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Design effective tools for agents, focusing on architectural reduction patterns.

Extract structured data from LLM responses with automatic validation and error handling.

A comprehensive collection of skills for building AI agents using LangChain and LangGraph.

Best practices and conventions for developing with FastAPI and Pydantic models.

Build AI agents and stateful LLM applications using LangChain and LangGraph.

Build Retrieval-Augmented Generation (RAG) systems for LLM applications using vector databases and semantic search.

Build high-quality MCP servers for LLMs to interact with external services.

Create, modify, and optimize skills while measuring their performance.

Master prompt engineering techniques for AI agents and LLM applications with a focus on Claude-specific methods.

Optimize LLM token costs and latency for AI agents and applications.