RAG

RAG extends your AI assistant with the ability to build, optimize, and debug RAG pipelines with chunking strategies, retrieval tuning, evaluation metrics, and production monitoring. Rather than leaving your conversation to handle this manually, you can ask your Claude agent directly — and it will take care of the task end-to-end, using RAG as its underlying capability.

RAG works across Claude, Cursor, OpenClaw through the Model Context Protocol (MCP) — an open standard that lets AI clients share tools and skills without lock-in. Because MCP is platform-agnostic by design, you install RAG once and it becomes available across all your AI clients. Whether you're working in Claude for focused sessions or Cursor for integrated workflows, the skill behaves consistently.

To install RAG, copy the skill folder to `~/.claude/skills/rag/` for use across all your projects, or `.claude/skills/rag/` for a single project. Restart Claude and the skill is immediately active — invoke it with `/rag` or just describe your goal and the AI will pick it up automatically.

RAG has been installed 2,118 times, making it one of the more actively used skills in the Developer & DevOps category. The install rate suggests it solves a real, recurring need rather than a niche edge case. Like all skills on DiscoverAISkills, it is free to install and use. The broader AI skills ecosystem continues to expand as developers contribute new capabilities across categories like developer tools, data analysis, writing, automation, and more.