qmd

Index local files and search them with BM25, vector, and hybrid queries.

386k|81.1k|Updated Nov 24, 2025
One-click install
npx skills add https://github.com/steipete/clawdis --skill qmd
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: qmd
Source: https://github.com/steipete/clawdis/tree/main/skills/qmd
Command: npx skills add https://github.com/steipete/clawdis --skill qmd

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you quickly index and search local documents, enabling fast retrieval with hybrid BM25 lexical matching and vector-based ranking, with optional reranking.

Core Features & Use Cases

  • Hybrid search: Combines BM25 lexical matching with vector embeddings for relevant results.
  • Local indexing: Builds and maintains an index of your files for fast queries.
  • Embeddings via Ollama: Uses Ollama for embeddings when configured via OLLAMA_URL.
  • Use Case: Quickly locate a specific note or document across thousands of files without manual browsing.

Quick Start

Use the qmd CLI to index local files: qmd collection add /path --name docs --mask "**/*.md" qmd update qmd search "deployment plan"

Frequently Asked Questions about qmd

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

FAQPage Schema
How do I index and search local files quickly without manual browsing?▼

File indexing enables fast retrieval by building a searchable index of your documents. qmd indexes local files using BM25 lexical matching and vector embeddings, then returns ranked results instantly across thousands of files with a single query command.

What's the difference between BM25 and vector search, and when should I use each?▼

BM25 performs keyword-based lexical matching for exact phrase matches; vector search finds semantically similar content. qmd combines both in hybrid search to catch exact matches and conceptually related documents, improving recall and relevance simultaneously.

Can I use qmd to index Markdown and code repositories?▼

Yes. qmd supports local repositories of Markdown, code, and text files. You configure file masks (e.g., **/*.md) to target specific file types, then index and query across your entire repository structure stored under ~/.cache/qmd.

Do I need Ollama to run qmd for search and indexing?▼

Ollama is required for embeddings and reranking. If you configure OLLAMA_URL, qmd uses Ollama to generate vector embeddings; without it, you can still perform BM25 lexical search but lose vector and reranking capabilities.

How do I get started indexing a directory and running my first search?▼

Use three commands: qmd collection add /path --name docs --mask "**/*.md" to add files, qmd update to build the index, then qmd search "query" to retrieve results. The index stores locally under ~/.cache/qmd by default.

Can I use qmd in MCP mode, and what does that enable?▼

Yes. qmd supports MCP mode via the mcp command, enabling integration with Model Context Protocol workflows for embedding qmd search capabilities into larger automation and AI tool pipelines.