prompt-engineering-instructor — Claude Code skill: structured LLM

What prompt-engineering-instructor does

The skill covers Instructor, a library that turns a model reply into a Python object instead of a blob of text. You define a Pydantic model — fields, types, constraints — and pass it as response_model. When the output does not match, Pydantic raises, Instructor sends the error back to the model and asks for another attempt. Three retries is the default.

Inside you get nested models, optional fields with defaults, enums for fixed value sets, built-in and custom validators, and checks that run across the whole model. Both Anthropic and OpenAI are covered: build the client with instructor.from_anthropic(Anthropic()) or instructor.from_openai(OpenAI()), then call as usual.

Worth installing if you write extraction, JSON parsing or any pipeline that reads LLM answers. It will not run without a provider key and the packages named as dependencies: instructor, pydantic, openai, anthropic. Base install is pip install instructor; provider extras look like instructor[anthropic].