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The skills directory was getting disorganized — mlops alone had 40 skills in a flat list, and 12 categories were singletons with just one skill each. Code change: - prompt_builder.py: Support sub-categories in skill scanner. skills/mlops/training/axolotl/SKILL.md now shows as category 'mlops/training' instead of just 'mlops'. Backwards-compatible with existing flat structure. Split mlops (40 skills) into 7 sub-categories: - mlops/training (12): accelerate, axolotl, flash-attention, grpo-rl-training, peft, pytorch-fsdp, pytorch-lightning, simpo, slime, torchtitan, trl-fine-tuning, unsloth - mlops/inference (8): gguf, guidance, instructor, llama-cpp, obliteratus, outlines, tensorrt-llm, vllm - mlops/models (6): audiocraft, clip, llava, segment-anything, stable-diffusion, whisper - mlops/vector-databases (4): chroma, faiss, pinecone, qdrant - mlops/evaluation (5): huggingface-tokenizers, lm-evaluation-harness, nemo-curator, saelens, weights-and-biases - mlops/cloud (2): lambda-labs, modal - mlops/research (1): dspy Merged singleton categories: - gifs → media (gif-search joins youtube-content) - music-creation → media (heartmula, songsee) - diagramming → creative (excalidraw joins ascii-art) - ocr-and-documents → productivity - domain → research (domain-intel) - feeds → research (blogwatcher) - market-data → research (polymarket) Fixed misplaced skills: - mlops/code-review → software-development (not ML-specific) - mlops/ml-paper-writing → research (academic writing) Added DESCRIPTION.md files for all new/updated categories.
108 lines
2.3 KiB
Markdown
108 lines
2.3 KiB
Markdown
# Real-World Examples
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Practical examples of using Instructor for structured data extraction.
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## Data Extraction
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```python
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class CompanyInfo(BaseModel):
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name: str
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founded: int
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industry: str
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employees: int
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text = "Apple was founded in 1976 in the technology industry with 164,000 employees."
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company = client.messages.create(
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model="claude-sonnet-4-5-20250929",
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max_tokens=1024,
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messages=[{"role": "user", "content": f"Extract: {text}"}],
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response_model=CompanyInfo
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)
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```
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## Classification
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```python
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class Sentiment(str, Enum):
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POSITIVE = "positive"
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NEGATIVE = "negative"
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NEUTRAL = "neutral"
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class Review(BaseModel):
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sentiment: Sentiment
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confidence: float = Field(ge=0.0, le=1.0)
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review = client.messages.create(
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model="claude-sonnet-4-5-20250929",
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max_tokens=1024,
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messages=[{"role": "user", "content": "This product is amazing!"}],
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response_model=Review
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)
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```
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## Multi-Entity Extraction
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```python
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class Person(BaseModel):
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name: str
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role: str
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class Entities(BaseModel):
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people: list[Person]
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organizations: list[str]
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locations: list[str]
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entities = client.messages.create(
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model="claude-sonnet-4-5-20250929",
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max_tokens=1024,
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messages=[{"role": "user", "content": "Tim Cook, CEO of Apple, spoke in Cupertino..."}],
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response_model=Entities
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)
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```
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## Structured Analysis
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```python
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class Analysis(BaseModel):
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summary: str
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key_points: list[str]
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sentiment: Sentiment
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actionable_items: list[str]
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analysis = client.messages.create(
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model="claude-sonnet-4-5-20250929",
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max_tokens=1024,
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messages=[{"role": "user", "content": "Analyze: [long text]"}],
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response_model=Analysis
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)
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```
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## Batch Processing
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```python
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texts = ["text1", "text2", "text3"]
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results = [
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client.messages.create(
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model="claude-sonnet-4-5-20250929",
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max_tokens=1024,
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messages=[{"role": "user", "content": text}],
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response_model=YourModel
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)
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for text in texts
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]
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```
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## Streaming
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```python
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for partial in client.messages.create_partial(
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model="claude-sonnet-4-5-20250929",
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max_tokens=1024,
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messages=[{"role": "user", "content": "Generate report..."}],
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response_model=Report
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):
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print(f"Progress: {partial.title}")
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# Update UI in real-time
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```
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