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gagan114662
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instructor

Extract structured data from LLM responses using Pydantic models.

Updated Jan 30, 2026
Search Tags:#type safety#pydantic#llm#data validation#api integration#structured output
One-click install
npx skills add https://github.com/gagan114662/content_books --skill instructor-gagan114662
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: instructor
Source: https://github.com/gagan114662/content_books/tree/main/AI-research-SKILLs/16-prompt-engineering/instructor
Command: npx skills add https://github.com/gagan114662/content_books --skill instructor-gagan114662

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill solves the challenge of reliably extracting structured, validated data from Large Language Model (LLM) responses, ensuring type safety and automatic error handling.

Core Features & Use Cases

  • Structured Data Extraction: Define Pydantic models to specify the exact output format.
  • Automatic Validation: Pydantic schemas enforce data integrity and type correctness.
  • Retry Mechanism: Automatically retries LLM calls when validation fails, providing feedback to the model.
  • Streaming: Supports streaming partial results for real-time updates.
  • Use Case: Extracting detailed user profiles, product specifications, or financial reports from unstructured LLM text into a well-defined Python object.

Quick Start

Use the instructor skill to extract user details into a Pydantic model named User.

Frequently Asked Questions about instructor

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

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

Structured data extraction from LLM responses uses Pydantic models to specify the exact output format, enforcing data integrity and type correctness. The LLM output is parsed directly into well-defined Python objects.

What is the best way to handle LLM output validation failures automatically?▼

Automatic validation failures are handled through a retry mechanism that automatically retries LLM calls when validation fails. This process provides validation feedback directly to the model to correct the output.

Can I extract structured data from OpenAI and Anthropic models?▼

Yes, structured data extraction integrates with providers like OpenAI and Anthropic. This integration enables robust data parsing and error handling directly from their LLM responses into Pydantic models.

How do I stream partial LLM results in real time?▼

Streaming partial LLM results is supported for real-time updates. This allows you to process and display structured data incrementally as the LLM generates the response, parsed via Pydantic schemas.

When do I need type safety for LLM data extraction?▼

Type safety for LLM data extraction is needed when converting unstructured LLM text into detailed user profiles, product specifications, or financial reports, ensuring the extracted Python objects maintain strict data integrity.

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