Extract structured data from LLM responses using Pydantic validation and automatic retries.
npx skills add https://github.com/zangjeicy/Hermes --skill instructor-zangjeicy
Please help me install this Agent Skill. Skill: instructor Source: https://github.com/zangjeicy/Hermes/tree/main/optional-skills/mlops/instructor Command: npx skills add https://github.com/zangjeicy/Hermes --skill instructor-zangjeicy
This skill solves the challenge of unreliable LLM outputs by enforcing strict schema validation, ensuring that the data returned by an AI is always in the exact format your application requires.
Use the instructor skill to extract user information from the provided text into a Pydantic model.
High-intent search queries and answers about installing and using this skill.
To extract structured JSON from LLM responses with Pydantic validation, use this skill to automatically validate model outputs against your Python data models and retry requests with error feedback if validation fails.
LLM outputs often contain invalid JSON or schema drift, but enforcing strict Pydantic validation catches formatting errors and automatically retries the request with error feedback to guarantee the exact required structure.
Yes, this skill supports structured data extraction across multiple LLM providers including OpenAI and Anthropic, allowing you to maintain full type safety regardless of the underlying language model.
The best way to perform entity recognition and classification from unstructured text is applying Pydantic schema enforcement to LLM outputs, ensuring extracted entities conform precisely to your defined data models.
Yes, this Pydantic validation approach supports real-time streaming of partial results, enabling you to process complex JSON parsing and structured data extraction incrementally as the LLM generates responses.
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