qmd

Name: qmd
Availability: InStock
Author: plaidmustache

Search local knowledge bases with hybrid BM25 and vector retrieval.

Updated May 4, 2026

Search Tags:#mcp #knowledge-base #vector-search #bm25 #local-search #qmd #hybrid-retrieval

Ready to run

npx skills add https://github.com/Plaidmustache/hermes-nulab --skill qmd-plaidmustache

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: qmd
Source: https://github.com/Plaidmustache/hermes-nulab/tree/main/optional-skills/research/qmd
Command: npx skills add https://github.com/Plaidmustache/hermes-nulab --skill qmd-plaidmustache

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Local, on-device search of your personal knowledge bases, notes, docs, and meeting transcripts to keep data private and quickly retrievable.

Core Features & Use Cases

Hybrid retrieval: BM25 keyword search plus vector embeddings with LLM reranking for high-quality results.
On-device indexing and CLI-based workflows with Hermes Agent MCP integration for seamless automation.
Use Case: researchers and knowledge workers who want fast, private search across notes and documents without cloud dependencies.

Quick Start

Install qmd locally, index your collections with qmd embed, and start querying with qmd search or qmd query.

Frequently Asked Questions about qmd

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

FAQPage Schema

How do I search personal notes and docs locally without cloud dependencies?▼

Local note and document search uses a hybrid retrieval pipeline combining BM25 keyword search and vector embeddings with LLM reranking. This on-device indexing keeps data private and quickly retrievable without cloud dependencies.

What is hybrid retrieval for personal knowledge bases?▼

Hybrid retrieval combines BM25 keyword search with vector search and LLM reranking. This mechanism improves result quality by matching exact terms while understanding semantic context across notes and meeting transcripts.

How do I index local document collections for CLI and MCP workflows?▼

To index local document collections, install the tool locally and run the embed command on your files. After indexing, query the collection using search or query commands within CLI and MCP-integrated workflows.

Does local vector search work for team knowledge bases and project documentation?▼

Local vector search works for team knowledge bases and project documentation through on-device indexing. It supports MCP-integrated workflows for seamless automation, making it suitable for personal research and team environments.

What is the best way to search meeting transcripts locally?▼

The best way to search meeting transcripts locally is using a hybrid retrieval approach. It combines BM25 and vector search with LLM reranking to provide high-quality results while keeping transcripts private on your device.