claudegoodies
Skill

instructor

From NousResearch

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

Facts

Repository
NousResearch/hermes-agent
Status
Actively maintained
Last commit
Source file
optional-skills/mlops/instructor/SKILL.md

Source preview

The instructions Claude Code reads when this skill runs.

# 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 [email protected]"
    }],
    response_model=User
)

print(user.name)   # "John Doe"
print(user.ag
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