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 laurigates/dotfiles --skill aiq-researchgit 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-research)<a href="https://agentmods.dev/skills/laurigates/dotfiles/aiq-research"><img src="https://agentmods.dev/badge/skills/laurigates/dotfiles/aiq-research.svg" alt="Measured on agentmods" 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 | $0.00025 | $0.03077 |
| Opus 5 | $0.00013 | $0.01538 |
| Sonnet 5 | $0.00005 | $0.00615 |
| Haiku 4.5 | $0.00003 | $0.00308 |
Grade A, and why
aiq-research 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 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.
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 — 357 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AIQ Research Skill
Purpose
Use this skill to call a locally running NVIDIA AI-Q Blueprint server through the helper script at
scripts/aiq.py.
Use this skill for research-shaped requests, including:
- "deep research on ..."
- "AIQ research ..."
- "research ..."
- "use AI-Q to answer ..."
- "ask AI-Q about ..."
Do not use this skill for install, deploy, start, stop, UI, CLI, Docker, Helm, or troubleshooting requests. Those
belong to aiq-deploy.
Prerequisites
Users need:
- Python 3.11+ available as
python3. - A reachable local or self-hosted AI-Q Blueprint backend.
AIQ_SERVER_URLset when the backend is not running athttp://localhost:8000; non-local values must be trusted by the user before any query is sent.- A backend configured with authentication disabled for this public helper, or a separate authenticated AI-Q skill for authenticated environments.
- Network access from the local machine to the AI-Q backend URL.
- Credentials configured in the backend environment, not in this skill. This public helper does not collect or manage API keys.
The helper script has no third-party Python package dependencies; it uses Python standard-library HTTP modules.
Instructions
- Resolve the target backend URL.
- Run
healthbefore sending research requests. - If no backend is reachable, ask for a backend URL or hand off to
aiq-deploy. - Before sending any user query, state the exact AI-Q backend URL that will receive it. For non-local URLs, continue only if the user has explicitly confirmed that URL is trusted in the current conversation.
- Poll asynchronous deep research jobs when AI-Q returns a job ID.
- Present returned reports with citations and source URLs intact.
- Stop on failed jobs and show the returned error; do not retry automatically.
Step 1 - Resolve the backend
Use AIQ_SERVER_URL when set. Otherwise try the default local backend:
python3 $SKILL_DIR/scripts/aiq.py health
Expected output: JSON from a reachable AI-Q health endpoint.
What ships with it
5 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.
- 8d ago First seen · 357 lines · 25 tokens per session scan A a765670f3d46
aiq-research is a skill published in the GitHub repository laurigates/dotfiles (10 stars, last pushed 3d ago), licensed MIT. It adds 25 tokens to every session and 3,077 once invoked, about $0.0001 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 2026-08-31.
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vibe
Delegate a coding task to a cheap AI model (Mistral Vibe by default, but any provider Vibe knows about — DeepSeek, Gemini Flash, etc.) and supervise the result via git diff. Claude orchestrates, the cheap model codes. Claude consumes 500-1500 tokens per delegation regardless of how many file reads the delegate does…
work-on
End-to-end workflow for shipping a GitHub issue. Assesses complexity, builds a tailored workflow, and orchestrates skills from research through PR.
gather-context
Build comprehensive understanding of a problem by gathering context from GitHub issues, codebase exploration, git history, and linked references. Use when starting work on an issue or investigating a problem.
plan
Design an implementation as a task graph with explicit dependencies, parallel phases, and agent team topology. Use after gathering context and agreeing on an approach, when the work is large enough that sequencing it badly costs real time.
youtube
Fetch and structure the transcript from this YouTube video.