qmd

Index Markdown notes locally and search via keyword, semantic, or hybrid modes.

Updated Jan 13, 2026

Search Tags:#documents #markdown #notes #semantic-search #local-search #qmd

Ready to run

npx skills add https://github.com/MrGKanev/agent-skills --skill qmd-mrgkanev

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: qmd
Source: https://github.com/MrGKanev/agent-skills/tree/main/skills/qmd
Command: npx skills add https://github.com/MrGKanev/agent-skills --skill qmd-mrgkanev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables fast local search across Markdown notes, docs, and knowledge bases by indexing content on disk and enabling rapid retrieval.

Core Features & Use Cases

Local indexing of Markdown files to enable fast search across personal notes and documentation.
Support for keyword, semantic, and hybrid search modes to match user intent.
Retrieve full documents or specific fragments from indexed collections for quick reference.

Quick Start

Ensure Bun is installed and qmd is accessible on your system. Install and set up a local collection, then perform searches with qmd search, qmd vsearch, or qmd get as needed. Example: qmd search "project plan" -c notes

Frequently Asked Questions about qmd

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I search local Markdown notes and knowledge bases quickly?▼

You can search local Markdown notes by indexing content on disk and running queries against the collection. This enables rapid retrieval of full documents or specific fragments from your personal knowledge base.

Can I use semantic search for my local Markdown documentation?▼

Yes, semantic search is supported for local Markdown documentation through an optional embedding setup. It matches user intent rather than just exact keywords, allowing for more flexible document retrieval.

What do I need to run a local search across Markdown files?▼

You need the Bun runtime installed and a local qmd installation accessible on your system. Optional embedding setup is required if you want to use semantic search modes across your Markdown files.

What is the best way to retrieve specific fragments from indexed Markdown notes?▼

The best way to retrieve specific fragments is using hybrid search modes. By indexing your Markdown notes on disk, you can query and extract exact document fragments for quick reference.

Does this local search approach work with large project documentation collections?▼

Yes, this approach works with large project documentation collections by indexing Markdown files on disk. It supports keyword, semantic, and hybrid search modes to match user intent across extensive knowledge bases.

Why use local Markdown search instead of standard file system search?▼

Local Markdown search indexes content on disk to enable rapid retrieval and intent matching. Unlike standard file system search, it supports semantic and hybrid modes to retrieve full documents or specific fragments.