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 holon-run/uxc --skill qmd-mcp-skillgit clone --depth 1 https://github.com/holon-run/uxcWrote 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/holon-run/uxc/qmd-mcp-skill)<a href="https://agentmods.dev/skills/holon-run/uxc/qmd-mcp-skill"><img src="https://agentmods.dev/badge/skills/holon-run/uxc/qmd-mcp-skill/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/holon-run/uxc/qmd-mcp-skill"><img src="https://agentmods.dev/badge/skills/holon-run/uxc/qmd-mcp-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00044 | $0.01325 |
| Opus 5 | $0.00022 | $0.00662 |
| Sonnet 5 | $0.00009 | $0.00265 |
| Haiku 4.5 | $0.00004 | $0.00133 |
Grade A, and why
qmd-mcp-skill scanned grade A with 0 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 10d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QMD MCP Skill
Use this skill to query a local QMD index through uxc using a fixed MCP stdio link.
Reuse the uxc skill for generic protocol discovery, JSON envelope parsing, and daemon lifecycle basics.
Prerequisites
uxcis installed and available inPATH.qmdis installed and available in the runtimePATH, or can be launched through a shell wrapper.- A QMD index already exists and is healthy:
qmd statusqmd updateqmd embed
- For GPU-backed setups, the shell used by
qmd mcpalready exports any required runtime environment such asCUDA_PATH,CUDACXX,LD_LIBRARY_PATH, or Node/nvm initialization.
Core Workflow
- Verify the local QMD index first:
qmd status- Confirm collections, vector count, and device look reasonable before linking MCP.
- Use a fixed link command by default:
command -v qmd-mcp-cli- If missing and
qmdalready works in the current shell:uxc link --daemon-idle-ttl 0 qmd-mcp-cli "qmd mcp"
- If
qmddepends onnvm, CUDA env, or other shell setup, wrap it explicitly:uxc link --daemon-idle-ttl 0 qmd-mcp-cli "/bin/bash -lc 'export NVM_DIR=$HOME/.nvm; . $NVM_DIR/nvm.sh; nvm use 23 >/dev/null; export CUDA_PATH=/usr/local/cuda-11.6; export CUDA_HOME=/usr/local/cuda-11.6; export CUDACXX=/usr/local/cuda-11.6/bin/nvcc; export LD_LIBRARY_PATH=/usr/local/cuda-11.6/lib64:${LD_LIBRARY_PATH:-}; export NODE_LLAMA_CPP_CMAKE_OPTION_CMAKE_CUDA_ARCHITECTURES=86; export NODE_LLAMA_CPP_GPU=cuda; qmd mcp'"
qmd-mcp-cli -h- If command conflict is detected and cannot be safely reused, stop and ask skill maintainers to pick another fixed command name.
- Confirm the daemon-backed stdio path is active:
uxc daemon statusuxc daemon sessions
- Inspect operation schema before execution:
qmd-mcp-cli query -hqmd-mcp-cli get -hqmd-mcp-cli multi_get -hqmd-mcp-cli status -h
- Prefer typed retrieval over CLI-style auto expansion:
- Start with
queryusing explicitlex/vec/hydesearches - Use
getormulti_getonly after narrowing candidates
- Start with
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 99 lines · 44 tokens per session scan A 9bbef8d636d5
qmd-mcp-skill is a skill published in the GitHub repository holon-run/uxc (113 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 1,325 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
lap
LAP CLI -- compile, search, and manage API specs for AI agents. Use when working with API specifications (OpenAPI, GraphQL, AsyncAPI, Protobuf, Postman), compiling specs to LAP format, searching the LAP registry, generating skills from API specs, or publishing APIs. Commands: init, compile, search, get, skill…
lap
LAP CLI -- compile, search, and manage API specs for AI agents. Use when working with API specifications (OpenAPI, GraphQL, AsyncAPI, Protobuf, Postman), compiling specs to LAP format, searching the LAP registry, generating skills from API specs, or publishing APIs. Commands: init, compile, search, get, skill…
bedrock-rag
Build RAG on Amazon Bedrock Knowledge Bases — ingestion, chunking, embeddings, vector stores, Retrieve and RetrieveAndGenerate, citations, and Guardrails contextual grounding. Use when a chatbot must answer from a document corpus with sources.
google-adk-python
Build AI agents with Google Agent Development Kit (ADK) for Python. Use when creating multi-agent systems, tool-using agents, or orchestrating LLM workflows with Google Cloud.
linear
Linear project management — issues, cycles, and projects via GraphQL API.
ai-automation
Workflow automation skills using AI. Build chatbots, automate repetitive tasks, integrate LLMs into pipelines, design intent-based assistants. Triggers on: chatbot, automation, workflow, AI agent, RAG, LLM integration, intent recognition, conversation design.