Extracts and validates structured data from LLM outputs using Pydantic schemas.
npx skills add https://github.com/Harries/hermes-agent --skill instructor-harries
Please help me install this Agent Skill. Skill: instructor Source: https://github.com/Harries/hermes-agent/tree/main/optional-skills/mlops/instructor Command: npx skills add https://github.com/Harries/hermes-agent --skill instructor-harries
This Skill enables reliable extraction of structured data from LLM responses by validating outputs against Pydantic models and retrying failed extractions automatically, reducing manual post-processing and errors.
Provide a sample LLM response and a Pydantic model, then run the Instructor client to extract and validate a structured object from the response.
High-intent search queries and answers about installing and using this skill.
You can extract structured data from LLM outputs by validating responses against Pydantic schemas. This approach maps raw text into typed objects, automatically retrying failed extractions to ensure valid, structured results without manual post-processing.
Pydantic validation checks LLM responses against predefined schemas, mapping text to structured objects. If validation fails, the system automatically retries the extraction, correcting formatting or typing errors dynamically until the output matches the schema.
Yes, structured output extraction supports multiple providers including OpenAI and Anthropic. You can configure the extraction client across these platforms to validate LLM responses against Pydantic models for consistent data extraction.
The best way to handle complex data extraction is using nested Pydantic schemas, enums, and model-level validation. This allows you to define multi-layered data structures and enforce strict validation rules directly on the LLM output.
Yes, structured LLM output extraction supports streaming partial results for real-time processing. This allows you to process and consume validated data chunks incrementally as the LLM generates the response, rather than waiting for completion.
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