qmd — community claude-turbo-search, community, ide skills

v1.0.0

About this Skill

Perfect for Development Agents needing rapid Markdown search and content analysis capabilities. Local hybrid search for markdown notes and docs. Use BEFORE reading files to save tokens - search first, read only whats relevant. Provides 90% token savings on exploration tasks.

iagocavalcante iagocavalcante
[0]
[0]
Updated: 3/12/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reviewed Landing Page Review Score: 9/11

Killer-Skills keeps this page indexable because it adds recommendation, limitations, and review signals beyond the upstream repository text.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review Locale and body language aligned
Review Score
9/11
Quality Score
57
Canonical Locale
en
Detected Body Locale
en

Perfect for Development Agents needing rapid Markdown search and content analysis capabilities. Local hybrid search for markdown notes and docs. Use BEFORE reading files to save tokens - search first, read only whats relevant. Provides 90% token savings on exploration tasks.

Core Value

Empowers agents to efficiently explore codebases, locate specific functionality, and find related files using qmd's local search engine for Markdown notes, docs, and knowledge bases, thereby reducing token usage through targeted file discovery.

Ideal Agent Persona

Perfect for Development Agents needing rapid Markdown search and content analysis capabilities.

Capabilities Granted for qmd

Exploring large codebases for specific functionality
Finding related Markdown files to a topic
Analyzing documentation for concept understanding

! Prerequisites & Limits

  • Requires local Markdown files or documentation
  • Limited to searching within Markdown format
  • Ideal for use before reading files directly to reduce token usage

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is qmd?

Perfect for Development Agents needing rapid Markdown search and content analysis capabilities. Local hybrid search for markdown notes and docs. Use BEFORE reading files to save tokens - search first, read only whats relevant. Provides 90% token savings on exploration tasks.

How do I install qmd?

Run the command: npx killer-skills add iagocavalcante/claude-turbo-search/qmd. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for qmd?

Key use cases include: Exploring large codebases for specific functionality, Finding related Markdown files to a topic, Analyzing documentation for concept understanding.

Which IDEs are compatible with qmd?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for qmd?

Requires local Markdown files or documentation. Limited to searching within Markdown format. Ideal for use before reading files directly to reduce token usage.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add iagocavalcante/claude-turbo-search/qmd. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use qmd immediately in the current project.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

qmd

Install qmd, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

qmd - Quick Markdown Search

Local search engine for Markdown notes, docs, and knowledge bases. Use this to find relevant files BEFORE reading them to dramatically reduce token usage.

When to Use (IMPORTANT)

ALWAYS prefer qmd search over reading files directly when:

  • Exploring a codebase or documentation
  • Looking for specific functionality or concepts
  • Finding related files to a topic
  • Answering questions about the codebase

Token savings: Instead of reading 5 files (5000+ tokens), search first (50 tokens) and read only the relevant sections (200 tokens).

Search Priority (follow this order)

  1. qmd search "query" - Fast BM25 keyword search. Use this first, it's instant.
  2. qmd vsearch "query" - Semantic similarity. Use only if keyword search fails.
  3. qmd query "query" - Hybrid + reranking. Avoid unless user explicitly requests highest quality.

Common Commands

bash
1# Fast keyword search (DEFAULT - use this first) 2qmd search "authentication" -n 10 3 4# Search specific collection 5qmd search "api routes" -c my-project 6 7# Get file paths only (for deciding what to read) 8qmd search "database schema" --files 9 10# JSON output for parsing 11qmd search "error handling" --json 12 13# Semantic search (slower, use as fallback) 14qmd vsearch "how does the login flow work"

Retrieve Documents

bash
1# Get specific file 2qmd get "path/to/file.md" 3 4# Get by document ID from search results 5qmd get "#docid" 6 7# Get multiple files 8qmd multi-get "docs/*.md" --json

Workflow Example

Bad (token expensive):

1. Read src/auth/login.ts (800 tokens)
2. Read src/auth/session.ts (600 tokens)
3. Read src/auth/middleware.ts (500 tokens)
4. Read src/auth/types.ts (400 tokens)
→ Found answer in middleware.ts
Total: 2300 tokens

Good (token efficient):

1. qmd search "authentication middleware" --files (response: 50 tokens)
   → Returns: src/auth/middleware.ts:45-62
2. Read only the relevant section (150 tokens)
Total: 200 tokens (90% savings!)

Tips for Claude

  • Search before reading: Always try qmd search first
  • Use --files flag: Get paths without content to decide what to read
  • Be specific: More specific queries = better results
  • Check collections: Use qmd status to see indexed collections
  • Combine with cartographer: CODEBASE_MAP.md gives structure, qmd gives content

Maintenance

bash
1qmd status # Check index health and collections 2qmd update # Re-index changed files (fast) 3qmd embed # Update vector embeddings (slower)

Related Skills

Looking for an alternative to qmd or another community skill for your workflow? Explore these related open-source skills.

View All

openclaw-release-maintainer

Logo of openclaw
openclaw

Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞

333.8k
0
AI

widget-generator

Logo of f
f

Generate customizable widget plugins for the prompts.chat feed system

149.6k
0
AI

flags

Logo of vercel
vercel

The React Framework

138.4k
0
Browser

pr-review

Logo of pytorch
pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

98.6k
0
Developer