qmd

Search local knowledge bases with BM25, vector search, and LLM reranking.

1|1|Updated May 25, 2026
One-click install
npx skills add https://github.com/aayushsoam/clawbot-agent --skill qmd-aayushsoam
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: qmd
Source: https://github.com/aayushsoam/clawbot-agent/tree/main/optional-skills/research/qmd
Command: npx skills add https://github.com/aayushsoam/clawbot-agent --skill qmd-aayushsoam

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires node, sqlite, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill solves the problem of efficiently searching through personal knowledge bases, notes, documents, and meeting transcripts stored locally. It provides a powerful search engine that combines keyword matching, semantic understanding, and LLM-powered reranking.

Core Features & Use Cases

  • Hybrid Search: Combines keyword matching, semantic understanding, and LLM reranking for precise results.
  • Local Search: Runs entirely on the user's device without cloud dependencies.
  • Integration: Supports CLI and MCP integration for seamless use with other tools.

Quick Start

To start using qmd, first add your collections with qmd collection add path/to/collection --name collection_name. Then, you can search your knowledge base with qmd search "query".

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 on my device?

You can search local markdown notes on your device using a hybrid search engine that combines BM25 keyword matching, vector search, and LLM reranking. It operates entirely locally to find precise results from your personal knowledge base.

Do I need Node.js and SQLite to run a local knowledge base search?

Yes, running this local knowledge base search requires Node.js and SQLite. It is currently supported exclusively on macOS, relying on these dependencies to process markdown notes and meeting transcripts on your device.

What is the best way to combine keyword matching and semantic search for personal documentation?

The best way to combine keyword and semantic search for documentation is using a hybrid search approach. It integrates BM25 for keyword matching, vector search for semantic understanding, and LLM reranking to deliver highly precise results.

Can I integrate local search into my existing CLI or MCP workflows?

Yes, you can integrate local search into your workflows using its built-in CLI and MCP support. After adding collections via the CLI, you can query your markdown notes and meeting transcripts directly from other MCP-compatible tools.

How do I add my markdown files to a local search engine?

You add markdown files to the local search engine by running the command `qmd collection add path/to/collection --name collection_name`. Once indexed, you can search your notes using `qmd search "query"`.