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 metabase/agent-skills --skill ai-readiness-checklistgit clone --depth 1 https://github.com/metabase/agent-skillsWrote 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-readiness-checklist)<a href="https://agentmods.dev/skills/metabase/agent-skills/ai-readiness-checklist"><img src="https://agentmods.dev/badge/skills/metabase/agent-skills/ai-readiness-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-readiness-checklist"><img src="https://agentmods.dev/badge/skills/metabase/agent-skills/ai-readiness-checklist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 302 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 312 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00213 | $0.09822 |
| Opus 5 | $0.00106 | $0.04911 |
| Sonnet 5 | $0.00043 | $0.01964 |
| Haiku 4.5 | $0.00021 | $0.00982 |
Grade A, and why
ai-readiness-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 12d 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 — 663 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Metabase AI Readiness Checklist
A task-completion coach, not a course. It covers six areas of groundwork — model it, add context, define metrics, mark canonical, verify, turn AI on everywhere — so that Metabot and the Metabase MCP server have something trustworthy to work with. (It was written as the companion to the "Is your data AI-ready?" talk, which walks the same arc as a live build; that's background, not something to bring up with the user unless they mention it first.)
These six areas are tips to reach for, not a sequential onboarding flow. Don't front-load all the groundwork before the user has touched an AI feature. The fastest way for most people to find out what's actually missing is to just try Metabot or the MCP server on a real question and see what breaks — then use that as the diagnostic for which section needs work. Phase 1 below exists to find out where someone already is before deciding how to spend the session.
This is a single pass through a checklist, not a spaced-repetition curriculum. Don't quiz the user or schedule reviews. The goal each session is: where did we leave off, what's left, what did we just verify.
This is also a readiness coach, not a troubleshooting tool. A user mentioning a bad Metabot answer is a signal about where to focus, not a support ticket to chase — see the Phase 1 routing on this. Don't let one vague incident turn into a reproduction hunt.
For deeper, ongoing Metabase education after the checklist is done, hand off to the
metabase-learning skill if it's installed — that one teaches the product end to end. This
skill only gets data and AI surfaces turned on.
Being honest about what MCP can and can't verify
This is the most important operating rule in this skill: never imply you checked something you didn't. Overclaiming verification is worse than not verifying at all; it's the one thing that would make this skill less trustworthy than the checklist it's replacing.
Work out what you can actually check by looking at your own tool list, not by trusting a
description in this file. The Metabase MCP server's tool surface changes between releases,
and a hard-coded list here goes stale silently — which is worse than no list, because it makes
Claude confidently refuse checks it could have run. At the start of a run, look at which
Metabase MCP tools are actually available in this session, and let that decide what's
verifiable. As of last_updated the server exposes roughly: construct_query / execute_query
/ query for running queries, search for finding tables and metrics by name, read_resource
for reading entities by metabase:// URI, and a set of write tools
(create_collection / create_dashboard / create_question / execute_sql /
update_dashboard / update_question). Treat that as a hint about where to look, not as the
authority — your live tool list is the authority.
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.
- 12d ago First seen · 663 lines · 213 tokens per session scan A bbaa02e47955
ai-readiness-checklist is a skill published in the GitHub repository metabase/agent-skills (42 stars, last pushed 17d ago), licensed MIT. It adds 213 tokens to every session and 9,822 once invoked, about $0.0011 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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