Instructor

If you're tired of writing brittle JSON parsing code and handling LLM output errors manually, Instructor wraps Anthropic and OpenAI APIs to enforce Pydantic schemas on responses. Define your data structure once, and it automatically validates outputs, retries with error feedback when validation fails, and gives you proper Python objects instead of raw JSON strings. The streaming support is solid for real-time UIs, letting you show partial results as they generate. Over 15,000 GitHub stars and used by 100,000+ developers, so the edge cases are probably handled. It's the difference between hoping your LLM returns valid JSON and knowing it will.

skills.sh installs: 1K
GitHub stars: 14.4K
last updated: Aug 21, 2026

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Install

$ npx -y skills add orchestra-research/ai-research-skills --skill instructor --agent claude-code

Installs into .claude/skills of the current project.

Files

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

# 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

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())

SKILL.md on GitHub