qmd Search

qmd Search extends your AI assistant with the ability to fast local search for markdown files, notes, and docs using qmd CLI. Use instead of `find` for file discovery. Combines BM25 full-text search, vector semantic search, and LLM reranking—all running locally. Use when searching for files, finding code, locating documentation, or discovering content in indexed collections. 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 qmd Search as its underlying capability.

qmd Search 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 qmd Search 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.

Getting started with qmd Search takes about two minutes. Place the skill at `~/.claude/skills/qmd-search/` (personal, all projects) or `.claude/skills/qmd-search/` (project-specific), then restart your AI client. From that point, typing `/qmd-search` in any conversation activates it, or the AI will use it on its own when it detects a relevant request.

qmd Search has been installed 3,301 times, making it one of the more actively used skills in the Documents & Notes category. The install rate suggests it solves a real, recurring need rather than a niche edge case. The source code is open on GitHub — you can inspect it, contribute fixes, or fork it to adapt the skill for your specific setup. 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.