QMD Search

QMD Search extends your AI assistant with the ability to search markdown knowledge bases efficiently using qmd. Use this when searching Obsidian vaults or markdown collections to find relevant content with minimal token usage. 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.

QMD Search installs like any other MCP skill: drop the folder into `~/.claude/skills/anshumanbh-qmd/` for global access, or `.claude/skills/anshumanbh-qmd/` to keep it scoped to one project. After a quick restart of Claude, you can trigger it explicitly with `/anshumanbh-qmd`, or let the AI decide when it's the right tool for your request.

QMD Search has been installed 2,155 times, making it one of the more actively used skills in the Design & Creative 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.