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 vasilyu1983/AI-Agents-public --skill data-metabasegit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/data-metabase)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/data-metabase"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/data-metabase/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/vasilyu1983/ai-agents-public/data-metabase"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/data-metabase.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 | $0.00042 | $0.03730 |
| Opus 5 | $0.00021 | $0.01865 |
| Sonnet 5 | $0.00008 | $0.00746 |
| Haiku 4.5 | $0.00004 | $0.00373 |
Grade A, and why
data-metabase 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 10d 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 — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Metabase Automation
Automate Metabase content, promotion, embedding, and admin refresh workflows.
Classic Metabase REST API still owns cards, dashboards, collections, permissions, and schema refresh operations. The newer Agent API is the right surface for headless semantic BI assistants and app-side AI workflows. Metabase v60 (April 2026) added an official MCP server and open-sourced AI; v61 (May 2026) added AI governance, dashboards-as-code via MCP, and per-group Metabot controls. v62 (June 2026, current line) added the official @metabase/cli, an Interactive Schema Viewer, a custom-visualization plugin SDK, an Alert Management hub, Library sub-collections, and expanded MCP capabilities — run SQL, create collections, and render interactive charts directly in the AI client. Verify /docs/latest and metabase.com/releases before citing version-specific behavior, since the release cadence is monthly.
Quick Reference
| Task | Path | Use When |
|---|---|---|
| Create/update questions and dashboards | Classic REST API + scripts/metabase_api.py |
Standard content automation and incremental upserts |
| Promote content between environments | Remote Sync or serialization | Git-backed promotion, reviewable diffs, cross-environment moves |
| Build embedded customer analytics | Embedding + tenants + embedding permissions | Multi-tenant apps, customer portals, row-level isolation |
| Build an AI analytics app | Agent API | Versioned, semantic, app-side AI querying |
| Integrate Metabase with an AI coding agent | MCP server (v60+) | Claude, Cursor, VS Code — generate questions and dashboards via conversation |
| Govern AI access by group | Metabot AI governance (v61+, Pro/Enterprise) | Per-group controls, token limits, usage analytics |
| Refresh schema metadata | Database sync/rescan endpoints | New tables, changed columns, stale field values |
| Tune native SQL questions | Export-first + native query patterns | Stable automation without guessing request shapes |
What ships with it
21 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.
- agents/openai.yaml 346 B
- assets/card-spec.template.json 296 B
- assets/dashboard-spec.template.json 107 B
- assets/dashcards-layout.template.json 312 B
- assets/embed-jwt-example.md 996 B
- data/sources.json 14 KB
- learnings.consolidated.md 589 B
- learnings.md 349 B
- references/agent-api.md 3.9 KB
- references/api-auth.md 3.3 KB
- references/charts-settings.md 2.0 KB
- references/dashboards.md 6.2 KB
- references/embedding-integration.md 14 KB
- references/metabase-59-surface.md 3.8 KB
- references/metabase-cli.md 2.6 KB
- references/native-query-patterns.md 14 KB
- references/permissions-collections.md 14 KB
- references/remote-sync.md 1.9 KB
- references/reports-cards.md 6.8 KB
- references/tenants-routing.md 1.7 KB
- scripts/metabase_api.py 20 KB runs code
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.
- 10d ago First seen · 259 lines · 42 tokens per session scan A da7d96853b86
data-metabase is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 8d ago), licensed MIT. It adds 42 tokens to every session and 3,730 once invoked, about $0.0002 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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