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 agentmods add skills/laurigates/dotfiles/aiq-deploynpx skills add laurigates/dotfiles --skill aiq-deploygit clone --depth 1 https://github.com/laurigates/dotfilesWrote 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/laurigates/dotfiles/aiq-deploy)<a href="https://agentmods.dev/skills/laurigates/dotfiles/aiq-deploy"><img src="https://agentmods.dev/badge/skills/laurigates/dotfiles/aiq-deploy.svg" alt="Measured on agentmods" 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 | $0.00027 | $0.03142 |
| Opus 5 | $0.00014 | $0.01571 |
| Sonnet 5 | $0.00005 | $0.00628 |
| Haiku 4.5 | $0.00003 | $0.00314 |
Grade A, and why
aiq-deploy scanned grade A with 1 finding 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 4d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sf http://localhost:8000/health This is a copy
100% identical to aiq-deploy — 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 — 352 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AIQ Deploy Skill
Purpose
Use this skill to get a local or self-hosted NVIDIA AI-Q Blueprint server running and verified for use by
aiq-research.
This skill owns setup, deployment, operational checks, troubleshooting, and shutdown. It does not run deep
research itself. After deployment is healthy, hand off the verified server URL to aiq-research.
The workflow stays explicit so deployment validation and handoff are repeatable across supported agent clients.
Prerequisites
Users need:
- Access to clone or update
https://github.com/NVIDIA-AI-Blueprints/aiq. - Git available in the shell.
- One deployment runtime:
- Docker Engine with Docker Compose v2 for the default durable local deployment.
- Python 3.11+ and
uvfor local process or CLI mode. - Node.js 20+ and
npmfor local browser UI development mode. kubectl1.28+, Helm 3.12+, and access to a Kubernetes cluster for Helm mode.
- Network access to GitHub, NVIDIA-hosted model endpoints, and any selected search provider.
- Credentials stored outside chat. Hosted-model usage requires
NVIDIA_API_KEY; web research requires at least one supported search provider key such asTAVILY_API_KEY,SERPER_API_KEY, orEXA_API_KEY. - System capacity for the selected runtime. Docker Compose mode starts the AI-Q backend and PostgreSQL by default;
browser UI mode also uses frontend port
3000. Self-hosted model or RAG deployments may require GPU resources.
Before writing secrets, verify deploy/.env is ignored:
git check-ignore deploy/.env
Expected output: deploy/.env or a matching ignore rule. If it is not ignored, stop and fix the ignore rule before
placing credentials in the file.
Instructions
- Locate or clone the AI-Q repository.
- Confirm the expected repository files exist.
- Select the deployment mode.
- Prepare
deploy/.envwithout overwriting user secrets. - Check runtime prerequisites for the selected path.
- Start the selected deployment.
- Run basic validation.
- Report the verified
AIQ_SERVER_URLforaiq-research. - Ask whether to run optional deep research completion validation.
What ships with it
17 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.
- BENCHMARK.md 4.1 KB
- evals/evals.json 1.4 KB
- references/configs.md 3.5 KB
- references/docker-compose.md 3.5 KB
- references/end-to-end-validation.md 5.6 KB
- references/env-and-secrets.md 3.8 KB
- references/frag.md 1.6 KB
- references/kubernetes-helm.md 983 B
- references/local-web.md 1.2 KB
- references/locate-or-clone.md 1.3 KB
- references/shutdown.md 2.2 KB
- references/skill-backend.md 1.4 KB
- references/terminal-cli.md 755 B
- references/troubleshooting.md 1.2 KB
- references/validation.md 3.2 KB
- skill-card.md 4.0 KB
- skill.oms.sig 7.5 KB
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.
- 4d ago First seen · 352 lines · 27 tokens per session scan A 9bfad6034823
aiq-deploy is a skill published in the GitHub repository laurigates/dotfiles (10 stars, last pushed today), licensed MIT. It adds 27 tokens to every session and 3,142 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to aiq-deploy, differing in 0 lines, and is treated as a copy.
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