What problem does it solve? Getting reliable, structured data from LLM responses is difficult because models return free-form text that breaks downstream parsing. This Skill solves that by enforcing Pydantic schemas on LLM outputs, automatically validating results, and retrying failed extractions with error feedback. ## Core Features & Use Cases - Schema-Validated Extraction: Define Pydantic models with types, constraints, enums, and nested structures, and receive validated Python objects instead of raw JSON strings. - Automatic Retry on Validation Failure: When output fails validation, the error is fed back to the LLM and the request retries up to a configurable limit. - Streaming Partial Results: Stream partial objects or iterables as the LLM generates them for real-time UI updates. - Multi-Provider Support: Works with Anthropic Claude, OpenAI, and local models via Ollama using a consistent API. - Use Case: Extract company information (name, founding year, industry, employee count) from unstructured text paragraphs into a typed CompanyInfo model, with automatic retries if any field fails validation. ## Quick Start Ask the agent to extract structured fields from a block of text into a Pydantic model using Instructor with your preferred LLM provider.
