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instructor

Extracts and validates structured LLM response data with Pydantic, including automatic retries and JSON parsing.

1|1|Updated May 25, 2026
Search Tags:#type safety#validation#LLM#Pydantic#JSON parsing#structured data extraction
One-click install
npx skills add https://github.com/aayushsoam/clawbot-agent --skill instructor-aayushsoam
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: instructor
Source: https://github.com/aayushsoam/clawbot-agent/tree/main/optional-skills/mlops/instructor
Command: npx skills add https://github.com/aayushsoam/clawbot-agent --skill instructor-aayushsoam

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires instructor, pydantic, openai, anthropic, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill solves the challenge of extracting structured data from LLM responses, validating it, and handling partial results, enabling efficient data processing and real-time analysis.

Core Features & Use Cases

  • Structured Data Extraction: Extract structured data from LLM responses with Pydantic validation.
  • Automatic Validation: Validate outputs against Pydantic schemas automatically.
  • Retry Mechanism: Retry failed extractions automatically with error handling.
  • JSON Parsing: Parse complex JSON with type safety and validation.
  • Streaming Results: Stream partial results for real-time processing.
  • Multi-Provider Support: Support for multiple LLM providers with consistent API.
  • Use Case: Use this Skill to extract user information from LLM responses, ensuring accurate and structured data output.

Quick Start

To extract structured data from an LLM response, use the instructor skill with the appropriate response model.

Frequently Asked Questions about instructor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract structured data from LLM responses using Pydantic?▼

To extract structured data from LLM responses using Pydantic, use this Skill to validate outputs against your schemas, automatically retry failed extractions, and parse complex JSON into structured objects.

What is the best way to validate LLM output against a Pydantic schema?▼

Validating LLM output against a Pydantic schema is handled by automatically parsing the response, checking types, and retrying failed extractions to ensure the final data strictly conforms to your defined model.

Does this approach support structured data extraction with multiple LLM providers?▼

Structured data extraction supports multiple LLM providers by utilizing a consistent API that requires the openai and anthropic libraries, ensuring type safety and validation across different platforms.

Can I stream partial structured data results for real-time processing?▼

Streaming partial structured data results for real-time processing is supported. The Skill streams incomplete outputs as they are generated, enabling efficient data processing and immediate analysis.

Why do I need pydantic, openai, and anthropic libraries for JSON parsing?▼

Pydantic, openai, and anthropic libraries are required for JSON parsing because Pydantic handles type safety and validation, while the provider libraries connect to the LLMs for data extraction.

What happens when LLM structured data extraction fails validation?▼

When LLM structured data extraction fails validation, the retry mechanism automatically catches the error and re-prompts the LLM, ensuring the output eventually conforms to the Pydantic schema.

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