Extract validated structured data from LLM responses using Pydantic schemas.
npx skills add https://github.com/KarlinskyS/hermesSkills --skill instructor-karlinskys
Please help me install this Agent Skill. Skill: instructor Source: https://github.com/KarlinskyS/hermesSkills/tree/main/mlops/inference/instructor Command: npx skills add https://github.com/KarlinskyS/hermesSkills --skill instructor-karlinskys
Many applications need reliable, typed data from LLM responses, but raw outputs are often unstructured, ambiguous, or invalid, leading to downstream errors and extra parsing work.
Ask Instructor to extract a user's name, age, and email from a sentence using a Pydantic model.
High-intent search queries and answers about installing and using this skill.
You can extract structured data from LLMs by defining Pydantic schemas to validate outputs automatically. This Skill applies the schema to the response, ensuring type safety for data extraction and classification tasks while prompting the model to correct any validation failures.
LLM output validation fails when responses do not match your defined Pydantic schema or type constraints. This Skill fixes it using automatic retries, prompting the model to correct the output upon validation failure to ensure accurate structured data extraction.
Yes, this structured data extraction tool works with local Ollama models, as well as Anthropic and OpenAI. It applies Pydantic validation and automatic retries across all supported providers to ensure type safety.
Yes, you can stream partial structured objects or iterables as the model generates content. This allows you to receive real-time results during data extraction instead of waiting for the entire LLM response to finish.
The best way to classify documents using LLMs is to enforce Pydantic schema validation on the output. This approach guarantees the model returns type-safe, structured categories, automatically retrying if the initial classification fails validation.
fjacquetValidate FinWiz outputs against Pydantic v2 strict schemas.
archibateModel structured data into validated Python classes with dataclasses, attrs, or Pydantic.
Montte-erpStream AI model outputs with tool calling and Zod-validated structured data.
ovachieverExtract structured data from LLM responses with Pydantic validation and retries.
