qmd — community claude-turbo-search, community, ide skills, Claude Code, Cursor, Windsurf

v1.0.0
GitHub

About this Skill

Ideal for Code Analysis Agents requiring efficient Markdown search and semantic indexing for large codebases in Claude Code Optimized file search and semantic indexing for large codebases in Claude Code

iagocavalcante iagocavalcante
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[0]
Updated: 3/5/2026

Agent Capability Analysis

The qmd skill by iagocavalcante is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance.

Ideal Agent Persona

Ideal for Code Analysis Agents requiring efficient Markdown search and semantic indexing for large codebases in Claude Code

Core Value

Empowers agents to perform optimized file searches using Quick Markdown Search, reducing token usage through semantic indexing of Markdown notes, docs, and knowledge bases, and supporting protocols like local file search

Capabilities Granted for qmd

Searching large codebases for specific functionality
Finding related files to a topic in documentation
Answering questions about codebases using semantic indexing

! Prerequisites & Limits

  • Requires local access to Markdown files
  • Optimized for Claude Code and Markdown file formats
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

qmd

Install qmd, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly

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)

FAQ & Installation Steps

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

? Frequently Asked Questions

What is qmd?

Ideal for Code Analysis Agents requiring efficient Markdown search and semantic indexing for large codebases in Claude Code Optimized file search and semantic indexing for large codebases in Claude Code

How do I install qmd?

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

What are the use cases for qmd?

Key use cases include: Searching large codebases for specific functionality, Finding related files to a topic in documentation, Answering questions about codebases using semantic indexing.

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 access to Markdown files. Optimized for Claude Code and Markdown file formats.

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. 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.

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