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Anshumanbh Qmd

Search markdown knowledge bases efficiently.

Rating
4.2 (17 reviews)
Downloads
12,244 downloads
Version
1.0.0

Overview

Search markdown knowledge bases efficiently.

Complete Documentation

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QMD Search Skill

Search markdown knowledge bases efficiently using qmd, a local indexing tool that uses BM25 + vector embeddings to return only relevant snippets instead of full files.

Why Use This

  • 96% token reduction - Returns relevant snippets instead of reading entire files
  • Instant results - Pre-indexed content means fast searches
  • Local & private - All indexing and search happens locally
  • Hybrid search - BM25 for keyword matching, vector search for semantic similarity

Commands

Search (BM25 keyword matching)

bash
qmd search "your query" --collection <name>
Fast, accurate keyword-based search. Best for specific terms or phrases.

Vector Search (semantic)

bash
qmd vsearch "your query" --collection <name>
Semantic similarity search. Best for conceptual queries where exact words may vary.

Hybrid Search (both + reranking)

bash
qmd hybrid "your query" --collection <name>
Combines both approaches with LLM reranking. Most thorough but often overkill.

How to Use

  • Check if collection exists:
bash
qmd collection list
  • Search the collection:
bash
# For specific terms
   qmd search "api authentication" --collection notes

   # For conceptual queries
   qmd vsearch "how to handle errors gracefully" --collection notes
  • Read results: qmd returns relevant snippets with file paths and context

Setup (if qmd not installed)

bash
# Install qmd
bun install -g https://github.com/tobi/qmd

# Add a collection (e.g., Obsidian vault)
qmd collection add ~/path/to/vault --name notes

# Generate embeddings for vector search
qmd embed --collection notes

Invocation Examples

text
/qmd api authentication          # BM25 search for "api authentication"
/qmd how to handle errors --semantic   # Vector search for conceptual query
/qmd --setup                     # Guide through initial setup

Best Practices

  • Use BM25 search (qmd search) for specific terms, names, or technical keywords
  • Use vector search (qmd vsearch) when looking for concepts where wording may vary
  • Avoid hybrid search unless you need maximum recall - it's slower
  • Re-run qmd embed after adding significant new content to keep vectors current

Handling Arguments

  • $ARGUMENTS contains the full search query
  • If --semantic flag is present, use qmd vsearch instead of qmd search
  • If --setup flag is present, guide user through installation and collection setup
  • If --collection is specified, use that collection; otherwise default to checking available collections

Workflow

  • Parse arguments from $ARGUMENTS
  • Check if qmd is installed (which qmd)
  • If not installed, offer to guide setup
  • If searching:
  • List collections if none specified
  • Run appropriate search command
  • Present results to user with file paths
  • If user wants to read a specific result, use the Read tool on the file path

Installation

Terminal bash

openclaw install anshumanbh-qmd
    
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💻Code Examples

example.sh
# For specific terms
   qmd search "api authentication" --collection notes

   # For conceptual queries
   qmd vsearch "how to handle errors gracefully" --collection notes
example.sh
# Install qmd
bun install -g https://github.com/tobi/qmd

# Add a collection (e.g., Obsidian vault)
qmd collection add ~/path/to/vault --name notes

# Generate embeddings for vector search
qmd embed --collection notes
example.txt
/qmd api authentication          # BM25 search for "api authentication"
/qmd how to handle errors --semantic   # Vector search for conceptual query
/qmd --setup                     # Guide through initial setup

Tags

#search_and-research

Quick Info

Category Web Scrapers
Model Claude 3.5
Complexity One-Click
Author anshumanbh
Last Updated 3/10/2026
🚀
Optimized for
Claude 3.5
🧠

Ready to Install?

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openclaw install anshumanbh-qmd