Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/metabase/agent-skillsnpx agentmods add skills/metabase/agent-skills/ai-governance-checklistWrote 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/metabase/agent-skills/ai-governance-checklist)<a href="https://agentmods.dev/skills/metabase/agent-skills/ai-governance-checklist"><img src="https://agentmods.dev/badge/skills/metabase/agent-skills/ai-governance-checklist/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/metabase/agent-skills/ai-governance-checklist"><img src="https://agentmods.dev/badge/skills/metabase/agent-skills/ai-governance-checklist.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 | $0.00241 | $0.06585 |
| Opus 5 | $0.00120 | $0.03292 |
| Sonnet 5 | $0.00048 | $0.01317 |
| Haiku 4.5 | $0.00024 | $0.00658 |
Grade A, and why
ai-governance-checklist 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 11d 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 — 463 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Metabase AI Governance Checklist
A task-completion coach, not a course. Walks through the five levers that turn "can we roll out AI analytics safely" into an actual answer: who can use it, what it can see, how much it costs, where the model runs, and the audit trail — plus the sovereignty options (bring-your-own model, self-hosting) for orgs that want to go further than the defaults. (It was written as the companion to the "AI analytics, on your terms, on your infrastructure" talk; that's background, not something to raise with the user unless they mention it.)
This is a governance/rollout coach, not a data-modeling coach. For getting the underlying
data itself AI-ready — Transforms, the Glossary, Metrics, the Library — that's the
ai-readiness-checklist skill; hand off there if the user's actual question is "is my data
good enough for AI" rather than "who gets to use it and what can they see."
The five levers are independent dials, not a sequence. Unlike a data-modeling checklist, these don't build on each other — restricting who can use Metabot doesn't require setting token limits first. Answer whichever one the user showed up asking about; don't force all five before answering any of them.
This is a single pass, not a spaced-repetition curriculum. The goal each session is: where did we leave off, what's left, what did we just confirm.
For the data-readiness side of AI setup, hand off to ai-readiness-checklist if it's
installed. For general Metabase education, metabase-learning teaches the product end to end.
This skill only covers the governance/rollout layer.
Being honest about what MCP can and can't verify
Same operating rule as ai-readiness-checklist, and it matters even more here: almost
everything in this skill is an admin setting, not a queryable object. The Metabase MCP
server is a query-and-build surface — it can run queries, search by name, and read a resource.
It has no tool that reports "what are this group's AI usage limits," "is Metabot restricted to
verified content," "what does the system prompt say," or "does the audit log show this
conversation." Those are all self-reported/coached: ask, coach through the UI, take the user's
word for the state. Worth being precise about scope here: the separate Metabase CLI (mb) can
read and write actual content (tables, fields, cards, dashboards, transforms, collections)
directly over the API — but as of this writing it has no commands for groups, permissions,
Application settings, or Metabot configuration, which is what this skill is actually coaching
on. So unlike ai-readiness-checklist (where mb genuinely can execute several sections
instead of just coaching), this skill's self-reported framing holds even if the user has mb
set up — check the CLI's current command list before assuming otherwise, since that could
change.
What ships with it
1 file 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.
- 11d ago First seen · 463 lines · 241 tokens per session scan A 71d787ee95a4
ai-governance-checklist is a skill published in the GitHub repository metabase/agent-skills (42 stars, last pushed 16d ago), licensed MIT. It adds 241 tokens to every session and 6,585 once invoked, about $0.0012 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.
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