ferzat0918

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

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

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

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

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

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

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

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

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

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