refactor: reorganize skills into sub-categories

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.
2026-04-25 00:51:20 +00:00 · 2026-03-09 03:35:53 -07:00 · 2026-03-09 03:35:53 -07:00 · 732c66b0f3
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--- a/skills/mlops/inference/instructor/SKILL.md
+++ b/skills/mlops/inference/instructor/SKILL.md
@ -0,0 +1,743 @@
+---
+name: instructor
+description: Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
+version: 1.0.0
+author: Orchestra Research
+license: MIT
+dependencies: [instructor, pydantic, openai, anthropic]
+metadata:
+  hermes:
+    tags: [Prompt Engineering, Instructor, Structured Output, Pydantic, Data Extraction, JSON Parsing, Type Safety, Validation, Streaming, OpenAI, Anthropic]
+
+---
+
+# Instructor: Structured LLM Outputs
+
+## When to Use This Skill
+
+Use Instructor when you need to:
+- **Extract structured data** from LLM responses reliably
+- **Validate outputs** against Pydantic schemas automatically
+- **Retry failed extractions** with automatic error handling
+- **Parse complex JSON** with type safety and validation
+- **Stream partial results** for real-time processing
+- **Support multiple LLM providers** with consistent API
+
+**GitHub Stars**: 15,000+ | **Battle-tested**: 100,000+ developers
+
+## Installation
+
+```bash
+# Base installation
+pip install instructor
+
+# With specific providers
+pip install "instructor[anthropic]"  # Anthropic Claude
+pip install "instructor[openai]"     # OpenAI
+pip install "instructor[all]"        # All providers
+```
+
+## Quick Start
+
+### Basic Example: Extract User Data
+
+```python
+import instructor
+from pydantic import BaseModel
+from anthropic import Anthropic
+
+# Define output structure
+class User(BaseModel):
+    name: str
+    age: int
+    email: str
+
+# Create instructor client
+client = instructor.from_anthropic(Anthropic())
+
+# Extract structured data
+user = client.messages.create(
+    model="claude-sonnet-4-5-20250929",
+    max_tokens=1024,
+    messages=[{
+        "role": "user",
+        "content": "John Doe is 30 years old. His email is john@example.com"
+    }],
+    response_model=User
+)
+
+print(user.name)   # "John Doe"
+print(user.age)    # 30
+print(user.email)  # "john@example.com"
+```
+
+### With OpenAI
+
+```python
+from openai import OpenAI
+
+client = instructor.from_openai(OpenAI())
+
+user = client.chat.completions.create(
+    model="gpt-4o-mini",
+    response_model=User,
+    messages=[{"role": "user", "content": "Extract: Alice, 25, alice@email.com"}]
+)
+```
+
+## Core Concepts
+
+### 1. Response Models (Pydantic)
+
+Response models define the structure and validation rules for LLM outputs.
+
+#### Basic Model
+
+```python
+from pydantic import BaseModel, Field
+
+class Article(BaseModel):
+    title: str = Field(description="Article title")
+    author: str = Field(description="Author name")
+    word_count: int = Field(description="Number of words", gt=0)
+    tags: list[str] = Field(description="List of relevant tags")
+
+article = client.messages.create(
+    model="claude-sonnet-4-5-20250929",
+    max_tokens=1024,
+    messages=[{
+        "role": "user",
+        "content": "Analyze this article: [article text]"
+    }],
+    response_model=Article
+)
+```
+
+**Benefits:**
+- Type safety with Python type hints
+- Automatic validation (word_count > 0)
+- Self-documenting with Field descriptions
+- IDE autocomplete support
+
+#### Nested Models
+
+```python
+class Address(BaseModel):
+    street: str
+    city: str
+    country: str
+
+class Person(BaseModel):
+    name: str
+    age: int
+    address: Address  # Nested model
+
+person = client.messages.create(
+    model="claude-sonnet-4-5-20250929",
+    max_tokens=1024,
+    messages=[{
+        "role": "user",
+        "content": "John lives at 123 Main St, Boston, USA"
+    }],
+    response_model=Person
+)
+
+print(person.address.city)  # "Boston"
+```
+
+#### Optional Fields
+
+```python
+from typing import Optional
+
+class Product(BaseModel):
+    name: str
+    price: float
+    discount: Optional[float] = None  # Optional
+    description: str = Field(default="No description")  # Default value
+
+# LLM doesn't need to provide discount or description
+```
+
+#### Enums for Constraints
+
+```python
+from enum import Enum
+
+class Sentiment(str, Enum):
+    POSITIVE = "positive"
+    NEGATIVE = "negative"
+    NEUTRAL = "neutral"
+
+class Review(BaseModel):
+    text: str
+    sentiment: Sentiment  # Only these 3 values allowed
+
+review = client.messages.create(
+    model="claude-sonnet-4-5-20250929",
+    max_tokens=1024,
+    messages=[{
+        "role": "user",
+        "content": "This product is amazing!"
+    }],
+    response_model=Review
+)
+
+print(review.sentiment)  # Sentiment.POSITIVE
+```
+
+### 2. Validation
+
+Pydantic validates LLM outputs automatically. If validation fails, Instructor retries.
+
+#### Built-in Validators
+
+```python
+from pydantic import Field, EmailStr, HttpUrl
+
+class Contact(BaseModel):
+    name: str = Field(min_length=2, max_length=100)
+    age: int = Field(ge=0, le=120)  # 0 <= age <= 120
+    email: EmailStr  # Validates email format
+    website: HttpUrl  # Validates URL format
+
+# If LLM provides invalid data, Instructor retries automatically
+```
+
+#### Custom Validators
+
+```python
+from pydantic import field_validator
+
+class Event(BaseModel):
+    name: str
+    date: str
+    attendees: int
+
+    @field_validator('date')
+    def validate_date(cls, v):
+        """Ensure date is in YYYY-MM-DD format."""
+        import re
+        if not re.match(r'\d{4}-\d{2}-\d{2}', v):
+            raise ValueError('Date must be YYYY-MM-DD format')
+        return v
+
+    @field_validator('attendees')
+    def validate_attendees(cls, v):
+        """Ensure positive attendees."""
+        if v < 1:
+            raise ValueError('Must have at least 1 attendee')
+        return v
+```
+
+#### Model-Level Validation
+
+```python
+from pydantic import model_validator
+
+class DateRange(BaseModel):
+    start_date: str
+    end_date: str
+
+    @model_validator(mode='after')
+    def check_dates(self):
+        """Ensure end_date is after start_date."""
+        from datetime import datetime
+        start = datetime.strptime(self.start_date, '%Y-%m-%d')
+        end = datetime.strptime(self.end_date, '%Y-%m-%d')
+
+        if end < start:
+            raise ValueError('end_date must be after start_date')
+        return self
+```
+
+### 3. Automatic Retrying
+
+Instructor retries automatically when validation fails, providing error feedback to the LLM.
+
+```python
+# Retries up to 3 times if validation fails
+user = client.messages.create(
+    model="claude-sonnet-4-5-20250929",
+    max_tokens=1024,
+    messages=[{
+        "role": "user",
+        "content": "Extract user from: John, age unknown"
+    }],
+    response_model=User,
+    max_retries=3  # Default is 3
+)
+
+# If age can't be extracted, Instructor tells the LLM:
+# "Validation error: age - field required"
+# LLM tries again with better extraction
+```
+
+**How it works:**
+1. LLM generates output
+2. Pydantic validates
+3. If invalid: Error message sent back to LLM
+4. LLM tries again with error feedback
+5. Repeats up to max_retries
+
+### 4. Streaming
+
+Stream partial results for real-time processing.
+
+#### Streaming Partial Objects
+
+```python
+from instructor import Partial
+
+class Story(BaseModel):
+    title: str
+    content: str
+    tags: list[str]
+
+# Stream partial updates as LLM generates
+for partial_story in client.messages.create_partial(
+    model="claude-sonnet-4-5-20250929",
+    max_tokens=1024,
+    messages=[{
+        "role": "user",
+        "content": "Write a short sci-fi story"
+    }],
+    response_model=Story
+):
+    print(f"Title: {partial_story.title}")
+    print(f"Content so far: {partial_story.content[:100]}...")
+    # Update UI in real-time
+```
+
+#### Streaming Iterables
+
+```python
+class Task(BaseModel):
+    title: str
+    priority: str
+
+# Stream list items as they're generated
+tasks = client.messages.create_iterable(
+    model="claude-sonnet-4-5-20250929",
+    max_tokens=1024,
+    messages=[{
+        "role": "user",
+        "content": "Generate 10 project tasks"
+    }],
+    response_model=Task
+)
+
+for task in tasks:
+    print(f"- {task.title} ({task.priority})")
+    # Process each task as it arrives
+```
+
+## Provider Configuration
+
+### Anthropic Claude
+
+```python
+import instructor
+from anthropic import Anthropic
+
+client = instructor.from_anthropic(
+    Anthropic(api_key="your-api-key")
+)
+
+# Use with Claude models
+response = client.messages.create(
+    model="claude-sonnet-4-5-20250929",
+    max_tokens=1024,
+    messages=[...],
+    response_model=YourModel
+)
+```
+
+### OpenAI
+
+```python
+from openai import OpenAI
+
+client = instructor.from_openai(
+    OpenAI(api_key="your-api-key")
+)
+
+response = client.chat.completions.create(
+    model="gpt-4o-mini",
+    response_model=YourModel,
+    messages=[...]
+)
+```
+
+### Local Models (Ollama)
+
+```python
+from openai import OpenAI
+
+# Point to local Ollama server
+client = instructor.from_openai(
+    OpenAI(
+        base_url="http://localhost:11434/v1",
+        api_key="ollama"  # Required but ignored
+    ),
+    mode=instructor.Mode.JSON
+)
+
+response = client.chat.completions.create(
+    model="llama3.1",
+    response_model=YourModel,
+    messages=[...]
+)
+```
+
+## Common Patterns
+
+### Pattern 1: Data Extraction from Text
+
+```python
+class CompanyInfo(BaseModel):
+    name: str
+    founded_year: int
+    industry: str
+    employees: int
+    headquarters: str
+
+text = """
+Tesla, Inc. was founded in 2003. It operates in the automotive and energy
+industry with approximately 140,000 employees. The company is headquartered
+in Austin, Texas.
+"""
+
+company = client.messages.create(
+    model="claude-sonnet-4-5-20250929",
+    max_tokens=1024,
+    messages=[{
+        "role": "user",
+        "content": f"Extract company information from: {text}"
+    }],
+    response_model=CompanyInfo
+)
+```
+
+### Pattern 2: Classification
+
+```python
+class Category(str, Enum):
+    TECHNOLOGY = "technology"
+    FINANCE = "finance"
+    HEALTHCARE = "healthcare"
+    EDUCATION = "education"
+    OTHER = "other"
+
+class ArticleClassification(BaseModel):
+    category: Category
+    confidence: float = Field(ge=0.0, le=1.0)
+    keywords: list[str]
+
+classification = client.messages.create(
+    model="claude-sonnet-4-5-20250929",
+    max_tokens=1024,
+    messages=[{
+        "role": "user",
+        "content": "Classify this article: [article text]"
+    }],
+    response_model=ArticleClassification
+)
+```
+
+### Pattern 3: Multi-Entity Extraction
+
+```python
+class Person(BaseModel):
+    name: str
+    role: str
+
+class Organization(BaseModel):
+    name: str
+    industry: str
+
+class Entities(BaseModel):
+    people: list[Person]
+    organizations: list[Organization]
+    locations: list[str]
+
+text = "Tim Cook, CEO of Apple, announced at the event in Cupertino..."
+
+entities = client.messages.create(
+    model="claude-sonnet-4-5-20250929",
+    max_tokens=1024,
+    messages=[{
+        "role": "user",
+        "content": f"Extract all entities from: {text}"
+    }],
+    response_model=Entities
+)
+
+for person in entities.people:
+    print(f"{person.name} - {person.role}")
+```
+
+### Pattern 4: Structured Analysis
+
+```python
+class SentimentAnalysis(BaseModel):
+    overall_sentiment: Sentiment
+    positive_aspects: list[str]
+    negative_aspects: list[str]
+    suggestions: list[str]
+    score: float = Field(ge=-1.0, le=1.0)
+
+review = "The product works well but setup was confusing..."
+
+analysis = client.messages.create(
+    model="claude-sonnet-4-5-20250929",
+    max_tokens=1024,
+    messages=[{
+        "role": "user",
+        "content": f"Analyze this review: {review}"
+    }],
+    response_model=SentimentAnalysis
+)
+```
+
+### Pattern 5: Batch Processing
+
+```python
+def extract_person(text: str) -> Person:
+    return client.messages.create(
+        model="claude-sonnet-4-5-20250929",
+        max_tokens=1024,
+        messages=[{
+            "role": "user",
+            "content": f"Extract person from: {text}"
+        }],
+        response_model=Person
+    )
+
+texts = [
+    "John Doe is a 30-year-old engineer",
+    "Jane Smith, 25, works in marketing",
+    "Bob Johnson, age 40, software developer"
+]
+
+people = [extract_person(text) for text in texts]
+```
+
+## Advanced Features
+
+### Union Types
+
+```python
+from typing import Union
+
+class TextContent(BaseModel):
+    type: str = "text"
+    content: str
+
+class ImageContent(BaseModel):
+    type: str = "image"
+    url: HttpUrl
+    caption: str
+
+class Post(BaseModel):
+    title: str
+    content: Union[TextContent, ImageContent]  # Either type
+
+# LLM chooses appropriate type based on content
+```
+
+### Dynamic Models
+
+```python
+from pydantic import create_model
+
+# Create model at runtime
+DynamicUser = create_model(
+    'User',
+    name=(str, ...),
+    age=(int, Field(ge=0)),
+    email=(EmailStr, ...)
+)
+
+user = client.messages.create(
+    model="claude-sonnet-4-5-20250929",
+    max_tokens=1024,
+    messages=[...],
+    response_model=DynamicUser
+)
+```
+
+### Custom Modes
+
+```python
+# For providers without native structured outputs
+client = instructor.from_anthropic(
+    Anthropic(),
+    mode=instructor.Mode.JSON  # JSON mode
+)
+
+# Available modes:
+# - Mode.ANTHROPIC_TOOLS (recommended for Claude)
+# - Mode.JSON (fallback)
+# - Mode.TOOLS (OpenAI tools)
+```
+
+### Context Management
+
+```python
+# Single-use client
+with instructor.from_anthropic(Anthropic()) as client:
+    result = client.messages.create(
+        model="claude-sonnet-4-5-20250929",
+        max_tokens=1024,
+        messages=[...],
+        response_model=YourModel
+    )
+    # Client closed automatically
+```
+
+## Error Handling
+
+### Handling Validation Errors
+
+```python
+from pydantic import ValidationError
+
+try:
+    user = client.messages.create(
+        model="claude-sonnet-4-5-20250929",
+        max_tokens=1024,
+        messages=[...],
+        response_model=User,
+        max_retries=3
+    )
+except ValidationError as e:
+    print(f"Failed after retries: {e}")
+    # Handle gracefully
+
+except Exception as e:
+    print(f"API error: {e}")
+```
+
+### Custom Error Messages
+
+```python
+class ValidatedUser(BaseModel):
+    name: str = Field(description="Full name, 2-100 characters")
+    age: int = Field(description="Age between 0 and 120", ge=0, le=120)
+    email: EmailStr = Field(description="Valid email address")
+
+    class Config:
+        # Custom error messages
+        json_schema_extra = {
+            "examples": [
+                {
+                    "name": "John Doe",
+                    "age": 30,
+                    "email": "john@example.com"
+                }
+            ]
+        }
+```
+
+## Best Practices
+
+### 1. Clear Field Descriptions
+
+```python
+# ❌ Bad: Vague
+class Product(BaseModel):
+    name: str
+    price: float
+
+# ✅ Good: Descriptive
+class Product(BaseModel):
+    name: str = Field(description="Product name from the text")
+    price: float = Field(description="Price in USD, without currency symbol")
+```
+
+### 2. Use Appropriate Validation
+
+```python
+# ✅ Good: Constrain values
+class Rating(BaseModel):
+    score: int = Field(ge=1, le=5, description="Rating from 1 to 5 stars")
+    review: str = Field(min_length=10, description="Review text, at least 10 chars")
+```
+
+### 3. Provide Examples in Prompts
+
+```python
+messages = [{
+    "role": "user",
+    "content": """Extract person info from: "John, 30, engineer"
+
+Example format:
+{
+  "name": "John Doe",
+  "age": 30,
+  "occupation": "engineer"
+}"""
+}]
+```
+
+### 4. Use Enums for Fixed Categories
+
+```python
+# ✅ Good: Enum ensures valid values
+class Status(str, Enum):
+    PENDING = "pending"
+    APPROVED = "approved"
+    REJECTED = "rejected"
+
+class Application(BaseModel):
+    status: Status  # LLM must choose from enum
+```
+
+### 5. Handle Missing Data Gracefully
+
+```python
+class PartialData(BaseModel):
+    required_field: str
+    optional_field: Optional[str] = None
+    default_field: str = "default_value"
+
+# LLM only needs to provide required_field
+```
+
+## Comparison to Alternatives
+
+| Feature | Instructor | Manual JSON | LangChain | DSPy |
+|---------|------------|-------------|-----------|------|
+| Type Safety | ✅ Yes | ❌ No | ⚠️ Partial | ✅ Yes |
+| Auto Validation | ✅ Yes | ❌ No | ❌ No | ⚠️ Limited |
+| Auto Retry | ✅ Yes | ❌ No | ❌ No | ✅ Yes |
+| Streaming | ✅ Yes | ❌ No | ✅ Yes | ❌ No |
+| Multi-Provider | ✅ Yes | ⚠️ Manual | ✅ Yes | ✅ Yes |
+| Learning Curve | Low | Low | Medium | High |
+
+**When to choose Instructor:**
+- Need structured, validated outputs
+- Want type safety and IDE support
+- Require automatic retries
+- Building data extraction systems
+
+**When to choose alternatives:**
+- DSPy: Need prompt optimization
+- LangChain: Building complex chains
+- Manual: Simple, one-off extractions
+
+## Resources
+
+- **Documentation**: https://python.useinstructor.com
+- **GitHub**: https://github.com/jxnl/instructor (15k+ stars)
+- **Cookbook**: https://python.useinstructor.com/examples
+- **Discord**: Community support available
+
+## See Also
+
+- `references/validation.md` - Advanced validation patterns
+- `references/providers.md` - Provider-specific configuration
+- `references/examples.md` - Real-world use cases
+
+