Anthropic's official collection of Agent Skills: folders with instructions and scripts that Claude loads for a task. A good reference for writing your own.
Prompting & context567-labs
instructor
The most popular way to get validated Pydantic objects out of an LLM: define a data model and the library retries the request when validation fails.
Who it is for
For Python developers who need reliable structured answers from any provider.
How to start
- Install:
pip install instructor(oruv add instructor). - Define a Pydantic model for what you want.
- Wrap your provider client with instructor and get a ready object from text.
Steps are taken from the README. Check the current version in the repository before running them.
Stars over the last 30 days
Author's description
structured outputs for llms
More in «Prompting & context»
Formerly Awesome ChatGPT Prompts: a huge community library of prompts plus an interactive book on prompting. You can deploy your own private copy.
A collection of system prompts and tool descriptions from popular AI products: Cursor, Devin, Windsurf, Lovable, Manus and others. Useful for studying how pros build them.
A detailed guide to prompt engineering: techniques from few-shot to chains of thought, papers, lectures and notebooks. Recently extended with context engineering and agents.
A collection of examples and guides for the OpenAI API: from basic requests to function calling and RAG. Most code is Python.
Recipes from Anthropic as notebooks: tools, RAG, multimodality, subagents and other techniques for working with Claude. The code is ready to copy into your projects.
