Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add MilkyWay008/Hermes-OTG --skill qmdgit clone --depth 1 https://github.com/MilkyWay008/Hermes-OTGWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/milkyway008/hermes-otg/qmd)<a href="https://agentmods.dev/skills/milkyway008/hermes-otg/qmd"><img src="https://agentmods.dev/badge/skills/milkyway008/hermes-otg/qmd/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/milkyway008/hermes-otg/qmd"><img src="https://agentmods.dev/badge/skills/milkyway008/hermes-otg/qmd.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00014 | $0.03420 |
| Opus 5 | $0.00007 | $0.01710 |
| Sonnet 5 | $0.00003 | $0.00684 |
| Haiku 4.5 | $0.00001 | $0.00342 |
Grade B, and why
qmd scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash - Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash - This is a copy
100% identical to qmd — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 442 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QMD — Query Markup Documents
Local, on-device search engine for personal knowledge bases. Indexes markdown notes, meeting transcripts, documentation, and any text-based files, then provides hybrid search combining keyword matching, semantic understanding, and LLM-powered reranking — all running locally with no cloud dependencies.
Created by Tobi Lütke. MIT licensed.
When to Use
- User asks to search their notes, docs, knowledge base, or meeting transcripts
- User wants to find something across a large collection of markdown/text files
- User wants semantic search ("find notes about X concept") not just keyword grep
- User has already set up qmd collections and wants to query them
- User asks to set up a local knowledge base or document search system
- Keywords: "search my notes", "find in my docs", "knowledge base", "qmd"
Prerequisites
Node.js >= 22 (required)
# Check version
node --version # must be >= 22
# macOS — install or upgrade via Homebrew
brew install node@22
# Linux — use NodeSource or nvm
curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash -
sudo apt-get install -y nodejs
# or with nvm:
nvm install 22 && nvm use 22
SQLite with Extension Support (macOS only)
macOS system SQLite lacks extension loading. Install via Homebrew:
brew install sqlite
Install qmd
npm install -g @tobilu/qmd
# or with Bun:
bun install -g @tobilu/qmd
First run auto-downloads 3 local GGUF models (~2GB total):
| Model | Purpose | Size |
|---|---|---|
| embeddinggemma-300M-Q8_0 | Vector embeddings | ~300MB |
| qwen3-reranker-0.6b-q8_0 | Result reranking | ~640MB |
| qmd-query-expansion-1.7B | Query expansion | ~1.1GB |
Verify Installation
qmd --version
qmd status
Quick Reference
| Command | What It Does | Speed |
|---|---|---|
qmd search "query" |
BM25 keyword search (no models) | ~0.2s |
qmd vsearch "query" |
Semantic vector search (1 model) | ~3s |
qmd query "query" |
Hybrid + reranking (all 3 models) | ~2-3s warm, ~19s cold |
qmd get <docid> |
Retrieve full document content | instant |
qmd multi-get "glob" |
Retrieve multiple files | instant |
qmd collection add <path> --name <n> |
Add a directory as a collection | instant |
qmd context add <path> "description" |
Add context metadata to improve retrieval | instant |
qmd embed |
Generate/update vector embeddings | varies |
qmd status |
Show index health and collection info | instant |
qmd mcp |
Start MCP server (stdio) | persistent |
qmd mcp --http --daemon |
Start MCP server (HTTP, warm models) | persistent |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 442 lines · 14 tokens per session scan B 3c53e604f099
qmd is a skill published in the GitHub repository MilkyWay008/Hermes-OTG (15 stars, last pushed 27d ago), licensed MIT. It adds 14 tokens to every session and 3,420 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 2 findings (asks for root, makes network calls). It is 100% identical to qmd, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
distilly
Build and use evidence-grounded local person profiles with Distilly's exact five-tool workflow. Use when a user asks to research, ingest, distill, update, correct, retrieve, or recall a real or fictional person's profile, voice, boundaries, or evidence.
hermes-memory-providers
Install and configure Mnemosyne as a Hermes Agent memory provider — local SQLite with vector search, episodic consolidation, and temporal knowledge graphs.
mnemosyne
Persistent cross-session memory via Mnemosyne — store, recall, and consolidate facts, preferences, and context.
mnemosyne-memory-override
Hard rule override that forces Mnemosyne for all durable memory storage. The legacy memory tool is DEPRECATED for user preferences, credentials, and project conventions. Use memory ONLY for ephemeral session state.
hermes-context-optimization
Use this when the user asks about Hermes prompt/context size, “hello” startup cost, compression behavior, memory/profile bloat, tool-schema overhead, skill loading, session-store/search-index storage, or multimodal/visual-context approaches such as Snapcompact.
hermes-mnemosyne
Mnemosyne is Hermes' primary local-first memory engine — SQLite with vector + FTS5 hybrid search, 19+ tools, auto-consolidation, and a standalone CLI. It's a pip-installed plugin (not a built-in toolset) discovered via $HERMESHOME/plugins/mnemosyne/.