/add-qmd
Add QMD (Query Markup Documents) as an advanced memory search backend. Upgrades the built-in grep-based memory_search with hybrid BM25 + vector semantic search + LLM re-ranking. Fully local, no API keys needed. Triggers on "add qmd", "qmd memory", "semantic memory search",
$ npx -y skills add sbusso/claudeclaw --skill add-qmd --agent claude-codeHow it fires
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- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
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/add-qmd
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Add QMD (Query Markup Documents) as an advanced memory search backend. Upgrades the built-in grep-based memory_search with hybrid BM25 + vector semantic search + LLM re-ranking. Fully local, no API keys needed. Triggers on "add qmd", "qmd memory", "semantic memory search",
SKILL.md
add-qmd.SKILL.mdname: add-qmd
description: Add QMD (Query Markup Documents) as an advanced memory search backend. Upgrades the built-in grep-based memory_search with hybrid BM25 + vector semantic search + LLM re-ranking. Fully local, no API keys needed. Triggers on "add qmd", "qmd memory", "semantic memory search", "upgrade memory".
Add QMD Memory Backend
QMD (https://github.com/tobi/qmd) is a local search engine for markdown files. It combines BM25 keyword search, vector semantic search, and LLM re-ranking — all running on-device via node-llama-cpp with GGUF models.
This skill upgrades ClaudeClaw's built-in grep-based `memory_search` MCP tool with QMD's hybrid search, giving agents much better recall across large memory collections.
Prerequisites
- ClaudeClaw with memory tools already working (memory_search, memory_save, memory_get)
- ~2GB disk space for GGUF embedding + reranking models (downloaded on first run)
- Node.js 20+
What This Skill Does
1. Installs QMD as an MCP server dependency 2. Configures QMD to index all group memory directories (`groups/*/memory/`, `groups/*/CLAUDE.md`, `groups/*/conversations/`) 3. Replaces the grep-based `memory_search` in `agent/runner/src/ipc-mcp-stdio.ts` with QMD's API 4. Adds QMD indexing to the agent startup flow (incremental re-index on each run)
Implementation Notes
QMD MCP Server
QMD exposes its search as an MCP server. Add to the agent runner's `mcpServers` config:
qmd: {
command: 'npx',
args: ['qmd', 'mcp', '--collection', collectionPath],
env: {},
}Indexing
QMD indexes markdown files into a local SQLite database. The collection should be configured per-group:
# Index a group's memory
qmd index --collection groups/{folder}/.qmd groups/{folder}/memory/ groups/{folder}/CLAUDE.md groups/{folder}/conversations/Search Integration
Replace the grep-based `memory_search` tool body with a call to QMD's MCP:
// Before (grep-based):
const results = grepFiles(args.query, allFiles);
// After (QMD):
// Use the qmd MCP server's search tool
// Returns semantically ranked results with relevance scores
Fallback
If QMD is not installed or indexing fails, fall back to the built-in grep-based search. This ensures memory tools always work even without QMD.
Not Implemented Yet
This skill is a specification for future implementation. The built-in grep-based memory tools work without QMD. Run this skill when you want to upgrade to semantic search.
Read more
name: add-qmd description: Add QMD (Query Markup Documents) as an advanced memory search backend. Upgrades the built-in grep-based memory_search with hybrid BM25 + vector semantic search + LLM re-ranking. Fully local, no API keys needed. Triggers on "add qmd", "qmd memory", "semantic memory search", "upgrade memory".
Add QMD Memory Backend
QMD (https://github.com/tobi/qmd) is a local search engine for markdown files. It combines BM25 keyword search, vector semantic search, and LLM re-ranking — all running on-device via node-llama-cpp with GGUF models.
This skill upgrades ClaudeClaw's built-in grep-based `memory_search` MCP tool with QMD's hybrid search, giving agents much better recall across large memory collections.
Prerequisites
- ClaudeClaw with memory tools already working (memory_search, memory_save, memory_get)
- ~2GB disk space for GGUF embedding + reranking models (downloaded on first run)
- Node.js 20+
What This Skill Does
1. Installs QMD as an MCP server dependency 2. Configures QMD to index all group memory directories (`groups/*/memory/`, `groups/*/CLAUDE.md`, `groups/*/conversations/`) 3. Replaces the grep-based `memory_search` in `agent/runner/src/ipc-mcp-stdio.ts` with QMD's API 4. Adds QMD indexing to the agent startup flow (incremental re-index on each run)
Implementation Notes
QMD MCP Server
QMD exposes its search as an MCP server. Add to the agent runner's `mcpServers` config:
qmd: {
command: 'npx',
args: ['qmd', 'mcp', '--collection', collectionPath],
env: {},
}Indexing
QMD indexes markdown files into a local SQLite database. The collection should be configured per-group:
# Index a group's memory
qmd index --collection groups/{folder}/.qmd groups/{folder}/memory/ groups/{folder}/CLAUDE.md groups/{folder}/conversations/Search Integration
Replace the grep-based `memory_search` tool body with a call to QMD's MCP:
// Before (grep-based): const results = grepFiles(args.query, allFiles); // After (QMD): // Use the qmd MCP server's search tool // Returns semantically ranked results with relevance scores
Fallback
If QMD is not installed or indexing fails, fall back to the built-in grep-based search. This ensures memory tools always work even without QMD.
Not Implemented Yet
This skill is a specification for future implementation. The built-in grep-based memory tools work without QMD. Run this skill when you want to upgrade to semantic search.
Repo: sbusso/claudeclaw
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