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Found 2 Skills
A Metabase AI-readiness coach, covering the *data groundwork* that makes AI answers trustworthy: modeling with Transforms, table and field metadata, the Glossary, saved Metrics, and marking canonical content. Use this skill whenever the user wants to check if their data is ready for AI, prep the underlying data for Metabot or the Metabase MCP server, work through an AI readiness checklist, or audit their data modeling/metadata/metrics before turning on AI features. Trigger it even if the user just says "is my data AI ready?", "let's get set up for Metabot", or "run the AI readiness checklist" in a Metabase context. **Not this skill** if the question is about controlling who may use AI, capping AI spend, restricting what Metabot can see, auditing AI usage, or passing a security review — that's `ai-governance-checklist`. Rough test: this skill is about whether the data is good enough; that one is about who gets to point AI at it.
A Metabase AI-governance coach, covering the *controls and rollout* side of AI: who may use Metabot, what it can see, what it costs, where the model runs, and the audit trail. Use this skill whenever the user wants to roll out AI analytics safely, control who can use Metabot or what it can see, set spend or token limits on AI, restrict Metabot's system prompt, audit AI usage, evaluate bring-your-own model or self-hosting options for AI, or prep for a security review of AI features. Trigger it even if the user just says "how do we control AI access", "can we limit what Metabot sees", "we need an AI security review", or "run the AI governance checklist" in a Metabase context. **Not this skill** if the question is about whether the underlying data is modeled, documented, and trustworthy enough for AI — Transforms, metadata, the Glossary, Metrics, the Library — that's `ai-readiness-checklist`. Rough test: this skill is about who gets to point AI at the data; that one is about whether the data is good enough.