A first-principles guide to Agent Skills: how they work, how to build one with skill-creator, and how they differ from MCP servers.
Agentic AI Systems Engineer
Production, not demos
Security from day one
Documents what breaks
Agentic AI Systems Engineer
Production, not demos
Security from day one
Documents what breaks
A first-principles guide to Agent Skills: how they work, how to build one with skill-creator, and how they differ from MCP servers.
What goes wrong when MCP meets real users — context overload, smelly tool descriptions, prompt-injection-as-RCE — and the infrastructure work that prevents it.
Large language models are brilliant but isolated. The Model Context Protocol (MCP) fixes this by standardizing AI integrations. Just as LSP did for IDEs, MCP lets you write a server once using three primitives, Tools, Resources, and Prompts, so any compliant AI assistant can seamlessly use your
Basic RAG works for demos, but production demands more. Explore 11 advanced strategies, from re-ranking and multi-query to agentic and self-reflective RAG, that solve specific failure modes. Learn how to stack these techniques to build accurate, production-ready AI systems.