Convert LLM responses into Pydantic-validated JSON with automatic retries.
npx skills add https://github.com/JamesFincher/gengar --skill instructor-jamesfincher
Please help me install this Agent Skill. Skill: instructor Source: https://github.com/JamesFincher/gengar/tree/main/optional-skills/mlops/instructor Command: npx skills add https://github.com/JamesFincher/gengar --skill instructor-jamesfincher
Instructor eliminates unreliable, loosely formatted LLM responses by enforcing structured outputs that are validated and automatically retried when extraction fails.
Use Instructor when you need to extract entities, classify items, or convert long-form text into consistent JSON records for pipelines (ETL, reporting, search indexing, or tool-parameter generation).
Ask your AI to extract the required fields from the text and return them as a Pydantic model, retrying automatically if any fields fail validation.
High-intent search queries and answers about installing and using this skill.
To get structured JSON output from LLM responses, you define a Pydantic response_model schema to validate the data. This process converts unstructured text into reliable typed records and automatically retries extraction when validation fails.
The best way to parse nested objects for ETL pipelines is applying schema-constrained extraction with automatic validation retries. This converts long-form text into consistent JSON records by enforcing type safety on complex or constrained values without fragile manual parsing.
Yes, structured data extraction works with both OpenAI and Anthropic APIs. The extraction process applies Pydantic schemas across common provider APIs to validate responses, ensuring that nested objects and constrained values parse correctly regardless of the specific LLM provider.
Yes, you can stream partial structured outputs from an LLM in real time. This mechanism emits incremental structured updates as they are parsed, allowing downstream systems to react immediately to validated data fragments instead of waiting for the full response.
LLM JSON parsing fails when fields don't match defined types or constraints. This is fixed using automatic retry on validation errors, which re-attempts the extraction with error feedback to ensure the model returns data matching your Pydantic schema.
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