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hermes-agent/tests/agent/test_skill_commands_reload.py

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refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
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"""Tests for ``agent.skill_commands.reload_skills``.
refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
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Covers the helper that powers ``/reload-skills`` (CLI + gateway slash command).
The helper rescans the skills directory and returns a diff of what changed.
It does NOT invalidate the skills system-prompt cache skills are invoked
at runtime via ``/skill-name``, ``skills_list``, or ``skill_view`` and don't
need to live in the system prompt.
``added`` and ``removed`` are lists of ``{"name": str, "description": str}``
dicts. Descriptions are truncated to 60 chars.
"""
import shutil
import tempfile
import textwrap
from pathlib import Path
import pytest
def _write_skill(skills_dir: Path, name: str, description: str = "") -> Path:
skill_dir = skills_dir / name
skill_dir.mkdir(parents=True, exist_ok=True)
(skill_dir / "SKILL.md").write_text(
textwrap.dedent(
f"""\
---
name: {name}
description: {description or f'{name} skill'}
---
body
"""
)
)
return skill_dir
@pytest.fixture
def hermes_home(monkeypatch):
"""Isolate HERMES_HOME for ``reload_skills`` tests.
Rather than popping cache-bearing modules from ``sys.modules`` (which
races against pytest-xdist's parallel workers), we monkeypatch the
module-level ``HERMES_HOME`` / ``SKILLS_DIR`` constants in place so the
isolation is local to this fixture's scope.
"""
td = tempfile.mkdtemp(prefix="hermes-reload-skills-")
monkeypatch.setenv("HERMES_HOME", td)
home = Path(td)
(home / "skills").mkdir(parents=True, exist_ok=True)
# Import lazily (inside fixture) so the modules are already resident,
# then redirect their captured paths at the new temp dir.
import tools.skills_tool as _st
import agent.skill_commands as _sc
monkeypatch.setattr(_st, "HERMES_HOME", home, raising=False)
monkeypatch.setattr(_st, "SKILLS_DIR", home / "skills", raising=False)
# Reset the in-process slash-command cache so each test starts from zero.
monkeypatch.setattr(_sc, "_skill_commands", {}, raising=False)
yield home
shutil.rmtree(td, ignore_errors=True)
class TestReloadSkillsHelper:
"""``agent.skill_commands.reload_skills``."""
def test_returns_expected_keys(self, hermes_home):
from agent.skill_commands import reload_skills
result = reload_skills()
assert set(result) == {"added", "removed", "unchanged", "total", "commands"}
assert result["total"] == 0
assert result["added"] == []
assert result["removed"] == []
refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
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def test_detects_newly_added_skill_with_description(self, hermes_home):
from agent.skill_commands import reload_skills, get_skill_commands
# Prime the cache so subsequent diff is meaningful
get_skill_commands()
refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
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_write_skill(hermes_home / "skills", "demo", "a demo skill")
result = reload_skills()
refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
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assert result["added"] == [{"name": "demo", "description": "a demo skill"}]
assert result["removed"] == []
assert result["total"] == 1
assert result["commands"] == 1
refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
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def test_detects_removed_skill_carries_description(self, hermes_home):
from agent.skill_commands import reload_skills
refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
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skill_dir = _write_skill(hermes_home / "skills", "demo", "soon to be gone")
# First reload: demo present
first = reload_skills()
assert first["total"] == 1
refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
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assert first["added"] == [{"name": "demo", "description": "soon to be gone"}]
refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
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# Remove and reload — the description must survive the removal diff
# (we cached it from the pre-rescan snapshot).
shutil.rmtree(skill_dir)
second = reload_skills()
refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
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assert second["removed"] == [{"name": "demo", "description": "soon to be gone"}]
assert second["added"] == []
assert second["total"] == 0
refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
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def test_description_passes_through_verbatim(self, hermes_home):
"""``description`` must be the full SKILL.md frontmatter string — no
truncation. The system prompt renders skills as
`` - name: description`` without a length cap, and the reload
note mirrors that format, so truncating here would make the diff
render differently from the original catalog."""
from agent.skill_commands import reload_skills, get_skill_commands
refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
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get_skill_commands() # prime
long_desc = "x" * 200
_write_skill(hermes_home / "skills", "longdesc", long_desc)
refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
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result = reload_skills()
assert len(result["added"]) == 1
assert result["added"][0]["description"] == long_desc
def test_unchanged_skills_appear_in_unchanged_list(self, hermes_home):
from agent.skill_commands import reload_skills, get_skill_commands
_write_skill(hermes_home / "skills", "alpha")
# Prime cache
get_skill_commands()
# Call reload again with no FS changes
result = reload_skills()
assert "alpha" in result["unchanged"]
assert result["added"] == []
assert result["removed"] == []
refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
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def test_does_not_invalidate_prompt_cache_snapshot(self, hermes_home):
"""reload_skills must NOT delete the skills prompt-cache snapshot.
refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
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Skills are called at runtime the system prompt doesn't need to
mention them for the model to use them so reloading them should
preserve prefix caching.
"""
from agent.prompt_builder import _skills_prompt_snapshot_path
from agent.skill_commands import reload_skills
refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
2026-04-29 20:39:15 -07:00
snapshot = _skills_prompt_snapshot_path()
snapshot.parent.mkdir(parents=True, exist_ok=True)
snapshot.write_text("{}")
assert snapshot.exists()
refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
2026-04-29 20:39:15 -07:00
reload_skills()
refactor(reload-skills): queue note for next turn, drop cache invalidation + agent tool Salvage-follow-up to @shannonsands's /reload-skills PR. Trims the feature to match the design: user-initiated rescan, no prompt-cache reset, no new schema surface, no phantom user turn, and the next-turn note carries each added/removed skill's 60-char description (not just its name). Changes vs the original PR: * Drop the in-process skills prompt-cache clear in reload_skills(). Skills are invoked at runtime via /skill-name, skills_list, or skill_view — they don't need to live in the system prompt for the model to use them. Keeping the cache intact preserves prefix caching across the reload so /reload-skills pays no cache-reset cost. (MCP has to break the cache because tool schemas must be known at conversation start; skills do not.) * Drop the skills_reload agent tool and SKILLS_RELOAD_SCHEMA from tools/skills_tool.py, plus the four skills_reload enumerations in toolsets.py. No new schema surface — agents can already see a freshly- installed skill via skill_view / skills_list the moment it's on disk. * Replace the phantom 'role: user' turn injection with a one-shot queued note. CLI uses self._pending_skills_reload_note (same pattern as _pending_model_switch_note, prepended to the next API call and cleared). Gateway uses self._pending_skills_reload_notes[session_key]. The note is prepended to the NEXT real user message in this session, so message alternation stays intact and nothing out-of-band is persisted to the transcript. * reload_skills() now returns added/removed as [{'name': str, 'description': str}, ...] (description truncated to 60 chars — matches the curator / gateway adapter budget). The injected next-turn note formats each entry as 'name — description' so the model can actually reason about which new skills to call without running skills_list first. * Only emit the note when the diff is non-empty. On empty diff, print 'No new skills detected' and do nothing else. * Tests rewritten to cover the queue semantics, the description payload, and a regression guard that the prompt-cache snapshot is preserved.
2026-04-29 20:39:15 -07:00
assert snapshot.exists(), (
"prompt cache snapshot should be preserved — skills don't live "
"in the system prompt so there's no reason to invalidate it"
)