Extract structured data from LLM responses with Pydantic validation and retries.
npx skills add https://github.com/gigagiova/hermes-agent --skill instructor-gigagiova
Please help me install this Agent Skill. Skill: instructor Source: https://github.com/gigagiova/hermes-agent/tree/main/skills/mlops/instructor Command: npx skills add https://github.com/gigagiova/hermes-agent --skill instructor-gigagiova
The Instructor skill enables reliable extraction of structured data from LLM outputs by validating results with Pydantic schemas and automatically retrying on validation errors.
Install the Instructor library, define a Pydantic model, and validate a structured output from an LLM.
High-intent search queries and answers about installing and using this skill.
You can extract structured data by validating LLM responses against Pydantic schemas and automatically retrying on validation failures, ensuring type-safe outputs for downstream data pipelines.
Yes, you can stream partial structured objects in real-time. This progressive result delivery allows you to iterate over incomplete Pydantic models as the LLM generates the response.
Structured output validation works across multiple providers including OpenAI and Anthropic. You can enforce type-safe Pydantic schemas and extract consistent data regardless of the underlying LLM.
To build a Python data extraction pipeline, you need the Instructor library and Pydantic. Define your Pydantic models to enforce schemas, and integrate them into Python prompts for end-to-end structured data workflows.
LLM response validation fails when the output does not match your defined Pydantic schema. The Instructor skill handles this by automatically retrying the LLM prompt on validation errors until structured output passes.
The best way to enforce type-safe results is by applying Pydantic validation to LLM outputs. This guarantees extracted data conforms to defined schemas and automatically retries generation on validation failures.
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