Extract structured data from LLM responses and validate with Pydantic schemas.
npx skills add https://github.com/OpenLAIR/dr-claw --skill instructor-openlair
Please help me install this Agent Skill. Skill: instructor Source: https://github.com/OpenLAIR/dr-claw/tree/main/skills/prompt-engineering/instructor Command: npx skills add https://github.com/OpenLAIR/dr-claw --skill instructor-openlair
Validating and structuring outputs from large language models is error-prone and manual, leading to inconsistent data formats and fragile workflows.
Provide a minimal, typed example that extracts a user profile and validates it with a Pydantic model, showing automatic retries on validation errors.
High-intent search queries and answers about installing and using this skill.
Validate structured LLM outputs by defining strict Pydantic schemas to enforce data structure and types. The system automatically retries misextractions with actionable feedback, ensuring higher accuracy and consistent data formats for downstream Python workflows.
Yes, you can stream partial structured LLM outputs. This feature operates across providers like OpenAI and Anthropic, enabling real-time processing and type-safe data extraction while the model is still generating its response.
Yes, structured LLM output extraction supports multiple providers including OpenAI and Anthropic. It provides a consistent API for type-safe data extraction, allowing you to normalize LLM outputs across different models within your extraction pipelines.
The best way to extract structured data for a classification pipeline is using Pydantic schemas to enforce types and structure. This method normalizes LLM outputs and automatically retries validation failures, ensuring accurate and consistent data extraction.
Structured data extraction fails validation when LLM responses do not match the defined Pydantic schema types. It is handled by automatically retrying misextractions with actionable feedback, ensuring the LLM corrects its output to achieve higher accuracy.
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JamesFincherConvert LLM responses into Pydantic-validated JSON with automatic retries.
handsomelong922Extract and validate LLM responses into Pydantic schemas with automatic retries.
NousResearchExtract structured data from LLM responses using Pydantic models.
