character-level compositing."""
-    def add(self, effect, alpha=1.0):
-        """alpha < 1.0 = probabilistic override (sparse overlay)."""
-        self.layers.append((effect, alpha))
-
-# Usage:
-stack = LayerStack()
-stack.add(bg_effect)           # base — fills screen
-stack.add(main_effect)         # overlay on top (space chars = transparent)
-stack.add(particle_effect)     # sparse overlay on top of that
-ch, co = stack.render(g, f, t, S)
-```
-
-### Level 2: Pixel-Level Blending
-
-After rendering to canvases, blend with Photoshop-style modes:
-
-```python
-class PixelBlendStack:
-    """Stack canvases with blend modes for complex compositing."""
-    def add(self, canvas, mode="normal", opacity=1.0)
-    def composite(self) -> canvas
-
-# Usage:
-pbs = PixelBlendStack()
-pbs.add(canvas_a)                        # base
-pbs.add(canvas_b, "screen", 0.7)        # additive glow
-pbs.add(canvas_c, "difference", 0.5)    # psychedelic interference
-result = pbs.composite()
-```
-
-### Level 3: Temporal Feedback
-
-Feed previous frame back into current frame for recursive effects:
-
-```python
-fb = FeedbackBuffer()
-for each frame:
-    canvas = render_current()
-    canvas = fb.apply(canvas, decay=0.8, blend="screen",
-                      transform="zoom", transform_amt=0.015, hue_shift=0.02)
-```
-
-### Effect Nodes — Uniform Interface
-
-In the v2 protocol, effect nodes are used **inside** scene functions. The scene function itself returns a canvas. Effect nodes produce intermediate `(chars, colors)` that are rendered to canvas via the grid's `.render()` method or `_render_vf()`.
-
-```python
-class EffectNode:
-    def render(self, g, f, t, S) -> (chars, colors)
-
-# Concrete implementations:
-class ValueFieldEffect(EffectNode):
-    """Wraps a value field function + hue field function + palette."""
-    def __init__(self, val_fn, hue_fn, pal=PAL_DEFAULT, sat=0.7)
-
-class LambdaEffect(EffectNode):
-    """Wrap any (g,f,t,S) -> (ch,co) function."""
-    def __init__(self, fn)
-
-class ConditionalEffect(EffectNode):
-    """Switch effects based on audio features."""
-    def __init__(self, condition, if_true, if_false=None)
-```
-
-### Value Field Generators (Atomic Building Blocks)
-
-These produce float32 arrays `(rows, cols)` in range [0,1]. They are the raw visual patterns. All have signature `(g, f, t, S, **params) -> float32 array`.
-
-```python
-def vf_sinefield(g, f, t, S, bri=0.5,
-                 freq=(0.13, 0.17, 0.07, 0.09), speed=(0.5, -0.4, -0.3, 0.2)):
-    """Layered sine field. General purpose background/texture."""
-    v1 = np.sin(g.cc*freq[0] + t*speed[0]) * np.sin(g.rr*freq[1] - t*speed[1]) * 0.5 + 0.5
-    v2 = np.sin(g.cc*freq[2] - t*speed[2] + g.rr*freq[3]) * 0.4 + 0.5
-    v3 = np.sin(g.dist_n*5 + t*0.2) * 0.3 + 0.4
-    return np.clip((v1*0.35 + v2*0.35 + v3*0.3) * bri * (0.6 + f.get("rms",0.3)*0.6), 0, 1)
-
-def vf_smooth_noise(g, f, t, S, octaves=3, bri=0.5):
-    """Multi-octave sine approximation of Perlin noise."""
-    val = np.zeros((g.rows, g.cols), dtype=np.float32)
-    for i in range(octaves):
-        freq = 0.05 * (2 ** i); amp = 0.5 / (i + 1)
-        phase = t * (0.3 + i * 0.2)
-        val = val + np.sin(g.cc*freq + phase) * np.cos(g.rr*freq*0.7 - phase*0.5) * amp
-    return np.clip(val * 0.5 + 0.5, 0, 1) * bri
-
-def vf_rings(g, f, t, S, n_base=6, spacing_base=4):
-    """Concentric rings, bass-driven count and wobble."""
-    n = int(n_base + f.get("sub_r",0.3)*25 + f.get("bass",0.3)*10)
-    sp = spacing_base + f.get("bass_r",0.3)*7 + f.get("rms",0.3)*3
-    val = np.zeros((g.rows, g.cols), dtype=np.float32)
-    for ri in range(n):
-        rad = (ri+1)*sp + f.get("bdecay",0)*15
-        wobble = f.get("mid_r",0.3)*5*np.sin(g.angle*3+t*4)
-        rd = np.abs(g.dist - rad - wobble)
-        th = 1 + f.get("sub",0.3)*3
-        val = np.maximum(val, np.clip((1 - rd/th) * (0.4 + f.get("bass",0.3)*0.8), 0, 1))
-    return val
-
-def vf_spiral(g, f, t, S, n_arms=3, tightness=2.5):
-    """Logarithmic spiral arms."""
-    val = np.zeros((g.rows, g.cols), dtype=np.float32)
-    for ai in range(n_arms):
-        offset = ai * 2*np.pi / n_arms
-        log_r = np.log(g.dist + 1) * tightness
-        arm_phase = g.angle + offset - log_r + t * 0.8
-        arm_val = np.clip(np.cos(arm_phase * n_arms) * 0.6 + 0.2, 0, 1)
-        arm_val *= (0.4 + f.get("rms",0.3)*0.6) * np.clip(1 - g.dist_n*0.5, 0.2, 1)
-        val = np.maximum(val, arm_val)
-    return val
-
-def vf_tunnel(g, f, t, S, speed=3.0, complexity=6):
-    """Tunnel depth effect — infinite zoom feeling."""
-    tunnel_d = 1.0 / (g.dist_n + 0.1)
-    v1 = np.sin(tunnel_d*2 - t*speed) * 0.45 + 0.55
-    v2 = np.sin(g.angle*complexity + tunnel_d*1.5 - t*2) * 0.35 + 0.55
-    return np.clip(v1*0.5 + v2*0.5, 0, 1)
-
-def vf_vortex(g, f, t, S, twist=3.0):
-    """Twisting radial pattern — distance modulates angle."""
-    twisted = g.angle + g.dist_n * twist * np.sin(t * 0.5)
-    val = np.sin(twisted * 4 - t * 2) * 0.5 + 0.5
-    return np.clip(val * (0.5 + f.get("bass",0.3)*0.8), 0, 1)
-
-def vf_interference(g, f, t, S, n_waves=6):
-    """Overlapping sine waves creating moire patterns."""
-    drivers = ["mid_r", "himid_r", "bass_r", "lomid_r", "hi_r", "sub_r"]
-    vals = np.zeros((g.rows, g.cols), dtype=np.float32)
-    for i in range(min(n_waves, len(drivers))):
-        angle = i * np.pi / n_waves
-        freq = 0.06 + i * 0.03; sp = 0.5 + i * 0.3
-        proj = g.cc * np.cos(angle) + g.rr * np.sin(angle)
-        vals = vals + np.sin(proj*freq + t*sp) * f.get(drivers[i], 0.3) * 2.5
-    return np.clip(vals * 0.12 + 0.45, 0.1, 1)
-
-def vf_aurora(g, f, t, S, n_bands=3):
-    """Horizontal aurora bands."""
-    val = np.zeros((g.rows, g.cols), dtype=np.float32)
-    for i in range(n_bands):
-        fr = 0.08 + i*0.04; fc = 0.012 + i*0.008
-        sr = 0.7 + i*0.3; sc = 0.18 + i*0.12
-        val = val + np.sin(g.rr*fr + t*sr) * np.sin(g.cc*fc + t*sc) * (0.6/n_bands)
-    return np.clip(val * (f.get("lomid_r",0.3)*3 + 0.2), 0, 0.7)
-
-def vf_ripple(g, f, t, S, sources=None, freq=0.3, damping=0.02):
-    """Concentric ripples from point sources."""
-    if sources is None: sources = [(0.5, 0.5)]
-    val = np.zeros((g.rows, g.cols), dtype=np.float32)
-    for ry, rx in sources:
-        dy = g.rr - g.rows*ry; dx = g.cc - g.cols*rx
-        d = np.sqrt(dy**2 + dx**2)
-        val = val + np.sin(d*freq - t*4) * np.exp(-d*damping) * 0.5
-    return np.clip(val + 0.5, 0, 1)
-
-def vf_plasma(g, f, t, S):
-    """Classic plasma: sum of sines at different orientations and speeds."""
-    v = np.sin(g.cc * 0.03 + t * 0.7) * 0.5
-    v = v + np.sin(g.rr * 0.04 - t * 0.5) * 0.4
-    v = v + np.sin((g.cc * 0.02 + g.rr * 0.03) + t * 0.3) * 0.3
-    v = v + np.sin(g.dist_n * 4 - t * 0.8) * 0.3
-    return np.clip(v * 0.5 + 0.5, 0, 1)
-
-def vf_diamond(g, f, t, S, freq=0.15):
-    """Diamond/checkerboard pattern."""
-    val = np.abs(np.sin(g.cc * freq + t * 0.5)) * np.abs(np.sin(g.rr * freq * 1.2 - t * 0.3))
-    return np.clip(val * (0.6 + f.get("rms",0.3)*0.8), 0, 1)
-
-def vf_noise_static(g, f, t, S, density=0.4):
-    """Random noise — different each frame. Non-deterministic."""
-    return np.random.random((g.rows, g.cols)).astype(np.float32) * density * (0.5 + f.get("rms",0.3)*0.5)
-```
-
-### Hue Field Generators (Color Mapping)
-
-These produce float32 hue arrays [0,1]. Independently combinable with any value field. Each is a factory returning a closure with signature `(g, f, t, S) -> float32 array`. Can also be a plain float for fixed hue.
-
-```python
-def hf_fixed(hue):
-    """Single hue everywhere."""
-    def fn(g, f, t, S):
-        return np.full((g.rows, g.cols), hue, dtype=np.float32)
-    return fn
-
-def hf_angle(offset=0.0):
-    """Hue mapped to angle from center — rainbow wheel."""
-    def fn(g, f, t, S):
-        return (g.angle / (2 * np.pi) + offset + t * 0.05) % 1.0
-    return fn
-
-def hf_distance(base=0.5, scale=0.02):
-    """Hue mapped to distance from center."""
-    def fn(g, f, t, S):
-        return (base + g.dist * scale + t * 0.03) % 1.0
-    return fn
-
-def hf_time_cycle(speed=0.1):
-    """Hue cycles uniformly over time."""
-    def fn(g, f, t, S):
-        return np.full((g.rows, g.cols), (t * speed) % 1.0, dtype=np.float32)
-    return fn
-
-def hf_audio_cent():
-    """Hue follows spectral centroid — timbral color shifting."""
-    def fn(g, f, t, S):
-        return np.full((g.rows, g.cols), f.get("cent", 0.5) * 0.3, dtype=np.float32)
-    return fn
-
-def hf_gradient_h(start=0.0, end=1.0):
-    """Left-to-right hue gradient."""
-    def fn(g, f, t, S):
-        h = np.broadcast_to(
-            start + (g.cc / g.cols) * (end - start),
-            (g.rows, g.cols)
-        ).copy()  # .copy() is CRITICAL — see troubleshooting.md
-        return h % 1.0
-    return fn
-
-def hf_gradient_v(start=0.0, end=1.0):
-    """Top-to-bottom hue gradient."""
-    def fn(g, f, t, S):
-        h = np.broadcast_to(
-            start + (g.rr / g.rows) * (end - start),
-            (g.rows, g.cols)
-        ).copy()
-        return h % 1.0
-    return fn
-
-def hf_plasma(speed=0.3):
-    """Plasma-style hue field — organic color variation."""
-    def fn(g, f, t, S):
-        return (np.sin(g.cc*0.02 + t*speed)*0.5 + np.sin(g.rr*0.015 + t*speed*0.7)*0.5) % 1.0
-    return fn
-```
-
-### Combining Value Fields
-
-The combinatorial explosion comes from mixing value fields with math:
-
-```python
-# Multiplication = intersection (only shows where both have brightness)
-combined = vf_plasma(g,f,t,S) * vf_vortex(g,f,t,S)
-
-# Addition = union (shows both, clips at 1.0)
-combined = np.clip(vf_rings(g,f,t,S) + vf_spiral(g,f,t,S), 0, 1)
-
-# Interference = beat pattern (shows XOR-like patterns)
-combined = np.abs(vf_plasma(g,f,t,S) - vf_tunnel(g,f,t,S))
-
-# Modulation = one effect shapes the other
-combined = vf_rings(g,f,t,S) * (0.3 + 0.7 * vf_plasma(g,f,t,S))
-
-# Maximum = shows the brightest of two effects
-combined = np.maximum(vf_spiral(g,f,t,S), vf_aurora(g,f,t,S))
-```
-
-### Full Scene Example (v2 — Canvas Return)
-
-A v2 scene function composes effects internally and returns a pixel canvas:
-
-```python
-def scene_complex(r, f, t, S):
-    """v2 scene function: returns canvas (uint8 H,W,3).
-    r = Renderer, f = audio features, t = time, S = persistent state dict."""
-    g = r.grids["md"]
-    rows, cols = g.rows, g.cols
-    
-    # 1. Value field composition
-    plasma = vf_plasma(g, f, t, S)
-    vortex = vf_vortex(g, f, t, S, twist=4.0)
-    combined = np.clip(plasma * 0.6 + vortex * 0.5 + plasma * vortex * 0.4, 0, 1)
-    
-    # 2. Color from hue field
-    h = (hf_angle(0.3)(g,f,t,S) * 0.5 + hf_time_cycle(0.08)(g,f,t,S) * 0.5) % 1.0
-    
-    # 3. Render to canvas via _render_vf helper
-    canvas = _render_vf(g, combined, h, sat=0.75, pal=PAL_DENSE)
-    
-    # 4. Optional: blend a second layer
-    overlay = _render_vf(r.grids["sm"], vf_rings(r.grids["sm"],f,t,S),
-                         hf_fixed(0.6)(r.grids["sm"],f,t,S), pal=PAL_BLOCK)
-    canvas = blend_canvas(canvas, overlay, "screen", 0.4)
-    
-    return canvas
-    
-# In the render_clip() loop (handled by the framework):
-# canvas = scene_fn(r, f, t, S)
-# canvas = tonemap(canvas, gamma=scene_gamma)
-# canvas = feedback.apply(canvas, ...)
-# canvas = shader_chain.apply(canvas, f=f, t=t)
-# pipe.stdin.write(canvas.tobytes())
-```
-
-Vary the **value field combo**, **hue field**, **palette**, **blend modes**, **feedback config**, and **shader chain** per section for maximum visual variety. With 12 value fields × 8 hue fields × 14 palettes × 20 blend modes × 7 feedback transforms × 38 shaders, the combinations are effectively infinite.

skills/creative/ascii-video/references/inputs.md

@@ -1,407 +0,0 @@
-# Input Sources
-
-## Audio Analysis
-
-### Loading
-
-```python
-tmp = tempfile.mktemp(suffix=".wav")
-subprocess.run(["ffmpeg", "-y", "-i", input_path, "-ac", "1", "-ar", "22050",
-                "-sample_fmt", "s16", tmp], capture_output=True, check=True)
-with wave.open(tmp) as wf:
-    sr = wf.getframerate()
-    raw = wf.readframes(wf.getnframes())
-samples = np.frombuffer(raw, dtype=np.int16).astype(np.float32) / 32768.0
-```
-
-### Per-Frame FFT
-
-```python
-hop = sr // fps          # samples per frame
-win = hop * 2            # analysis window (2x hop for overlap)
-window = np.hanning(win)
-freqs = rfftfreq(win, 1.0 / sr)
-
-bands = {
-    "sub":   (freqs >= 20)  & (freqs < 80),
-    "bass":  (freqs >= 80)  & (freqs < 250),
-    "lomid": (freqs >= 250) & (freqs < 500),
-    "mid":   (freqs >= 500) & (freqs < 2000),
-    "himid": (freqs >= 2000)& (freqs < 6000),
-    "hi":    (freqs >= 6000),
-}
-```
-
-For each frame: extract chunk, apply window, FFT, compute band energies.
-
-### Feature Set
-
-| Feature | Formula | Controls |
-|---------|---------|----------|
-| `rms` | `sqrt(mean(chunk²))` | Overall loudness/energy |
-| `sub`..`hi` | `sqrt(mean(band_magnitudes²))` | Per-band energy |
-| `centroid` | `sum(freq*mag) / sum(mag)` | Brightness/timbre |
-| `flatness` | `geomean(mag) / mean(mag)` | Noise vs tone |
-| `flux` | `sum(max(0, mag - prev_mag))` | Transient strength |
-| `sub_r`..`hi_r` | `band / sum(all_bands)` | Spectral shape (volume-independent) |
-| `cent_d` | `abs(gradient(centroid))` | Timbral change rate |
-| `beat` | Flux peak detection | Binary beat onset |
-| `bdecay` | Exponential decay from beats | Smooth beat pulse (0→1→0) |
-
-**Band ratios are critical** — they decouple spectral shape from volume, so a quiet bass section and a loud bass section both read as "bassy" rather than just "loud" vs "quiet".
-
-### Smoothing
-
-EMA prevents visual jitter:
-
-```python
-def ema(arr, alpha):
-    out = np.empty_like(arr); out[0] = arr[0]
-    for i in range(1, len(arr)):
-        out[i] = alpha * arr[i] + (1 - alpha) * out[i-1]
-    return out
-
-# Slow-moving features (alpha=0.12): centroid, flatness, band ratios, cent_d
-# Fast-moving features (alpha=0.3): rms, flux, raw bands
-```
-
-### Beat Detection
-
-```python
-flux_smooth = np.convolve(flux, np.ones(5)/5, mode="same")
-peaks, _ = signal.find_peaks(flux_smooth, height=0.15, distance=fps//5, prominence=0.05)
-
-beat = np.zeros(n_frames)
-bdecay = np.zeros(n_frames, dtype=np.float32)
-for p in peaks:
-    beat[p] = 1.0
-    for d in range(fps // 2):
-        if p + d < n_frames:
-            bdecay[p + d] = max(bdecay[p + d], math.exp(-d * 2.5 / (fps // 2)))
-```
-
-`bdecay` gives smooth 0→1→0 pulse per beat, decaying over ~0.5s. Use for flash/glitch/mirror triggers.
-
-### Normalization
-
-After computing all frames, normalize each feature to 0-1:
-
-```python
-for k in features:
-    a = features[k]
-    lo, hi = a.min(), a.max()
-    features[k] = (a - lo) / (hi - lo + 1e-10)
-```
-
-## Video Sampling
-
-### Frame Extraction
-
-```python
-# Method 1: ffmpeg pipe (memory efficient)
-cmd = ["ffmpeg", "-i", input_video, "-f", "rawvideo", "-pix_fmt", "rgb24",
-       "-s", f"{target_w}x{target_h}", "-r", str(fps), "-"]
-pipe = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL)
-frame_size = target_w * target_h * 3
-for fi in range(n_frames):
-    raw = pipe.stdout.read(frame_size)
-    if len(raw) < frame_size: break
-    frame = np.frombuffer(raw, dtype=np.uint8).reshape(target_h, target_w, 3)
-    # process frame...
-
-# Method 2: OpenCV (if available)
-cap = cv2.VideoCapture(input_video)
-```
-
-### Luminance-to-Character Mapping
-
-Convert video pixels to ASCII characters based on brightness:
-
-```python
-def frame_to_ascii(frame_rgb, grid, pal=PAL_DEFAULT):
-    """Convert video frame to character + color arrays."""
-    rows, cols = grid.rows, grid.cols
-    # Resize frame to grid dimensions
-    small = np.array(Image.fromarray(frame_rgb).resize((cols, rows), Image.LANCZOS))
-    # Luminance
-    lum = (0.299 * small[:,:,0] + 0.587 * small[:,:,1] + 0.114 * small[:,:,2]) / 255.0
-    # Map to chars
-    chars = val2char(lum, lum > 0.02, pal)
-    # Colors: use source pixel colors, scaled by luminance for visibility
-    colors = np.clip(small * np.clip(lum[:,:,None] * 1.5 + 0.3, 0.3, 1), 0, 255).astype(np.uint8)
-    return chars, colors
-```
-
-### Edge-Weighted Character Mapping
-
-Use edge detection for more detail in contour regions:
-
-```python
-def frame_to_ascii_edges(frame_rgb, grid, pal=PAL_DEFAULT, edge_pal=PAL_BOX):
-    gray = np.mean(frame_rgb, axis=2)
-    small_gray = resize(gray, (grid.rows, grid.cols))
-    lum = small_gray / 255.0
-
-    # Sobel edge detection
-    gx = np.abs(small_gray[:, 2:] - small_gray[:, :-2])
-    gy = np.abs(small_gray[2:, :] - small_gray[:-2, :])
-    edge = np.zeros_like(small_gray)
-    edge[:, 1:-1] += gx; edge[1:-1, :] += gy
-    edge = np.clip(edge / edge.max(), 0, 1)
-
-    # Edge regions get box drawing chars, flat regions get brightness chars
-    is_edge = edge > 0.15
-    chars = val2char(lum, lum > 0.02, pal)
-    edge_chars = val2char(edge, is_edge, edge_pal)
-    chars[is_edge] = edge_chars[is_edge]
-
-    return chars, colors
-```
-
-### Motion Detection
-
-Detect pixel changes between frames for motion-reactive effects:
-
-```python
-prev_frame = None
-def compute_motion(frame):
-    global prev_frame
-    if prev_frame is None:
-        prev_frame = frame.astype(np.float32)
-        return np.zeros(frame.shape[:2])
-    diff = np.abs(frame.astype(np.float32) - prev_frame).mean(axis=2)
-    prev_frame = frame.astype(np.float32) * 0.7 + prev_frame * 0.3  # smoothed
-    return np.clip(diff / 30.0, 0, 1)  # normalized motion map
-```
-
-Use motion map to drive particle emission, glitch intensity, or character density.
-
-### Video Feature Extraction
-
-Per-frame features analogous to audio features, for driving effects:
-
-```python
-def analyze_video_frame(frame_rgb):
-    gray = np.mean(frame_rgb, axis=2)
-    return {
-        "brightness": gray.mean() / 255.0,
-        "contrast": gray.std() / 128.0,
-        "edge_density": compute_edge_density(gray),
-        "motion": compute_motion(frame_rgb).mean(),
-        "dominant_hue": compute_dominant_hue(frame_rgb),
-        "color_variance": compute_color_variance(frame_rgb),
-    }
-```
-
-## Image Sequence
-
-### Static Image to ASCII
-
-Same as single video frame conversion. For animated sequences:
-
-```python
-import glob
-frames = sorted(glob.glob("frames/*.png"))
-for fi, path in enumerate(frames):
-    img = np.array(Image.open(path).resize((VW, VH)))
-    chars, colors = frame_to_ascii(img, grid, pal)
-```
-
-### Image as Texture Source
-
-Use an image as a background texture that effects modulate:
-
-```python
-def load_texture(path, grid):
-    img = np.array(Image.open(path).resize((grid.cols, grid.rows)))
-    lum = np.mean(img, axis=2) / 255.0
-    return lum, img  # luminance for char mapping, RGB for colors
-```
-
-## Text / Lyrics
-
-### SRT Parsing
-
-```python
-import re
-def parse_srt(path):
-    """Returns [(start_sec, end_sec, text), ...]"""
-    entries = []
-    with open(path) as f:
-        content = f.read()
-    blocks = content.strip().split("\n\n")
-    for block in blocks:
-        lines = block.strip().split("\n")
-        if len(lines) >= 3:
-            times = lines[1]
-            m = re.match(r"(\d+):(\d+):(\d+),(\d+) --> (\d+):(\d+):(\d+),(\d+)", times)
-            if m:
-                g = [int(x) for x in m.groups()]
-                start = g[0]*3600 + g[1]*60 + g[2] + g[3]/1000
-                end = g[4]*3600 + g[5]*60 + g[6] + g[7]/1000
-                text = " ".join(lines[2:])
-                entries.append((start, end, text))
-    return entries
-```
-
-### Lyrics Display Modes
-
-- **Typewriter**: characters appear left-to-right over the time window
-- **Fade-in**: whole line fades from dark to bright
-- **Flash**: appear instantly on beat, fade out
-- **Scatter**: characters start at random positions, converge to final position
-- **Wave**: text follows a sine wave path
-
-```python
-def lyrics_typewriter(ch, co, text, row, col, t, t_start, t_end, color):
-    """Reveal characters progressively over time window."""
-    progress = np.clip((t - t_start) / (t_end - t_start), 0, 1)
-    n_visible = int(len(text) * progress)
-    stamp(ch, co, text[:n_visible], row, col, color)
-```
-
-## Generative (No Input)
-
-For pure generative ASCII art, the "features" dict is synthesized from time:
-
-```python
-def synthetic_features(t, bpm=120):
-    """Generate audio-like features from time alone."""
-    beat_period = 60.0 / bpm
-    beat_phase = (t % beat_period) / beat_period
-    return {
-        "rms": 0.5 + 0.3 * math.sin(t * 0.5),
-        "bass": 0.5 + 0.4 * math.sin(t * 2 * math.pi / beat_period),
-        "sub": 0.3 + 0.3 * math.sin(t * 0.8),
-        "mid": 0.4 + 0.3 * math.sin(t * 1.3),
-        "hi": 0.3 + 0.2 * math.sin(t * 2.1),
-        "cent": 0.5 + 0.2 * math.sin(t * 0.3),
-        "flat": 0.4,
-        "flux": 0.3 + 0.2 * math.sin(t * 3),
-        "beat": 1.0 if beat_phase < 0.05 else 0.0,
-        "bdecay": max(0, 1.0 - beat_phase * 4),
-        # ratios
-        "sub_r": 0.2, "bass_r": 0.25, "lomid_r": 0.15,
-        "mid_r": 0.2, "himid_r": 0.12, "hi_r": 0.08,
-        "cent_d": 0.1,
-    }
-```
-
-## TTS Integration
-
-For narrated videos (testimonials, quotes, storytelling), generate speech audio per segment and mix with background music.
-
-### ElevenLabs Voice Generation
-
-```python
-import requests
-
-def generate_tts(text, voice_id, api_key, output_path, model="eleven_multilingual_v2"):
-    """Generate TTS audio via ElevenLabs API."""
-    url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}"
-    headers = {"xi-api-key": api_key, "Content-Type": "application/json"}
-    data = {"text": text, "model_id": model,
-            "voice_settings": {"stability": 0.5, "similarity_boost": 0.75}}
-    resp = requests.post(url, json=data, headers=headers, timeout=30)
-    resp.raise_for_status()
-    with open(output_path, "wb") as f:
-        f.write(resp.content)
-```
-
-### Voice Assignment
-
-Use multiple voices for variety. Shuffle deterministically so re-runs are consistent:
-
-```python
-import random as _rng
-
-def assign_voices(n_quotes, voice_pool, seed=42):
-    """Assign a different voice to each quote, cycling if needed."""
-    r = _rng.Random(seed)
-    shuffled = list(voice_pool)
-    r.shuffle(shuffled)
-    return [shuffled[i % len(shuffled)] for i in range(n_quotes)]
-```
-
-### Pronunciation Control
-
-TTS text should be separate from display text. Common fixes:
-- Brand names: spell phonetically ("Nous" -> "Noose", "nginx" -> "engine-x")
-- Abbreviations: expand ("API" -> "A P I", "CLI" -> "C L I")
-- Technical terms: add phonetic hints
-
-```python
-QUOTES = [("Display text here", "Author")]
-QUOTES_TTS = ["TTS text with phonetic spelling here"]
-# Keep both arrays in sync -- same indices
-```
-
-### Audio Pipeline
-
-1. Generate individual TTS clips (MP3/WAV per quote)
-2. Get duration of each clip
-3. Calculate timing: speech start/end per quote with gaps
-4. Concatenate into single TTS track with silence padding
-5. Mix with background music
-
-```python
-def build_tts_track(tts_clips, target_duration, gap_seconds=2.0):
-    """Concatenate TTS clips with gaps, pad to target duration."""
-    # Get durations
-    durations = []
-    for clip in tts_clips:
-        result = subprocess.run(
-            ["ffprobe", "-v", "error", "-show_entries", "format=duration",
-             "-of", "csv=p=0", clip],
-            capture_output=True, text=True)
-        durations.append(float(result.stdout.strip()))
-    
-    # Calculate timing
-    total_speech = sum(durations)
-    total_gaps = target_duration - total_speech
-    gap = max(0.5, total_gaps / (len(tts_clips) + 1))
-    
-    timing = []  # (start, end, quote_index)
-    t = gap  # start after initial gap
-    for i, dur in enumerate(durations):
-        timing.append((t, t + dur, i))
-        t += dur + gap
-    
-    # Concatenate with ffmpeg
-    # ... silence padding + concat filter
-    return timing
-```
-
-### Audio Mixing
-
-Mix TTS (center) with background music (wide stereo, low volume):
-
-```python
-def mix_audio(tts_path, bgm_path, output_path, bgm_volume=0.15):
-    """Mix TTS centered with BGM panned wide stereo."""
-    cmd = [
-        "ffmpeg", "-y",
-        "-i", tts_path,   # mono TTS
-        "-i", bgm_path,   # stereo BGM
-        "-filter_complex",
-        f"[0:a]aformat=sample_fmts=fltp:sample_rates=44100:channel_layouts=mono,"
-        f"pan=stereo|c0=c0|c1=c0[tts];"  # TTS center
-        f"[1:a]loudnorm=I=-16:TP=-1.5:LRA=11,"
-        f"volume={bgm_volume},"
-        f"extrastereo=2.5[bgm];"  # BGM wide stereo
-        f"[tts][bgm]amix=inputs=2:duration=longest[out]",
-        "-map", "[out]", "-c:a", "pcm_s16le", output_path
-    ]
-    subprocess.run(cmd, capture_output=True, check=True)
-```
-
-### Feature Analysis on Mixed Audio
-
-Run the standard audio analysis (FFT, beat detection) on the final mixed track so visual effects react to both TTS and music:
-
-```python
-# Analyze mixed_final.wav (not individual tracks)
-features = analyze_audio("mixed_final.wav", fps=24)
-```
-
-This means visuals will pulse with both the music beats and the speech energy -- creating natural synchronization.

skills/creative/ascii-video/references/optimization.md

@@ -1,435 +0,0 @@
-# Optimization Reference
-
-## Hardware Detection
-
-Detect the user's hardware at script startup and adapt rendering parameters automatically. Never hardcode worker counts or resolution.
-
-### CPU and Memory Detection
-
-```python
-import multiprocessing
-import platform
-import shutil
-import os
-
-def detect_hardware():
-    """Detect hardware capabilities and return render config."""
-    cpu_count = multiprocessing.cpu_count()
-    
-    # Leave 1-2 cores free for OS + ffmpeg encoding
-    if cpu_count >= 16:
-        workers = cpu_count - 2
-    elif cpu_count >= 8:
-        workers = cpu_count - 1
-    elif cpu_count >= 4:
-        workers = cpu_count - 1
-    else:
-        workers = max(1, cpu_count)
-    
-    # Memory detection (platform-specific)
-    try:
-        if platform.system() == "Darwin":
-            import subprocess
-            mem_bytes = int(subprocess.check_output(["sysctl", "-n", "hw.memsize"]).strip())
-        elif platform.system() == "Linux":
-            with open("/proc/meminfo") as f:
-                for line in f:
-                    if line.startswith("MemTotal"):
-                        mem_bytes = int(line.split()[1]) * 1024
-                        break
-        else:
-            mem_bytes = 8 * 1024**3  # assume 8GB on unknown
-    except Exception:
-        mem_bytes = 8 * 1024**3
-
-    mem_gb = mem_bytes / (1024**3)
-    
-    # Each worker uses ~50-150MB depending on grid sizes
-    # Cap workers if memory is tight
-    mem_per_worker_mb = 150
-    max_workers_by_mem = int(mem_gb * 1024 * 0.6 / mem_per_worker_mb)  # use 60% of RAM
-    workers = min(workers, max_workers_by_mem)
-    
-    # ffmpeg availability and codec support
-    has_ffmpeg = shutil.which("ffmpeg") is not None
-    
-    return {
-        "cpu_count": cpu_count,
-        "workers": workers,
-        "mem_gb": mem_gb,
-        "platform": platform.system(),
-        "arch": platform.machine(),
-        "has_ffmpeg": has_ffmpeg,
-    }
-```
-
-### Adaptive Quality Profiles
-
-Scale resolution, FPS, CRF, and grid density based on hardware:
-
-```python
-def quality_profile(hw, target_duration_s, user_preference="auto"):
-    """
-    Returns render settings adapted to hardware.
-    user_preference: "auto", "draft", "preview", "production", "max"
-    """
-    if user_preference == "draft":
-        return {"vw": 960, "vh": 540, "fps": 12, "crf": 28, "workers": min(4, hw["workers"]),
-                "grid_scale": 0.5, "shaders": "minimal", "particles_max": 200}
-    
-    if user_preference == "preview":
-        return {"vw": 1280, "vh": 720, "fps": 15, "crf": 25, "workers": hw["workers"],
-                "grid_scale": 0.75, "shaders": "standard", "particles_max": 500}
-    
-    if user_preference == "max":
-        return {"vw": 3840, "vh": 2160, "fps": 30, "crf": 15, "workers": hw["workers"],
-                "grid_scale": 2.0, "shaders": "full", "particles_max": 3000}
-    
-    # "production" or "auto"
-    # Auto-detect: estimate render time, downgrade if it would take too long
-    n_frames = int(target_duration_s * 24)
-    est_seconds_per_frame = 0.18  # ~180ms at 1080p
-    est_total_s = n_frames * est_seconds_per_frame / max(1, hw["workers"])
-    
-    if hw["mem_gb"] < 4 or hw["cpu_count"] <= 2:
-        # Low-end: 720p, 15fps
-        return {"vw": 1280, "vh": 720, "fps": 15, "crf": 23, "workers": hw["workers"],
-                "grid_scale": 0.75, "shaders": "standard", "particles_max": 500}
-    
-    if est_total_s > 3600:  # would take over an hour
-        # Downgrade to 720p to speed up
-        return {"vw": 1280, "vh": 720, "fps": 24, "crf": 20, "workers": hw["workers"],
-                "grid_scale": 0.75, "shaders": "standard", "particles_max": 800}
-    
-    # Standard production: 1080p 24fps
-    return {"vw": 1920, "vh": 1080, "fps": 24, "crf": 20, "workers": hw["workers"],
-            "grid_scale": 1.0, "shaders": "full", "particles_max": 1200}
-
-
-def apply_quality_profile(profile):
-    """Set globals from quality profile."""
-    global VW, VH, FPS, N_WORKERS
-    VW = profile["vw"]
-    VH = profile["vh"]
-    FPS = profile["fps"]
-    N_WORKERS = profile["workers"]
-    # Grid sizes scale with resolution
-    # CRF passed to ffmpeg encoder
-    # Shader set determines which post-processing is active
-```
-
-### CLI Integration
-
-```python
-parser = argparse.ArgumentParser()
-parser.add_argument("--quality", choices=["draft", "preview", "production", "max", "auto"],
-                    default="auto", help="Render quality preset")
-parser.add_argument("--workers", type=int, default=0, help="Override worker count (0=auto)")
-parser.add_argument("--resolution", type=str, default="", help="Override resolution e.g. 1280x720")
-args = parser.parse_args()
-
-hw = detect_hardware()
-if args.workers > 0:
-    hw["workers"] = args.workers
-profile = quality_profile(hw, target_duration, args.quality)
-if args.resolution:
-    w, h = args.resolution.split("x")
-    profile["vw"], profile["vh"] = int(w), int(h)
-apply_quality_profile(profile)
-
-log(f"Hardware: {hw['cpu_count']} cores, {hw['mem_gb']:.1f}GB RAM, {hw['platform']}")
-log(f"Render:   {profile['vw']}x{profile['vh']} @{profile['fps']}fps, "
-    f"CRF {profile['crf']}, {profile['workers']} workers")
-```
-
-## Performance Budget
-
-Target: 100-200ms per frame (5-10 fps single-threaded, 40-80 fps across 8 workers).
-
-| Component | Time | Notes |
-|-----------|------|-------|
-| Feature extraction | 1-5ms | Pre-computed for all frames before render |
-| Effect function | 2-15ms | Vectorized numpy, avoid Python loops |
-| Character render | 80-150ms | **Bottleneck** -- per-cell Python loop |
-| Shader pipeline | 5-25ms | Depends on active shaders |
-| ffmpeg encode | ~5ms | Amortized by pipe buffering |
-
-## Bitmap Pre-Rasterization
-
-Rasterize every character at init, not per-frame:
-
-```python
-# At init time -- done once
-for c in all_characters:
-    img = Image.new("L", (cell_w, cell_h), 0)
-    ImageDraw.Draw(img).text((0, 0), c, fill=255, font=font)
-    bitmaps[c] = np.array(img, dtype=np.float32) / 255.0  # float32 for fast multiply
-
-# At render time -- fast lookup
-bitmap = bitmaps[char]
-canvas[y:y+ch, x:x+cw] = np.maximum(canvas[y:y+ch, x:x+cw],
-                                      (bitmap[:,:,None] * color).astype(np.uint8))
-```
-
-Collect all characters from all palettes + overlay text into the init set. Lazy-init for any missed characters.
-
-## Coordinate Array Caching
-
-Pre-compute all grid-relative coordinate arrays at init, not per-frame:
-
-```python
-# These are O(rows*cols) and used in every effect
-self.rr = np.arange(rows)[:, None]    # row indices
-self.cc = np.arange(cols)[None, :]    # col indices
-self.dist = np.sqrt(dx**2 + dy**2)   # distance from center
-self.angle = np.arctan2(dy, dx)       # angle from center
-self.dist_n = ...                      # normalized distance
-```
-
-## Vectorized Effect Patterns
-
-### Avoid Per-Cell Python Loops in Effects
-
-The render loop (compositing bitmaps) is unavoidably per-cell. But effect functions must be fully vectorized numpy -- never iterate over rows/cols in Python.
-
-Bad (O(rows*cols) Python loop):
-```python
-for r in range(rows):
-    for c in range(cols):
-        val[r, c] = math.sin(c * 0.1 + t) * math.cos(r * 0.1 - t)
-```
-
-Good (vectorized):
-```python
-val = np.sin(g.cc * 0.1 + t) * np.cos(g.rr * 0.1 - t)
-```
-
-### Vectorized Matrix Rain
-
-The naive per-column per-trail-pixel loop is the second biggest bottleneck after the render loop. Use numpy fancy indexing:
-
-```python
-# Instead of nested Python loops over columns and trail pixels:
-# Build row index arrays for all active trail pixels at once
-all_rows = []
-all_cols = []
-all_fades = []
-for c in range(cols):
-    head = int(state["ry"][c])
-    trail_len = state["rln"][c]
-    for i in range(trail_len):
-        row = head - i
-        if 0 <= row < rows:
-            all_rows.append(row)
-            all_cols.append(c)
-            all_fades.append(1.0 - i / trail_len)
-
-# Vectorized assignment
-ar = np.array(all_rows)
-ac = np.array(all_cols)
-af = np.array(all_fades, dtype=np.float32)
-# Assign chars and colors in bulk using fancy indexing
-ch[ar, ac] = ...  # vectorized char assignment
-co[ar, ac, 1] = (af * bri * 255).astype(np.uint8)  # green channel
-```
-
-### Vectorized Fire Columns
-
-Same pattern -- accumulate index arrays, assign in bulk:
-
-```python
-fire_val = np.zeros((rows, cols), dtype=np.float32)
-for fi in range(n_cols):
-    fx_c = int((fi * cols / n_cols + np.sin(t * 2 + fi * 0.7) * 3) % cols)
-    height = int(energy * rows * 0.7)
-    dy = np.arange(min(height, rows))
-    fr = rows - 1 - dy
-    frac = dy / max(height, 1)
-    # Width spread: base columns wider at bottom
-    for dx in range(-1, 2):  # 3-wide columns
-        c = fx_c + dx
-        if 0 <= c < cols:
-            fire_val[fr, c] = np.maximum(fire_val[fr, c],
-                                          (1 - frac * 0.6) * (0.5 + rms * 0.5))
-# Now map fire_val to chars and colors in one vectorized pass
-```
-
-## Bloom Optimization
-
-**Do NOT use `scipy.ndimage.uniform_filter`** -- measured at 424ms/frame.
-
-Use 4x downsample + manual box blur instead -- 84ms/frame (5x faster):
-
-```python
-sm = canvas[::4, ::4].astype(np.float32)  # 4x downsample
-br = np.where(sm > threshold, sm, 0)
-for _ in range(3):                          # 3-pass manual box blur
-    p = np.pad(br, ((1,1),(1,1),(0,0)), mode='edge')
-    br = (p[:-2,:-2] + p[:-2,1:-1] + p[:-2,2:] +
-          p[1:-1,:-2] + p[1:-1,1:-1] + p[1:-1,2:] +
-          p[2:,:-2] + p[2:,1:-1] + p[2:,2:]) / 9.0
-bl = np.repeat(np.repeat(br, 4, axis=0), 4, axis=1)[:H, :W]
-```
-
-## Vignette Caching
-
-Distance field is resolution- and strength-dependent, never changes per frame:
-
-```python
-_vig_cache = {}
-def sh_vignette(canvas, strength):
-    key = (canvas.shape[0], canvas.shape[1], round(strength, 2))
-    if key not in _vig_cache:
-        Y = np.linspace(-1, 1, H)[:, None]
-        X = np.linspace(-1, 1, W)[None, :]
-        _vig_cache[key] = np.clip(1.0 - np.sqrt(X**2+Y**2) * strength, 0.15, 1).astype(np.float32)
-    return np.clip(canvas * _vig_cache[key][:,:,None], 0, 255).astype(np.uint8)
-```
-
-Same pattern for CRT barrel distortion (cache remap coordinates).
-
-## Film Grain Optimization
-
-Generate noise at half resolution, tile up:
-
-```python
-noise = np.random.randint(-amt, amt+1, (H//2, W//2, 1), dtype=np.int16)
-noise = np.repeat(np.repeat(noise, 2, axis=0), 2, axis=1)[:H, :W]
-```
-
-2x blocky grain looks like film grain and costs 1/4 the random generation.
-
-## Parallel Rendering
-
-### Worker Architecture
-
-```python
-hw = detect_hardware()
-N_WORKERS = hw["workers"]
-
-# Batch splitting (for non-clip architectures)
-batch_size = (n_frames + N_WORKERS - 1) // N_WORKERS
-batches = [(i, i*batch_size, min((i+1)*batch_size, n_frames), features, seg_path) ...]
-
-with multiprocessing.Pool(N_WORKERS) as pool:
-    segments = pool.starmap(render_batch, batches)
-```
-
-### Per-Clip Parallelism (Preferred for Segmented Videos)
-
-```python
-from concurrent.futures import ProcessPoolExecutor, as_completed
-
-with ProcessPoolExecutor(max_workers=N_WORKERS) as pool:
-    futures = {pool.submit(render_clip, seg, features, path): seg["id"]
-               for seg, path in clip_args}
-    for fut in as_completed(futures):
-        clip_id = futures[fut]
-        try:
-            fut.result()
-            log(f"  {clip_id} done")
-        except Exception as e:
-            log(f"  {clip_id} FAILED: {e}")
-```
-
-### Worker Isolation
-
-Each worker:
-- Creates its own `Renderer` instance (with full grid + bitmap init)
-- Opens its own ffmpeg subprocess
-- Has independent random seed (`random.seed(batch_id * 10000)`)
-- Writes to its own segment file and stderr log
-
-### ffmpeg Pipe Safety
-
-**CRITICAL**: Never `stderr=subprocess.PIPE` with long-running ffmpeg. The stderr buffer fills at ~64KB and deadlocks:
-
-```python
-# WRONG -- will deadlock
-pipe = subprocess.Popen(cmd, stdin=subprocess.PIPE, stderr=subprocess.PIPE)
-
-# RIGHT -- stderr to file
-stderr_fh = open(err_path, "w")
-pipe = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.DEVNULL, stderr=stderr_fh)
-# ... write all frames ...
-pipe.stdin.close()
-pipe.wait()
-stderr_fh.close()
-```
-
-### Concatenation
-
-```python
-with open(concat_file, "w") as cf:
-    for seg in segments:
-        cf.write(f"file '{seg}'\n")
-
-cmd = ["ffmpeg", "-y", "-f", "concat", "-safe", "0", "-i", concat_file]
-if audio_path:
-    cmd += ["-i", audio_path, "-c:v", "copy", "-c:a", "aac", "-b:a", "192k", "-shortest"]
-else:
-    cmd += ["-c:v", "copy"]
-cmd.append(output_path)
-subprocess.run(cmd, capture_output=True, check=True)
-```
-
-## Particle System Performance
-
-Cap particle counts based on quality profile:
-
-| System | Low | Standard | High |
-|--------|-----|----------|------|
-| Explosion | 300 | 1000 | 2500 |
-| Embers | 500 | 1500 | 3000 |
-| Starfield | 300 | 800 | 1500 |
-| Dissolve | 200 | 600 | 1200 |
-
-Cull by truncating lists:
-```python
-MAX_PARTICLES = profile.get("particles_max", 1200)
-if len(S["px"]) > MAX_PARTICLES:
-    for k in ("px", "py", "vx", "vy", "life", "char"):
-        S[k] = S[k][-MAX_PARTICLES:]  # keep newest
-```
-
-## Memory Management
-
-- Feature arrays: pre-computed for all frames, shared across workers via fork semantics (COW)
-- Canvas: allocated once per worker, reused (`np.zeros(...)`)
-- Character arrays: allocated per frame (cheap -- rows*cols U1 strings)
-- Bitmap cache: ~500KB per grid size, initialized once per worker
-
-Total memory per worker: ~50-150MB. Total: ~400-800MB for 8 workers.
-
-For low-memory systems (< 4GB), reduce worker count and use smaller grids.
-
-## Brightness Verification
-
-After render, spot-check brightness at sample timestamps:
-
-```python
-for t in [2, 30, 60, 120, 180]:
-    cmd = ["ffmpeg", "-ss", str(t), "-i", output_path,
-           "-frames:v", "1", "-f", "rawvideo", "-pix_fmt", "rgb24", "-"]
-    r = subprocess.run(cmd, capture_output=True)
-    arr = np.frombuffer(r.stdout, dtype=np.uint8)
-    print(f"t={t}s  mean={arr.mean():.1f}  max={arr.max()}")
-```
-
-Target: mean > 5 for quiet sections, mean > 15 for active sections. If consistently below, increase brightness floor in effects and/or global boost multiplier.
-
-## Render Time Estimates
-
-Scale with hardware. Baseline: 1080p, 24fps, ~180ms/frame/worker.
-
-| Duration | Frames | 4 workers | 8 workers | 16 workers |
-|----------|--------|-----------|-----------|------------|
-| 30s | 720 | ~3 min | ~2 min | ~1 min |
-| 2 min | 2,880 | ~13 min | ~7 min | ~4 min |
-| 3.5 min | 5,040 | ~23 min | ~12 min | ~6 min |
-| 5 min | 7,200 | ~33 min | ~17 min | ~9 min |
-| 10 min | 14,400 | ~65 min | ~33 min | ~17 min |
-
-At 720p: multiply times by ~0.5. At 4K: multiply by ~4.
-
-Heavier effects (many particles, dense grids, extra shader passes) add ~20-50%.

skills/creative/ascii-video/references/scenes.md

@@ -1,382 +0,0 @@
-# Scene System Reference
-
-Scenes are the top-level creative unit. Each scene is a time-bounded segment with its own effect function, shader chain, feedback configuration, and tone-mapping gamma.
-
-## Scene Protocol (v2)
-
-### Function Signature
-
-```python
-def fx_scene_name(r, f, t, S) -> canvas:
-    """
-    Args:
-        r: Renderer instance — access multiple grids via r.get_grid("sm")
-        f: dict of audio/video features, all values normalized to [0, 1]
-        t: time in seconds (global, not local to scene)
-        S: dict for persistent state (particles, rain columns, etc.)
-
-    Returns:
-        canvas: numpy uint8 array, shape (VH, VW, 3) — full pixel frame
-    """
-```
-
-This replaces the v1 protocol where scenes returned `(chars, colors)` tuples. The v2 protocol gives scenes full control over multi-grid rendering and pixel-level composition internally.
-
-### The Renderer Class
-
-```python
-class Renderer:
-    def __init__(self):
-        self.grids = {}   # lazy-initialized grid cache
-        self.g = None      # "active" grid (for backward compat)
-        self.S = {}        # persistent state dict
-
-    def get_grid(self, key):
-        """Get or create a GridLayer by size key."""
-        if key not in self.grids:
-            sizes = {"xs": 8, "sm": 10, "md": 16, "lg": 20, "xl": 24, "xxl": 40}
-            self.grids[key] = GridLayer(FONT_PATH, sizes[key])
-        return self.grids[key]
-
-    def set_grid(self, key):
-        """Set active grid (legacy). Prefer get_grid() for multi-grid scenes."""
-        self.g = self.get_grid(key)
-        return self.g
-```
-
-**Key difference from v1**: scenes call `r.get_grid("sm")`, `r.get_grid("lg")`, etc. to access multiple grids. Each grid is lazy-initialized and cached. The `set_grid()` method still works for single-grid scenes.
-
-### Minimal Scene (Single Grid)
-
-```python
-def fx_simple_rings(r, f, t, S):
-    """Single-grid scene: rings with distance-mapped hue."""
-    canvas = _render_vf(r, "md",
-        lambda g, f, t, S: vf_rings(g, f, t, S, n_base=8, spacing_base=3),
-        hf_distance(0.3, 0.02), PAL_STARS, f, t, S, sat=0.85)
-    return canvas
-```
-
-### Standard Scene (Two Grids + Blend)
-
-```python
-def fx_tunnel_ripple(r, f, t, S):
-    """Two-grid scene: tunnel depth exclusion-blended with ripple."""
-    canvas_a = _render_vf(r, "md",
-        lambda g, f, t, S: vf_tunnel(g, f, t, S, speed=5.0, complexity=10) * 1.3,
-        hf_distance(0.55, 0.02), PAL_GREEK, f, t, S, sat=0.7)
-
-    canvas_b = _render_vf(r, "sm",
-        lambda g, f, t, S: vf_ripple(g, f, t, S,
-            sources=[(0.3,0.3), (0.7,0.7), (0.5,0.2)], freq=0.5, damping=0.012) * 1.4,
-        hf_angle(0.1), PAL_STARS, f, t, S, sat=0.8)
-
-    return blend_canvas(canvas_a, canvas_b, "exclusion", 0.8)
-```
-
-### Complex Scene (Three Grids + Conditional + Custom Rendering)
-
-```python
-def fx_rings_explosion(r, f, t, S):
-    """Three-grid scene with particles and conditional kaleidoscope."""
-    # Layer 1: rings
-    canvas_a = _render_vf(r, "sm",
-        lambda g, f, t, S: vf_rings(g, f, t, S, n_base=10, spacing_base=2) * 1.4,
-        lambda g, f, t, S: (g.angle / (2*np.pi) + t * 0.15) % 1.0,
-        PAL_STARS, f, t, S, sat=0.9)
-
-    # Layer 2: vortex on different grid
-    canvas_b = _render_vf(r, "md",
-        lambda g, f, t, S: vf_vortex(g, f, t, S, twist=6.0) * 1.2,
-        hf_time_cycle(0.15), PAL_BLOCKS, f, t, S, sat=0.8)
-
-    result = blend_canvas(canvas_b, canvas_a, "screen", 0.7)
-
-    # Layer 3: particles (custom rendering, not _render_vf)
-    g = r.get_grid("sm")
-    if "px" not in S:
-        S["px"], S["py"], S["vx"], S["vy"], S["life"], S["pch"] = (
-            [], [], [], [], [], [])
-    if f.get("beat", 0) > 0.5:
-        chars = list("\u2605\u2736\u2733\u2738\u2726\u2728*+")
-        for _ in range(int(80 + f.get("rms", 0.3) * 120)):
-            ang = random.uniform(0, 2 * math.pi)
-            sp = random.uniform(1, 10) * (0.5 + f.get("sub_r", 0.3) * 2)
-            S["px"].append(float(g.cols // 2))
-            S["py"].append(float(g.rows // 2))
-            S["vx"].append(math.cos(ang) * sp * 2.5)
-            S["vy"].append(math.sin(ang) * sp)
-            S["life"].append(1.0)
-            S["pch"].append(random.choice(chars))
-
-    # Update + draw particles
-    ch_p = np.full((g.rows, g.cols), " ", dtype="U1")
-    co_p = np.zeros((g.rows, g.cols, 3), dtype=np.uint8)
-    i = 0
-    while i < len(S["px"]):
-        S["px"][i] += S["vx"][i]; S["py"][i] += S["vy"][i]
-        S["vy"][i] += 0.03; S["life"][i] -= 0.02
-        if S["life"][i] <= 0:
-            for k in ("px","py","vx","vy","life","pch"): S[k].pop(i)
-        else:
-            pr, pc = int(S["py"][i]), int(S["px"][i])
-            if 0 <= pr < g.rows and 0 <= pc < g.cols:
-                ch_p[pr, pc] = S["pch"][i]
-                co_p[pr, pc] = hsv2rgb_scalar(
-                    0.08 + (1-S["life"][i])*0.15, 0.95, S["life"][i])
-            i += 1
-
-    canvas_p = g.render(ch_p, co_p)
-    result = blend_canvas(result, canvas_p, "add", 0.8)
-
-    # Conditional kaleidoscope on strong beats
-    if f.get("bdecay", 0) > 0.4:
-        result = sh_kaleidoscope(result.copy(), folds=6)
-
-    return result
-```
-
-### Scene with Custom Character Rendering (Matrix Rain)
-
-When you need per-cell control beyond what `_render_vf()` provides:
-
-```python
-def fx_matrix_layered(r, f, t, S):
-    """Matrix rain blended with tunnel — two grids, screen blend."""
-    # Layer 1: Matrix rain (custom per-column rendering)
-    g = r.get_grid("md")
-    rows, cols = g.rows, g.cols
-    pal = PAL_KATA
-
-    if "ry" not in S or len(S["ry"]) != cols:
-        S["ry"] = np.random.uniform(-rows, rows, cols).astype(np.float32)
-        S["rsp"] = np.random.uniform(0.3, 2.0, cols).astype(np.float32)
-        S["rln"] = np.random.randint(8, 35, cols)
-        S["rch"] = np.random.randint(1, len(pal), (rows, cols))
-
-    speed = 0.6 + f.get("bass", 0.3) * 3
-    if f.get("beat", 0) > 0.5: speed *= 2.5
-    S["ry"] += S["rsp"] * speed
-
-    ch = np.full((rows, cols), " ", dtype="U1")
-    co = np.zeros((rows, cols, 3), dtype=np.uint8)
-    heads = S["ry"].astype(int)
-    for c in range(cols):
-        head = heads[c]
-        for i in range(S["rln"][c]):
-            row = head - i
-            if 0 <= row < rows:
-                fade = 1.0 - i / S["rln"][c]
-                ch[row, c] = pal[S["rch"][row, c] % len(pal)]
-                if i == 0:
-                    v = int(min(255, fade * 300))
-                    co[row, c] = (int(v*0.9), v, int(v*0.9))
-                else:
-                    v = int(fade * 240)
-                    co[row, c] = (int(v*0.1), v, int(v*0.4))
-    canvas_a = g.render(ch, co)
-
-    # Layer 2: Tunnel on sm grid for depth texture
-    canvas_b = _render_vf(r, "sm",
-        lambda g, f, t, S: vf_tunnel(g, f, t, S, speed=5.0, complexity=10),
-        hf_distance(0.3, 0.02), PAL_BLOCKS, f, t, S, sat=0.6)
-
-    return blend_canvas(canvas_a, canvas_b, "screen", 0.5)
-```
-
----
-
-## Scene Table
-
-The scene table defines the timeline: which scene plays when, with what configuration.
-
-### Structure
-
-```python
-SCENES = [
-    {
-        "start": 0.0,           # start time in seconds
-        "end": 3.96,            # end time in seconds
-        "name": "starfield",    # identifier (used for clip filenames)
-        "grid": "sm",           # default grid (for render_clip setup)
-        "fx": fx_starfield,     # scene function reference (must be module-level)
-        "gamma": 0.75,          # tonemap gamma override (default 0.75)
-        "shaders": [            # shader chain (applied after tonemap + feedback)
-            ("bloom", {"thr": 120}),
-            ("vignette", {"s": 0.2}),
-            ("grain", {"amt": 8}),
-        ],
-        "feedback": None,       # feedback buffer config (None = disabled)
-        # "feedback": {"decay": 0.8, "blend": "screen", "opacity": 0.3,
-        #              "transform": "zoom", "transform_amt": 0.02, "hue_shift": 0.02},
-    },
-    {
-        "start": 3.96,
-        "end": 6.58,
-        "name": "matrix_layered",
-        "grid": "md",
-        "fx": fx_matrix_layered,
-        "shaders": [
-            ("crt", {"strength": 0.05}),
-            ("scanlines", {"intensity": 0.12}),
-            ("color_grade", {"tint": (0.7, 1.2, 0.7)}),
-            ("bloom", {"thr": 100}),
-        ],
-        "feedback": {"decay": 0.5, "blend": "add", "opacity": 0.2},
-    },
-    # ... more scenes ...
-]
-```
-
-### Beat-Synced Scene Cutting
-
-Derive cut points from audio analysis:
-
-```python
-# Get beat timestamps
-beats = [fi / FPS for fi in range(N_FRAMES) if features["beat"][fi] > 0.5]
-
-# Group beats into phrase boundaries (every 4-8 beats)
-cuts = [0.0]
-for i in range(0, len(beats), 4):  # cut every 4 beats
-    cuts.append(beats[i])
-cuts.append(DURATION)
-
-# Or use the music's structure: silence gaps, energy changes
-energy = features["rms"]
-# Find timestamps where energy drops significantly -> natural break points
-```
-
-### `render_clip()` — The Render Loop
-
-This function renders one scene to a clip file:
-
-```python
-def render_clip(seg, features, clip_path):
-    r = Renderer()
-    r.set_grid(seg["grid"])
-    S = r.S
-    random.seed(hash(seg["id"]) + 42)  # deterministic per scene
-
-    # Build shader chain from config
-    chain = ShaderChain()
-    for shader_name, kwargs in seg.get("shaders", []):
-        chain.add(shader_name, **kwargs)
-
-    # Setup feedback buffer
-    fb = None
-    fb_cfg = seg.get("feedback", None)
-    if fb_cfg:
-        fb = FeedbackBuffer()
-
-    fx_fn = seg["fx"]
-
-    # Open ffmpeg pipe
-    cmd = ["ffmpeg", "-y", "-f", "rawvideo", "-pix_fmt", "rgb24",
-           "-s", f"{VW}x{VH}", "-r", str(FPS), "-i", "pipe:0",
-           "-c:v", "libx264", "-preset", "fast", "-crf", "20",
-           "-pix_fmt", "yuv420p", clip_path]
-    stderr_fh = open(clip_path.replace(".mp4", ".log"), "w")
-    pipe = subprocess.Popen(cmd, stdin=subprocess.PIPE,
-                            stdout=subprocess.DEVNULL, stderr=stderr_fh)
-
-    for fi in range(seg["frame_start"], seg["frame_end"]):
-        t = fi / FPS
-        feat = {k: float(features[k][fi]) for k in features}
-
-        # 1. Scene renders canvas
-        canvas = fx_fn(r, feat, t, S)
-
-        # 2. Tonemap normalizes brightness
-        canvas = tonemap(canvas, gamma=seg.get("gamma", 0.75))
-
-        # 3. Feedback adds temporal recursion
-        if fb and fb_cfg:
-            canvas = fb.apply(canvas, **{k: fb_cfg[k] for k in fb_cfg})
-
-        # 4. Shader chain adds post-processing
-        canvas = chain.apply(canvas, f=feat, t=t)
-
-        pipe.stdin.write(canvas.tobytes())
-
-    pipe.stdin.close(); pipe.wait(); stderr_fh.close()
-```
-
-### Building Segments from Scene Table
-
-```python
-segments = []
-for i, scene in enumerate(SCENES):
-    segments.append({
-        "id": f"s{i:02d}_{scene['name']}",
-        "name": scene["name"],
-        "grid": scene["grid"],
-        "fx": scene["fx"],
-        "shaders": scene.get("shaders", []),
-        "feedback": scene.get("feedback", None),
-        "gamma": scene.get("gamma", 0.75),
-        "frame_start": int(scene["start"] * FPS),
-        "frame_end": int(scene["end"] * FPS),
-    })
-```
-
-### Parallel Rendering
-
-Scenes are independent units dispatched to a process pool:
-
-```python
-from concurrent.futures import ProcessPoolExecutor, as_completed
-
-with ProcessPoolExecutor(max_workers=N_WORKERS) as pool:
-    futures = {
-        pool.submit(render_clip, seg, features, clip_path): seg["id"]
-        for seg, clip_path in zip(segments, clip_paths)
-    }
-    for fut in as_completed(futures):
-        try:
-            fut.result()
-        except Exception as e:
-            log(f"ERROR {futures[fut]}: {e}")
-```
-
-**Pickling constraint**: `ProcessPoolExecutor` serializes arguments via pickle. Module-level functions can be pickled; lambdas and closures cannot. All `fx_*` scene functions MUST be defined at module level, not as closures or class methods.
-
-### Test-Frame Mode
-
-Render a single frame at a specific timestamp to verify visuals without a full render:
-
-```python
-if args.test_frame >= 0:
-    fi = min(int(args.test_frame * FPS), N_FRAMES - 1)
-    t = fi / FPS
-    feat = {k: float(features[k][fi]) for k in features}
-    scene = next(sc for sc in reversed(SCENES) if t >= sc["start"])
-    r = Renderer()
-    r.set_grid(scene["grid"])
-    canvas = scene["fx"](r, feat, t, r.S)
-    canvas = tonemap(canvas, gamma=scene.get("gamma", 0.75))
-    chain = ShaderChain()
-    for sn, kw in scene.get("shaders", []):
-        chain.add(sn, **kw)
-    canvas = chain.apply(canvas, f=feat, t=t)
-    Image.fromarray(canvas).save(f"test_{args.test_frame:.1f}s.png")
-    print(f"Mean brightness: {canvas.astype(float).mean():.1f}")
-```
-
-CLI: `python reel.py --test-frame 10.0`
-
----
-
-## Scene Design Checklist
-
-For each scene:
-
-1. **Choose 2-3 grid sizes** — different scales create interference
-2. **Choose different value fields** per layer — don't use the same effect on every grid
-3. **Choose different hue fields** per layer — or at minimum different hue offsets
-4. **Choose different palettes** per layer — mixing PAL_RUNE with PAL_BLOCKS looks different from PAL_RUNE with PAL_DENSE
-5. **Choose a blend mode** that matches the energy — screen for bright, difference for psychedelic, exclusion for subtle
-6. **Add conditional effects** on beat — kaleidoscope, mirror, glitch
-7. **Configure feedback** for trailing/recursive looks — or None for clean cuts
-8. **Set gamma** if using destructive shaders (solarize, posterize)
-9. **Test with --test-frame** at the scene's midpoint before full render

skills/creative/ascii-video/references/troubleshooting.md

@@ -1,331 +0,0 @@
-# Troubleshooting Reference
-
-Common bugs, gotchas, and platform-specific issues encountered during ASCII video development.
-
-## NumPy Broadcasting
-
-### The `broadcast_to().copy()` Trap
-
-Hue field generators often return arrays that are broadcast views — they have shape `(1, cols)` or `(rows, 1)` that numpy broadcasts to `(rows, cols)`. These views are **read-only**. If any downstream code tries to modify them in-place (e.g., `h %= 1.0`), numpy raises:
-
-```
-ValueError: output array is read-only
-```
-
-**Fix**: Always `.copy()` after `broadcast_to()`:
-
-```python
-h = np.broadcast_to(h, (g.rows, g.cols)).copy()
-```
-
-This is especially important in `_render_vf()` where hue arrays flow through `hsv2rgb()`.
-
-### The `+=` vs `+` Trap
-
-Broadcasting also fails with in-place operators when operand shapes don't match exactly:
-
-```python
-# FAILS if result is (rows,1) and operand is (rows, cols)
-val += np.sin(g.cc * 0.02 + t * 0.3) * 0.5
-
-# WORKS — creates a new array
-val = val + np.sin(g.cc * 0.02 + t * 0.3) * 0.5
-```
-
-The `vf_plasma()` function had this bug. Use `+` instead of `+=` when mixing different-shaped arrays.
-
-### Shape Mismatch in `hsv2rgb()`
-
-`hsv2rgb(h, s, v)` requires all three arrays to have identical shapes. If `h` is `(1, cols)` and `s` is `(rows, cols)`, the function crashes or produces wrong output.
-
-**Fix**: Ensure all inputs are broadcast and copied to `(rows, cols)` before calling.
-
----
-
-## Blend Mode Pitfalls
-
-### Overlay Crushes Dark Inputs
-
-`overlay(a, b) = 2*a*b` when `a < 0.5`. Two values of 0.12 produce `2 * 0.12 * 0.12 = 0.03`. The result is darker than either input.
-
-**Impact**: If both layers are dark (which ASCII art usually is), overlay produces near-black output.
-
-**Fix**: Use `screen` for dark source material. Screen always brightens: `1 - (1-a)*(1-b)`.
-
-### Colordodge Division by Zero
-
-`colordodge(a, b) = a / (1 - b)`. When `b = 1.0` (pure white pixels), this divides by zero.
-
-**Fix**: Add epsilon: `a / (1 - b + 1e-6)`. The implementation in `BLEND_MODES` should include this.
-
-### Colorburn Division by Zero
-
-`colorburn(a, b) = 1 - (1-a) / b`. When `b = 0` (pure black pixels), this divides by zero.
-
-**Fix**: Add epsilon: `1 - (1-a) / (b + 1e-6)`.
-
-### Multiply Always Darkens
-
-`multiply(a, b) = a * b`. Since both operands are [0,1], the result is always <= min(a,b). Never use multiply as a feedback blend mode — the frame goes black within a few frames.
-
-**Fix**: Use `screen` for feedback, or `add` with low opacity.
-
----
-
-## Multiprocessing
-
-### Pickling Constraints
-
-`ProcessPoolExecutor` serializes function arguments via pickle. This constrains what you can pass to workers:
-
-| Can Pickle | Cannot Pickle |
-|-----------|---------------|
-| Module-level functions (`def fx_foo():`) | Lambdas (`lambda x: x + 1`) |
-| Dicts, lists, numpy arrays | Closures (functions defined inside functions) |
-| Class instances (with `__reduce__`) | Instance methods |
-| Strings, numbers | File handles, sockets |
-
-**Impact**: All scene functions referenced in the SCENES table must be defined at module level with `def`. If you use a lambda or closure, you get:
-
-```
-_pickle.PicklingError: Can't pickle <function <lambda> at 0x...>
-```
-
-**Fix**: Define all scene functions at module top level. Lambdas used inside `_render_vf()` as val_fn/hue_fn are fine because they execute within the worker process — they're not pickled across process boundaries.
-
-### macOS spawn vs Linux fork
-
-On macOS, `multiprocessing` defaults to `spawn` (full serialization). On Linux, it defaults to `fork` (copy-on-write). This means:
-
-- **macOS**: Feature arrays are serialized per worker (~57KB for 30s video, but scales with duration). Each worker re-imports the entire module.
-- **Linux**: Feature arrays are shared via COW. Workers inherit the parent's memory.
-
-**Impact**: On macOS, module-level code (like `detect_hardware()`) runs in every worker process. If it has side effects (e.g., subprocess calls), those happen N+1 times.
-
-### Per-Worker State Isolation
-
-Each worker creates its own:
-- `Renderer` instance (with fresh grid cache)
-- `FeedbackBuffer` (feedback doesn't cross scene boundaries)
-- Random seed (`random.seed(hash(seg_id) + 42)`)
-
-This means:
-- Particle state doesn't carry between scenes (expected)
-- Feedback trails reset at scene cuts (expected)
-- `np.random` state is NOT seeded by `random.seed()` — they use separate RNGs
-
-**Fix for deterministic noise**: Use `np.random.RandomState(seed)` explicitly:
-
-```python
-rng = np.random.RandomState(hash(seg_id) + 42)
-noise = rng.random((rows, cols))
-```
-
----
-
-## Brightness Issues
-
-### Dark Scenes After Tonemap
-
-If a scene is still dark after tonemap, check:
-
-1. **Gamma too high**: Lower gamma (0.5-0.6) for scenes with destructive post-processing
-2. **Shader destroying brightness**: Solarize, posterize, or contrast adjustments in the shader chain can undo tonemap's work. Move destructive shaders earlier in the chain, or increase gamma to compensate.
-3. **Feedback with multiply**: Multiply feedback darkens every frame. Switch to screen or add.
-4. **Overlay blend in scene**: If the scene function uses `blend_canvas(..., "overlay", ...)` with dark layers, switch to screen.
-
-### Diagnostic: Test-Frame Brightness
-
-```bash
-python reel.py --test-frame 10.0
-# Output: Mean brightness: 44.3, max: 255
-```
-
-If mean < 20, the scene needs attention. Common fixes:
-- Lower gamma in the SCENES entry
-- Change internal blend modes from overlay/multiply to screen/add
-- Increase value field multipliers (e.g., `vf_plasma(...) * 1.5`)
-- Check that the shader chain doesn't have an aggressive solarize or threshold
-
-### v1 Brightness Pattern (Deprecated)
-
-The old pattern used a linear multiplier:
-
-```python
-# OLD — don't use
-canvas = np.clip(canvas.astype(np.float32) * 2.0, 0, 255).astype(np.uint8)
-```
-
-This fails because:
-- Dark scenes (mean 8): `8 * 2.0 = 16` — still dark
-- Bright scenes (mean 130): `130 * 2.0 = 255` — clipped, lost detail
-
-Use `tonemap()` instead. See `composition.md` § Adaptive Tone Mapping.
-
----
-
-## ffmpeg Issues
-
-### Pipe Deadlock
-
-The #1 production bug. If you use `stderr=subprocess.PIPE`:
-
-```python
-# DEADLOCK — stderr buffer fills at 64KB, blocks ffmpeg, blocks your writes
-pipe = subprocess.Popen(cmd, stdin=subprocess.PIPE, stderr=subprocess.PIPE)
-```
-
-**Fix**: Always redirect stderr to a file:
-
-```python
-stderr_fh = open(err_path, "w")
-pipe = subprocess.Popen(cmd, stdin=subprocess.PIPE,
-                        stdout=subprocess.DEVNULL, stderr=stderr_fh)
-```
-
-### Frame Count Mismatch
-
-If the number of frames written to the pipe doesn't match what ffmpeg expects (based on `-r` and duration), the output may have:
-- Missing frames at the end
-- Incorrect duration
-- Audio-video desync
-
-**Fix**: Calculate frame count explicitly: `n_frames = int(duration * FPS)`. Don't use `range(int(start*FPS), int(end*FPS))` without verifying the total matches.
-
-### Concat Fails with "unsafe file name"
-
-```
-[concat @ ...] Unsafe file name
-```
-
-**Fix**: Always use `-safe 0`:
-```python
-["ffmpeg", "-f", "concat", "-safe", "0", "-i", concat_path, ...]
-```
-
----
-
-## Font Issues
-
-### Cell Height (macOS Pillow)
-
-`textbbox()` and `getbbox()` return incorrect heights on some macOS Pillow versions. Use `getmetrics()`:
-
-```python
-ascent, descent = font.getmetrics()
-cell_height = ascent + descent  # correct
-# NOT: font.getbbox("M")[3]  # wrong on some versions
-```
-
-### Missing Unicode Glyphs
-
-Not all fonts render all Unicode characters. If a palette character isn't in the font, the glyph renders as a blank or tofu box, appearing as a dark hole in the output.
-
-**Fix**: Validate at init:
-
-```python
-all_chars = set()
-for pal in [PAL_DEFAULT, PAL_DENSE, PAL_RUNE, ...]:
-    all_chars.update(pal)
-
-valid_chars = set()
-for c in all_chars:
-    if c == " ":
-        valid_chars.add(c)
-        continue
-    img = Image.new("L", (20, 20), 0)
-    ImageDraw.Draw(img).text((0, 0), c, fill=255, font=font)
-    if np.array(img).max() > 0:
-        valid_chars.add(c)
-    else:
-        log(f"WARNING: '{c}' (U+{ord(c):04X}) missing from font")
-```
-
-### Platform Font Paths
-
-| Platform | Common Paths |
-|----------|-------------|
-| macOS | `/System/Library/Fonts/Menlo.ttc`, `/System/Library/Fonts/Monaco.ttf` |
-| Linux | `/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf` |
-| Windows | `C:\Windows\Fonts\consola.ttf` (Consolas) |
-
-Always probe multiple paths and fall back gracefully. See `architecture.md` § Font Selection.
-
----
-
-## Performance
-
-### Slow Shaders
-
-Some shaders use Python loops and are very slow at 1080p:
-
-| Shader | Issue | Fix |
-|--------|-------|-----|
-| `wave_distort` | Per-row Python loop | Use vectorized fancy indexing |
-| `halftone` | Triple-nested loop | Vectorize with block reduction |
-| `matrix rain` | Per-column per-trail loop | Accumulate index arrays, bulk assign |
-
-### Render Time Scaling
-
-If render is taking much longer than expected:
-1. Check grid count — each extra grid adds ~100-150ms/frame for init
-2. Check particle count — cap at quality-appropriate limits
-3. Check shader count — each shader adds 2-25ms
-4. Check for accidental Python loops in effects (should be numpy only)
-
----
-
-## Common Mistakes
-
-### Using `r.S` vs the `S` Parameter
-
-The v2 scene protocol passes `S` (the state dict) as an explicit parameter. But `S` IS `r.S` — they're the same object. Both work:
-
-```python
-def fx_scene(r, f, t, S):
-    S["counter"] = S.get("counter", 0) + 1   # via parameter (preferred)
-    r.S["counter"] = r.S.get("counter", 0) + 1  # via renderer (also works)
-```
-
-Use the `S` parameter for clarity. The explicit parameter makes it obvious that the function has persistent state.
-
-### Forgetting to Handle Empty Feature Values
-
-Audio features default to 0.0 if the audio is silent. Use `.get()` with sensible defaults:
-
-```python
-energy = f.get("bass", 0.3)  # default to 0.3, not 0
-```
-
-If you default to 0, effects go blank during silence.
-
-### Writing New Files Instead of Editing Existing State
-
-A common bug in particle systems: creating new arrays every frame instead of updating persistent state.
-
-```python
-# WRONG — particles reset every frame
-S["px"] = []
-for _ in range(100):
-    S["px"].append(random.random())
-
-# RIGHT — only initialize once, update each frame
-if "px" not in S:
-    S["px"] = []
-# ... emit new particles based on beats
-# ... update existing particles
-```
-
-### Not Clipping Value Fields
-
-Value fields should be [0, 1]. If they exceed this range, `val2char()` produces index errors:
-
-```python
-# WRONG — vf_plasma() * 1.5 can exceed 1.0
-val = vf_plasma(g, f, t, S) * 1.5
-
-# RIGHT — clip after scaling
-val = np.clip(vf_plasma(g, f, t, S) * 1.5, 0, 1)
-```
-
-The `_render_vf()` helper clips automatically, but if you're building custom scenes, clip explicitly.

skills/gaming/pokemon-player/SKILL.md

@@ -1,215 +0,0 @@
----
-name: pokemon-player
-description: Play Pokemon games autonomously via headless emulation. Starts a game server, reads structured game state from RAM, makes strategic decisions, and sends button inputs — all from the terminal.
-tags: [gaming, pokemon, emulator, pyboy, gameplay, gameboy]
----
-# Pokemon Player
-
-Play Pokemon games via headless emulation using the `pokemon-agent` package.
-
-## When to Use
-- User says "play pokemon", "start pokemon", "pokemon game"
-- User asks about Pokemon Red, Blue, Yellow, FireRed, etc.
-- User wants to watch an AI play Pokemon
-- User references a ROM file (.gb, .gbc, .gba)
-
-## Startup Procedure
-
-### 1. First-time setup (clone, venv, install)
-The repo is NousResearch/pokemon-agent on GitHub. Clone it, then
-set up a Python 3.10+ virtual environment. Use uv (preferred for speed)
-to create the venv and install the package in editable mode with the
-pyboy extra. If uv is not available, fall back to python3 -m venv + pip.
-
-On this machine it is already set up at /home/teknium/pokemon-agent
-with a venv ready — just cd there and source .venv/bin/activate.
-
-You also need a ROM file. Ask the user for theirs. On this machine
-one exists at roms/pokemon_red.gb inside that directory.
-NEVER download or provide ROM files — always ask the user.
-
-### 2. Start the game server
-From inside the pokemon-agent directory with the venv activated, run
-pokemon-agent serve with --rom pointing to the ROM and --port 9876.
-Run it in the background with &.
-To resume from a saved game, add --load-state with the save name.
-Wait 4 seconds for startup, then verify with GET /health.
-
-### 3. Set up live dashboard for user to watch
-Use an SSH reverse tunnel via localhost.run so the user can view
-the dashboard in their browser. Connect with ssh, forwarding local
-port 9876 to remote port 80 on nokey@localhost.run. Redirect output
-to a log file, wait 10 seconds, then grep the log for the .lhr.life
-URL. Give the user the URL with /dashboard/ appended.
-The tunnel URL changes each time — give the user the new one if restarted.
-
-## Save and Load
-
-### When to save
-- Every 15-20 turns of gameplay
-- ALWAYS before gym battles, rival encounters, or risky fights
-- Before entering a new town or dungeon
-- Before any action you are unsure about
-
-### How to save
-POST /save with a descriptive name. Good examples:
-before_brock, route1_start, mt_moon_entrance, got_cut
-
-### How to load
-POST /load with the save name.
-
-### List available saves
-GET /saves returns all saved states.
-
-### Loading on server startup
-Use --load-state flag when starting the server to auto-load a save.
-This is faster than loading via the API after startup.
-
-## The Gameplay Loop
-
-### Step 1: OBSERVE — check state AND take a screenshot
-GET /state for position, HP, battle, dialog.
-GET /screenshot and save to /tmp/pokemon.png, then use vision_analyze.
-Always do BOTH — RAM state gives numbers, vision gives spatial awareness.
-
-### Step 2: ORIENT
-- Dialog/text on screen → advance it
-- In battle → fight or run
-- Party hurt → head to Pokemon Center
-- Near objective → navigate carefully
-
-### Step 3: DECIDE
-Priority: dialog > battle > heal > story objective > training > explore
-
-### Step 4: ACT — move 2-4 steps max, then re-check
-POST /action with a SHORT action list (2-4 actions, not 10-15).
-
-### Step 5: VERIFY — screenshot after every move sequence
-Take a screenshot and use vision_analyze to confirm you moved where
-intended. This is the MOST IMPORTANT step. Without vision you WILL get lost.
-
-### Step 6: RECORD progress to memory with PKM: prefix
-
-### Step 7: SAVE periodically
-
-## Action Reference
-- press_a — confirm, talk, select
-- press_b — cancel, close menu
-- press_start — open game menu
-- walk_up/down/left/right — move one tile
-- hold_b_N — hold B for N frames (use for speeding through text)
-- wait_60 — wait about 1 second (60 frames)
-- a_until_dialog_end — press A repeatedly until dialog clears
-
-## Critical Tips from Experience
-
-### USE VISION CONSTANTLY
-- Take a screenshot every 2-4 movement steps
-- The RAM state tells you position and HP but NOT what is around you
-- Ledges, fences, signs, building doors, NPCs — only visible via screenshot
-- Ask the vision model specific questions: "what is one tile north of me?"
-- When stuck, always screenshot before trying random directions
-
-### Warp Transitions Need Extra Wait Time
-When walking through a door or stairs, the screen fades to black during
-the map transition. You MUST wait for it to complete. Add 2-3 wait_60
-actions after any door/stair warp. Without waiting, the position reads
-as stale and you will think you are still in the old map.
-
-### Building Exit Trap
-When you exit a building, you appear directly IN FRONT of the door.
-If you walk north, you go right back inside. ALWAYS sidestep first
-by walking left or right 2 tiles, then proceed in your intended direction.
-
-### Dialog Handling
-Gen 1 text scrolls slowly letter-by-letter. To speed through dialog,
-hold B for 120 frames then press A. Repeat as needed. Holding B makes
-text display at max speed. Then press A to advance to the next line.
-The a_until_dialog_end action checks the RAM dialog flag, but this flag
-does not catch ALL text states. If dialog seems stuck, use the manual
-hold_b + press_a pattern instead and verify via screenshot.
-
-### Ledges Are One-Way
-Ledges (small cliff edges) can only be jumped DOWN (south), never climbed
-UP (north). If blocked by a ledge going north, you must go left or right
-to find the gap around it. Use vision to identify which direction the
-gap is. Ask the vision model explicitly.
-
-### Navigation Strategy
-- Move 2-4 steps at a time, then screenshot to check position
-- When entering a new area, screenshot immediately to orient
-- Ask the vision model "which direction to [destination]?"
-- If stuck for 3+ attempts, screenshot and re-evaluate completely
-- Do not spam 10-15 movements — you will overshoot or get stuck
-
-### Running from Wild Battles
-On the battle menu, RUN is bottom-right. To reach it from the default
-cursor position (FIGHT, top-left): press down then right to move cursor
-to RUN, then press A. Wrap with hold_b to speed through text/animations.
-
-### Battling (FIGHT)
-On the battle menu FIGHT is top-left (default cursor position).
-Press A to enter move selection, A again to use the first move.
-Then hold B to speed through attack animations and text.
-
-## Battle Strategy
-
-### Decision Tree
-1. Want to catch? → Weaken then throw Poke Ball
-2. Wild you don't need? → RUN
-3. Type advantage? → Use super-effective move
-4. No advantage? → Use strongest STAB move
-5. Low HP? → Switch or use Potion
-
-### Gen 1 Type Chart (key matchups)
-- Water beats Fire, Ground, Rock
-- Fire beats Grass, Bug, Ice
-- Grass beats Water, Ground, Rock
-- Electric beats Water, Flying
-- Ground beats Fire, Electric, Rock, Poison
-- Psychic beats Fighting, Poison (dominant in Gen 1!)
-
-### Gen 1 Quirks
-- Special stat = both offense AND defense for special moves
-- Psychic type is overpowered (Ghost moves bugged)
-- Critical hits based on Speed stat
-- Wrap/Bind prevent opponent from acting
-- Focus Energy bug: REDUCES crit rate instead of raising it
-
-## Memory Conventions
-| Prefix | Purpose | Example |
-|--------|---------|---------|
-| PKM:OBJECTIVE | Current goal | Get Parcel from Viridian Mart |
-| PKM:MAP | Navigation knowledge | Viridian: mart is northeast |
-| PKM:STRATEGY | Battle/team plans | Need Grass type before Misty |
-| PKM:PROGRESS | Milestone tracker | Beat rival, heading to Viridian |
-| PKM:STUCK | Stuck situations | Ledge at y=28 go right to bypass |
-| PKM:TEAM | Team notes | Squirtle Lv6, Tackle + Tail Whip |
-
-## Progression Milestones
-- Choose starter
-- Deliver Parcel from Viridian Mart, receive Pokedex
-- Boulder Badge — Brock (Rock) → use Water/Grass
-- Cascade Badge — Misty (Water) → use Grass/Electric
-- Thunder Badge — Lt. Surge (Electric) → use Ground
-- Rainbow Badge — Erika (Grass) → use Fire/Ice/Flying
-- Soul Badge — Koga (Poison) → use Ground/Psychic
-- Marsh Badge — Sabrina (Psychic) → hardest gym
-- Volcano Badge — Blaine (Fire) → use Water/Ground
-- Earth Badge — Giovanni (Ground) → use Water/Grass/Ice
-- Elite Four → Champion!
-
-## Stopping Play
-1. Save the game with a descriptive name via POST /save
-2. Update memory with PKM:PROGRESS
-3. Tell user: "Game saved as [name]! Say 'play pokemon' to resume."
-4. Kill the server and tunnel background processes
-
-## Pitfalls
-- NEVER download or provide ROM files
-- Do NOT send more than 4-5 actions without checking vision
-- Always sidestep after exiting buildings before going north
-- Always add wait_60 x2-3 after door/stair warps
-- Dialog detection via RAM is unreliable — verify with screenshots
-- Save BEFORE risky encounters
-- The tunnel URL changes each time you restart it

skills/leisure/find-nearby/SKILL.md

@@ -1,69 +0,0 @@
----
-name: find-nearby
-description: Find nearby places (restaurants, cafes, bars, pharmacies, etc.) using OpenStreetMap. Works with coordinates, addresses, cities, zip codes, or Telegram location pins. No API keys needed.
-version: 1.0.0
-metadata:
-  hermes:
-    tags: [location, maps, nearby, places, restaurants, local]
-    related_skills: []
----
-
-# Find Nearby — Local Place Discovery
-
-Find restaurants, cafes, bars, pharmacies, and other places near any location. Uses OpenStreetMap (free, no API keys). Works with:
-
-- **Coordinates** from Telegram location pins (latitude/longitude in conversation)
-- **Addresses** ("near 123 Main St, Springfield")
-- **Cities** ("restaurants in downtown Austin")
-- **Zip codes** ("pharmacies near 90210")
-- **Landmarks** ("cafes near Times Square")
-
-## Quick Reference
-
-```bash
-# By coordinates (from Telegram location pin or user-provided)
-python3 SKILL_DIR/scripts/find_nearby.py --lat <LAT> --lon <LON> --type restaurant --radius 1500
-
-# By address, city, or landmark (auto-geocoded)
-python3 SKILL_DIR/scripts/find_nearby.py --near "Times Square, New York" --type cafe
-
-# Multiple place types
-python3 SKILL_DIR/scripts/find_nearby.py --near "downtown austin" --type restaurant --type bar --limit 10
-
-# JSON output
-python3 SKILL_DIR/scripts/find_nearby.py --near "90210" --type pharmacy --json
-```
-
-### Parameters
-
-| Flag | Description | Default |
-|------|-------------|---------|
-| `--lat`, `--lon` | Exact coordinates | — |
-| `--near` | Address, city, zip, or landmark (geocoded) | — |
-| `--type` | Place type (repeatable for multiple) | restaurant |
-| `--radius` | Search radius in meters | 1500 |
-| `--limit` | Max results | 15 |
-| `--json` | Machine-readable JSON output | off |
-
-### Common Place Types
-
-`restaurant`, `cafe`, `bar`, `pub`, `fast_food`, `pharmacy`, `hospital`, `bank`, `atm`, `fuel`, `parking`, `supermarket`, `convenience`, `hotel`
-
-## Workflow
-
-1. **Get the location.** Look for coordinates (`latitude: ... / longitude: ...`) from a Telegram pin, or ask the user for an address/city/zip.
-
-2. **Ask for preferences** (only if not already stated): place type, how far they're willing to go, any specifics (cuisine, "open now", etc.).
-
-3. **Run the script** with appropriate flags. Use `--json` if you need to process results programmatically.
-
-4. **Present results** with names, distances, and Google Maps links. If the user asked about hours or "open now," check the `hours` field in results — if missing or unclear, verify with `web_search`.
-
-5. **For directions**, use the `directions_url` from results, or construct: `https://www.google.com/maps/dir/?api=1&origin=<LAT>,<LON>&destination=<LAT>,<LON>`
-
-## Tips
-
-- If results are sparse, widen the radius (1500 → 3000m)
-- For "open now" requests: check the `hours` field in results, cross-reference with `web_search` for accuracy since OSM hours aren't always complete
-- Zip codes alone can be ambiguous globally — prompt the user for country/state if results look wrong
-- The script uses OpenStreetMap data which is community-maintained; coverage varies by region

skills/leisure/find-nearby/scripts/find_nearby.py

@@ -1,184 +0,0 @@
-#!/usr/bin/env python3
-"""Find nearby places using OpenStreetMap (Overpass + Nominatim). No API keys needed.
-
-Usage:
-    # By coordinates
-    python find_nearby.py --lat 36.17 --lon -115.14 --type restaurant --radius 1500
-
-    # By address/city/zip (auto-geocoded)
-    python find_nearby.py --near "Times Square, New York" --type cafe --radius 1000
-    python find_nearby.py --near "90210" --type pharmacy
-
-    # Multiple types
-    python find_nearby.py --lat 36.17 --lon -115.14 --type restaurant --type bar
-
-    # JSON output for programmatic use
-    python find_nearby.py --near "downtown las vegas" --type restaurant --json
-"""
-
-import argparse
-import json
-import math
-import sys
-import urllib.parse
-import urllib.request
-from typing import Any
-
-OVERPASS_URLS = [
-    "https://overpass-api.de/api/interpreter",
-    "https://overpass.kumi.systems/api/interpreter",
-]
-NOMINATIM_URL = "https://nominatim.openstreetmap.org/search"
-USER_AGENT = "HermesAgent/1.0 (find-nearby skill)"
-TIMEOUT = 15
-
-
-def _http_get(url: str) -> Any:
-    req = urllib.request.Request(url, headers={"User-Agent": USER_AGENT})
-    with urllib.request.urlopen(req, timeout=TIMEOUT) as r:
-        return json.loads(r.read())
-
-
-def _http_post(url: str, data: str) -> Any:
-    req = urllib.request.Request(
-        url, data=data.encode(), headers={"User-Agent": USER_AGENT}
-    )
-    with urllib.request.urlopen(req, timeout=TIMEOUT) as r:
-        return json.loads(r.read())
-
-
-def haversine(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
-    """Distance in meters between two coordinates."""
-    R = 6_371_000
-    rlat1, rlat2 = math.radians(lat1), math.radians(lat2)
-    dlat = math.radians(lat2 - lat1)
-    dlon = math.radians(lon2 - lon1)
-    a = math.sin(dlat / 2) ** 2 + math.cos(rlat1) * math.cos(rlat2) * math.sin(dlon / 2) ** 2
-    return R * 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
-
-
-def geocode(query: str) -> tuple[float, float]:
-    """Convert address/city/zip to coordinates via Nominatim."""
-    params = urllib.parse.urlencode({"q": query, "format": "json", "limit": 1})
-    results = _http_get(f"{NOMINATIM_URL}?{params}")
-    if not results:
-        print(f"Error: Could not geocode '{query}'. Try a more specific address.", file=sys.stderr)
-        sys.exit(1)
-    return float(results[0]["lat"]), float(results[0]["lon"])
-
-
-def find_nearby(lat: float, lon: float, types: list[str], radius: int = 1500, limit: int = 15) -> list[dict]:
-    """Query Overpass for nearby amenities."""
-    # Build Overpass QL query
-    type_filters = "".join(
-        f'nwr["amenity"="{t}"](around:{radius},{lat},{lon});' for t in types
-    )
-    query = f"[out:json][timeout:{TIMEOUT}];({type_filters});out center tags;"
-
-    # Try each Overpass server
-    data = None
-    for url in OVERPASS_URLS:
-        try:
-            data = _http_post(url, f"data={urllib.parse.quote(query)}")
-            break
-        except Exception:
-            continue
-
-    if not data:
-        return []
-
-    # Parse results
-    places = []
-    for el in data.get("elements", []):
-        tags = el.get("tags", {})
-        name = tags.get("name")
-        if not name:
-            continue
-
-        # Get coordinates (nodes have lat/lon directly, ways/relations use center)
-        plat = el.get("lat") or (el.get("center", {}) or {}).get("lat")
-        plon = el.get("lon") or (el.get("center", {}) or {}).get("lon")
-        if not plat or not plon:
-            continue
-
-        dist = haversine(lat, lon, plat, plon)
-
-        place = {
-            "name": name,
-            "type": tags.get("amenity", ""),
-            "distance_m": round(dist),
-            "lat": plat,
-            "lon": plon,
-            "maps_url": f"https://www.google.com/maps/search/?api=1&query={plat},{plon}",
-            "directions_url": f"https://www.google.com/maps/dir/?api=1&origin={lat},{lon}&destination={plat},{plon}",
-        }
-
-        # Add useful optional fields
-        if tags.get("cuisine"):
-            place["cuisine"] = tags["cuisine"]
-        if tags.get("opening_hours"):
-            place["hours"] = tags["opening_hours"]
-        if tags.get("phone"):
-            place["phone"] = tags["phone"]
-        if tags.get("website"):
-            place["website"] = tags["website"]
-        if tags.get("addr:street"):
-            addr_parts = [tags.get("addr:housenumber", ""), tags.get("addr:street", "")]
-            if tags.get("addr:city"):
-                addr_parts.append(tags["addr:city"])
-            place["address"] = " ".join(p for p in addr_parts if p)
-
-        places.append(place)
-
-    # Sort by distance, limit results
-    places.sort(key=lambda p: p["distance_m"])
-    return places[:limit]
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Find nearby places via OpenStreetMap")
-    parser.add_argument("--lat", type=float, help="Latitude")
-    parser.add_argument("--lon", type=float, help="Longitude")
-    parser.add_argument("--near", type=str, help="Address, city, or zip code (geocoded automatically)")
-    parser.add_argument("--type", action="append", dest="types", default=[], help="Place type (restaurant, cafe, bar, pharmacy, etc.)")
-    parser.add_argument("--radius", type=int, default=1500, help="Search radius in meters (default: 1500)")
-    parser.add_argument("--limit", type=int, default=15, help="Max results (default: 15)")
-    parser.add_argument("--json", action="store_true", dest="json_output", help="Output as JSON")
-    args = parser.parse_args()
-
-    # Resolve coordinates
-    if args.near:
-        lat, lon = geocode(args.near)
-    elif args.lat is not None and args.lon is not None:
-        lat, lon = args.lat, args.lon
-    else:
-        print("Error: Provide --lat/--lon or --near", file=sys.stderr)
-        sys.exit(1)
-
-    if not args.types:
-        args.types = ["restaurant"]
-
-    places = find_nearby(lat, lon, args.types, args.radius, args.limit)
-
-    if args.json_output:
-        print(json.dumps({"origin": {"lat": lat, "lon": lon}, "results": places, "count": len(places)}, indent=2))
-    else:
-        if not places:
-            print(f"No {'/'.join(args.types)} found within {args.radius}m")
-            return
-        print(f"Found {len(places)} places within {args.radius}m:\n")
-        for i, p in enumerate(places, 1):
-            dist_str = f"{p['distance_m']}m" if p["distance_m"] < 1000 else f"{p['distance_m']/1000:.1f}km"
-            print(f"  {i}. {p['name']} ({p['type']}) — {dist_str}")
-            if p.get("cuisine"):
-                print(f"     Cuisine: {p['cuisine']}")
-            if p.get("hours"):
-                print(f"     Hours: {p['hours']}")
-            if p.get("address"):
-                print(f"     Address: {p['address']}")
-            print(f"     Map: {p['maps_url']}")
-            print()
-
-
-if __name__ == "__main__":
-    main()

skills/mcp/native-mcp/SKILL.md

@@ -321,32 +321,6 @@ mcp_servers:
 
 All tools from all servers are registered and available simultaneously. Each server's tools are prefixed with its name to avoid collisions.
 
-## Sampling (Server-Initiated LLM Requests)
-
-Hermes supports MCP's `sampling/createMessage` capability — MCP servers can request LLM completions through the agent during tool execution. This enables agent-in-the-loop workflows (data analysis, content generation, decision-making).
-
-Sampling is **enabled by default**. Configure per server:
-
-```yaml
-mcp_servers:
-  my_server:
-    command: "npx"
-    args: ["-y", "my-mcp-server"]
-    sampling:
-      enabled: true           # default: true
-      model: "gemini-3-flash" # model override (optional)
-      max_tokens_cap: 4096    # max tokens per request
-      timeout: 30             # LLM call timeout (seconds)
-      max_rpm: 10             # max requests per minute
-      allowed_models: []      # model whitelist (empty = all)
-      max_tool_rounds: 5      # tool loop limit (0 = disable)
-      log_level: "info"       # audit verbosity
-```
-
-Servers can also include `tools` in sampling requests for multi-turn tool-augmented workflows. The `max_tool_rounds` config prevents infinite tool loops. Per-server audit metrics (requests, errors, tokens, tool use count) are tracked via `get_mcp_status()`.
-
-Disable sampling for untrusted servers with `sampling: { enabled: false }`.
-
 ## Notes
 
 - MCP tools are called synchronously from the agent's perspective but run asynchronously on a dedicated background event loop

skills/media/DESCRIPTION.md

@@ -1,3 +1 @@
----
-description: Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.
----
+Media content extraction and transformation tools — YouTube transcripts, audio, video processing.

skills/mlops/axolotl/references/other.md

@@ -1098,7 +1098,7 @@ Please see the ocifs docs.
 
 The path should start with https://.
 
-This must be publicly accessible.
+This must be publically accessible.
 
 Now that you know how to load datasets, you can learn more on how to load your specific dataset format into your target output format dataset formats docs.