instructor
Category: llm-applications · License: MIT · Version: 1.0.0
Tags: Prompt Engineering, Instructor, Structured Output, Pydantic, Data Extraction, JSON Parsing, Type Safety, Validation, Streaming, OpenAI, Anthropic
When to use it
Extracts structured, validated data from LLM responses using the Instructor Python library with Pydantic response models, including nested models, enums, custom validators, automatic retries with validation error feedback, and streaming of partial objects or iterables. Works with Anthropic, OpenAI, and local Ollama models. Use when pulling typed fields or entities out of free text, when classifying text into fixed categories, when an LLM must return JSON that passes schema validation, when failed extractions need automatic retry, or when streaming partial structured results. Not for prompt optimization (use DSPy) or building multi-step chains (use LangChain).Example requests
Ask your agent in plain language, for example:- “I have 300 free-text clinical trial abstracts. How do I get a typed Python object out of each one with sample size, intervention, and primary endpoint?”
- “pydantic ValidationError: 1 validation error for PaperMeta, year: Input should be less than or equal to 2026 [input_value=2206]. My extraction from Claude keeps failing on this. Can I make it fix itself instead of crashing?”
- “I’m labeling 5,000 patient forum posts as one of: symptom report, medication question, side effect, or other. The model’s replies are sloppy and I need every answer to be exactly one of those labels as a Python value, running locally through Ollama. Best approach?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole llm-applications category with /plugin install llm-applications@research-agent-skills.