Extract structured data from LLM responses with Pydantic validation and retries.
npx skills add https://github.com/ovachiever/droid-tings --skill instructor
Please help me install this Agent Skill. Skill: instructor Source: https://github.com/ovachiever/droid-tings/tree/main/skills/instructor Command: npx skills add https://github.com/ovachiever/droid-tings --skill instructor
Extract structured data from LLM outputs with automatic validation, retry on errors, and streaming of partial results for real-time processing.
Create a Pydantic model and validate an LLM response using Instructor with a Claude or OpenAI backend.
High-intent search queries and answers about installing and using this skill.
Structured data extraction from LLM responses uses Pydantic models to define expected output schemas with automatic validation. Instructor applies these schemas across OpenAI, Anthropic, and other providers, ensuring type-safe JSON parsing and catching validation errors before they reach your application.
Automatic retries on extraction failures are built into Instructor's validation layer. When Pydantic validation fails, Instructor sends error feedback back to the LLM, which regenerates the response until it conforms to your schema or a retry limit is reached.
Yes, Instructor supports streaming partial results for real-time processing, enabling you to pipe validated data chunks to UIs or downstream systems as they arrive rather than waiting for complete LLM responses.
Instructor provides a unified API across OpenAI, Anthropic, and Google that handles nested-JSON parsing with type safety through Pydantic models. This eliminates provider-specific parsing logic and ensures consistent, validated extraction regardless of which LLM backend you use.
Pydantic-based validation is built into Instructor, so you define schemas declaratively using Pydantic models rather than writing custom validators. Instructor automatically applies these constraints and retries the LLM if responses fail validation.
Instructor is designed for production reliability through automatic retries and error feedback loops. It surfaces validation errors clearly and exhausts retries before surfacing failures, making it suitable for automated, high-throughput extraction pipelines.
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