Extract structured data from LLM responses with Pydantic validation.
npx skills add https://github.com/hhhi21g/HealthCenter --skill instructor-hhhi21g
Please help me install this Agent Skill. Skill: instructor Source: https://github.com/hhhi21g/HealthCenter/tree/main/.codex/skills/instructor Command: npx skills add https://github.com/hhhi21g/HealthCenter --skill instructor-hhhi21g
This Skill ensures accurate and reliable data extraction from LLM responses, automates validation, and offers robust error handling through automatic retries.
Use the instructor skill to extract user data from the AI-generated response: "John Doe is 30 years old. His email is john@example.com".
High-intent search queries and answers about installing and using this skill.
To extract structured data from LLM responses with Pydantic validation, define a schema and let the tool automatically validate the output. It retries failed extractions automatically, ensuring reliable type safety and accurate JSON parsing.
Parsing complex JSON with type safety is best handled by validating LLM outputs against Pydantic schemas. This approach automatically enforces data types and structures, retrying failed extractions to ensure robust JSON parsing.
Yes, you can use it with multiple LLM providers like OpenAI and Anthropic. It offers a consistent API for structured data extraction across different platforms, ensuring Pydantic validation and automatic retries work uniformly.
Streaming partial results works by emitting structured data segments as the LLM generates them. This enables real-time processing and immediate validation of partial JSON outputs against Pydantic schemas without waiting for the full response.
LLM structured data extraction fails when responses do not match Pydantic schemas or contain invalid JSON. Automatic retries work by feeding validation errors back to the LLM, prompting it to correct the output and retry the extraction.
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