instructor
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
npx skills add Orchestra-Research/AI-Research-SKILLs --skill instructor --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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": "J
- When to Use This Skill
- Installation
- Quick Start
- Basic Example: Extract User Data
- With OpenAI
- Core Concepts
- 1. Response Models (Pydantic)
- 2. Validation
- 3. Automatic Retrying
- 4. Streaming
- Provider Configuration
- Anthropic Claude
- OpenAI
- Local Models (Ollama)
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
What does the instructor skill do?
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
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill instructor --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.