tobi / qmd
Install for your project team
Run this command in your project directory to install the skill for your entire team:
mkdir -p .claude/skills/qmd && curl -L -o skill.zip "https://fastmcp.me/Skills/Download/1046" && unzip -o skill.zip -d .claude/skills/qmd && rm skill.zip
Project Skills
This skill will be saved in .claude/skills/qmd/ and checked into git. All team members will have access to it automatically.
Important: Please verify the skill by reviewing its instructions before using it.
Search personal markdown knowledge bases, notes, meeting transcripts, and documentation using QMD - a local hybrid search engine. Combines BM25 keyword search, vector semantic search, and LLM re-ranking. Use when users ask to search notes, find documents, look up information in their knowledge base, retrieve meeting notes, or search documentation. Triggers on "search markdown files", "search my notes", "find in docs", "look up", "what did I write about", "meeting notes about".
Skill Content
---
name: qmd
description: Search markdown knowledge bases, notes, and documentation using QMD. Use when users ask to search notes, find documents, or look up information.
license: MIT
compatibility: Requires qmd CLI or MCP server. Install via `npm install -g @tobilu/qmd`.
metadata:
author: tobi
version: "2.0.0"
allowed-tools: Bash(qmd:*), mcp__qmd__*
---
# QMD - Quick Markdown Search
Local search engine for markdown content.
## Status
!`qmd status 2>/dev/null || echo "Not installed: npm install -g @tobilu/qmd"`
## MCP: `query`
```json
{
"searches": [
{ "type": "lex", "query": "CAP theorem consistency" },
{ "type": "vec", "query": "tradeoff between consistency and availability" }
],
"collections": ["docs"],
"limit": 10
}
```
### Query Types
| Type | Method | Input |
|------|--------|-------|
| `lex` | BM25 | Keywords — exact terms, names, code |
| `vec` | Vector | Question — natural language |
| `hyde` | Vector | Answer — hypothetical result (50-100 words) |
### Writing Good Queries
**lex (keyword)**
- 2-5 terms, no filler words
- Exact phrase: `"connection pool"` (quoted)
- Exclude terms: `performance -sports` (minus prefix)
- Code identifiers work: `handleError async`
**vec (semantic)**
- Full natural language question
- Be specific: `"how does the rate limiter handle burst traffic"`
- Include context: `"in the payment service, how are refunds processed"`
**hyde (hypothetical document)**
- Write 50-100 words of what the *answer* looks like
- Use the vocabulary you expect in the result
**expand (auto-expand)**
- Use a single-line query (implicit) or `expand: question` on its own line
- Lets the local LLM generate lex/vec/hyde variations
- Do not mix `expand:` with other typed lines — it's either a standalone expand query or a full query document
### Combining Types
| Goal | Approach |
|------|----------|
| Know exact terms | `lex` only |
| Don't know vocabulary | Use a single-line query (implicit `expand:`) or `vec` |
| Best recall | `lex` + `vec` |
| Complex topic | `lex` + `vec` + `hyde` |
First query gets 2x weight in fusion — put your best guess first.
### Lex Query Syntax
| Syntax | Meaning | Example |
|--------|---------|---------|
| `term` | Prefix match | `perf` matches "performance" |
| `"phrase"` | Exact phrase | `"rate limiter"` |
| `-term` | Exclude | `performance -sports` |
Note: `-term` only works in lex queries, not vec/hyde.
### Collection Filtering
```json
{ "collections": ["docs"] } // Single
{ "collections": ["docs", "notes"] } // Multiple (OR)
```
Omit to search all collections.
## Other MCP Tools
| Tool | Use |
|------|-----|
| `get` | Retrieve doc by path or `#docid` |
| `multi_get` | Retrieve multiple by glob/list |
| `status` | Collections and health |
## CLI
```bash
qmd query "question" # Auto-expand + rerank
qmd query $'lex: X\nvec: Y' # Structured
qmd query $'expand: question' # Explicit expand
qmd search "keywords" # BM25 only (no LLM)
qmd get "#abc123" # By docid
qmd multi-get "journals/2026-*.md" -l 40 # Batch pull snippets by glob
qmd multi-get notes/foo.md,notes/bar.md # Comma-separated list, preserves order
```
## HTTP API
```bash
curl -X POST http://localhost:8181/query \
-H "Content-Type: application/json" \
-d '{"searches": [{"type": "lex", "query": "test"}]}'
```
## Setup
```bash
npm install -g @tobilu/qmd
qmd collection add ~/notes --name notes
qmd embed
```