metabase/agent-skills

metabase-react-sdk-setup

First-time setup for the Metabase React SDK — instance detection, API key, dashboard discovery, JWT auth, SDK installation, and initial embedding code.

소스 보기
원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

Use this skill for any task involving @metabase/embedding-sdk-react — whether that's initial setup, embedding dashboards, theming, or plugins.

Communication style: Be concise. Do one step at a time. When asking the user for input, output only the question — do not explain upcoming steps, implementation details, or what you plan to do next. The user does not need a roadmap.

CRITICAL — YOU MUST GET AN API KEY BEFORE DOING ANYTHING ELSE Step 1 asks the user for a Metabase URL and API key. You CANNOT proceed without both. Do NOT detect the Metabase version, fetch llms.txt, install packages, or write ANY code until the user has given you an API key. Do NOT attempt to call any Metabase API endpoint without an API key — it will return 401 and you will be guessing. If any Metabase API call returns 401, STOP everything and ask the user for an API key.

Step 1 — Get the Metabase URL and API key

You need a Metabase instance URL and an admin API key before anything else.

`.env.metabase` is only for admin tasks within this skill (API calls to Metabase). It is NOT the app's runtime config. Never import, read, or reference .env.metabase from the user's application code or build config. The app's instance URL goes in the user's own .env file (e.g., VITE_METABASE_URL, NEXT_PUBLIC_METABASE_URL) — set that up in Step 4.

Check if .env.metabase exists in the project root and already has both METABASE_INSTANCE_URL (non-empty) and METABASE_ADMIN_API_KEY (non-empty). If so, skip to Step 2.

Otherwise, create the file and gitignore it:

bash
grep -qxF '.env.metabase' .gitignore 2>/dev/null || echo '.env.metabase' >> .gitignore
printf 'METABASE_INSTANCE_URL=\nMETABASE_ADMIN_API_KEY=\n' > .env.metabase

Then output only this message — no preamble, no explanation of what comes next, no implementation details:

I created .env.metabase in the project root. Please fill in both values: 1. Set METABASE_INSTANCE_URL to your Metabase URL (e.g. http://localhost:3000) 2. Open {your URL}/admin/settings/authentication/api-keys, create a new API key 3. Set METABASE_ADMIN_API_KEY to that key 4. Let me know when you're done

Do not guess or assume the instance URL. Do not pre-fill localhost:3000. Do not ask the user to paste the key in the chat — it should only go in .env.metabase. Wait for the user to confirm, then proceed to Step 2.

Step 2 — Detect version and discover dashboards

Now that you have an API key, detect the version and find dashboards.

2a — Detect version

bash
source .env.metabase && \
  curl -s "$METABASE_INSTANCE_URL/api/session/properties" \
    -H "X-API-Key: $METABASE_ADMIN_API_KEY" | grep -o '"tag":"[^"]*"'

Parse both the edition and the major version from the tag:

Tag formatEditionExample
v0.X.YOSS (Community)v0.60.1 → major 60, OSS
v1.X.YEnterprise (EE)v1.60.1 → major 60, EE

If major version < 49, tell the user the Embedding SDK requires Metabase 49+ and stop.

If the tag starts with `v1.`, the instance is Enterprise Edition — use full JWT SSO embedding. Do not fall back to guest embedding or any OSS-only auth path.

Remember the major version number — you will need it in Step 3.

2b — Enable the Embedding SDK

Automatically enable the SDK so the user doesn't have to toggle it manually in the admin panel:

bash
source .env.metabase && \
  curl -s -X PUT "$METABASE_INSTANCE_URL/api/setting/enable-embedding-sdk" \
    -H "X-API-Key: $METABASE_ADMIN_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{"value": true}'

If this returns an error (e.g., 403), tell the user to enable it manually at <INSTANCE_URL>/admin/settings/embedding and move on.

Do NOT fetch `llms.txt` yet. You need dashboard IDs first.

2c — Find dashboards and table candidates

Run both of these:

bash
source .env.metabase && \
  curl -s "$METABASE_INSTANCE_URL/api/search?models=dashboard&archived=false" \
    -H "X-API-Key: $METABASE_ADMIN_API_KEY"
bash
source .env.metabase && \
  curl -s "$METABASE_INSTANCE_URL/api/automagic-dashboards/database/1/candidates" \
    -H "X-API-Key: $METABASE_ADMIN_API_KEY"

Filter and prioritize the results:

  • Exclude any dashboards from the "Usage analytics" collection — those are internal Metabase admin dashboards, not user content.
  • Deprioritize anything from the "Sample Database" — prefer the user's own databases and dashboards.
  • Pick the top 5 most relevant to what the user asked for from each category. Do not dump every result.

Format like this:

Existing dashboards: 1. Sales Overview (ID 3) 2. Customer Analysis (ID 7) ... Or I can create a new dashboard from your data: A. Orders table B. Products table ... Which ones should I embed? (e.g. "1 and 3" or "A")

Wait for the user to pick. If they choose a table, generate and save the X-ray dashboard:

bash
source .env.metabase && \
  DASHBOARD=$(curl -s "$METABASE_INSTANCE_URL/api/automagic-dashboards/table/<TABLE_ID>" \
    -H "X-API-Key: $METABASE_ADMIN_API_KEY")

source .env.metabase && \
  curl -s "$METABASE_INSTANCE_URL/api/dashboard/save" \
    -H "X-API-Key: $METABASE_ADMIN_API_KEY" \
    -H "Content-Type: application/json" \
    -d "$DASHBOARD"

The save response contains the persisted dashboard with a real id field. Use these IDs going forward.

Step 3 — Fetch docs, set up auth, and install SDK

You MUST have real dashboard IDs before reaching this step. If you don't, go back to Step 2.

Now fetch the versioned docs index using the major version from Step 2a:

bash
curl -s https://www.metabase.com/docs/v0.<MAJOR>/llms.txt

Fall back to https://www.metabase.com/docs/latest/llms.txt if empty. This contains correct prop names, auth config shapes, SDK install commands, and breaking changes for this version. Do not fetch llms-embedding-full.txt (too large).

3a — Set up JWT SSO authentication (skip if already done)

Follow the auth setup instructions in llms.txt. In particular:

  • Retrieve the JWT signing secret from Metabase: Settings → Admin → Embedding → Embedding secret key
  • Tell the user to save it as METABASE_JWT_SECRET in their server-side environment only (never a browser-accessible env var)
  • Ask which backend framework they are using (Next.js API route, Express, Fastify, etc.) and scaffold a minimal JWT signing endpoint following the pattern in llms.txt

3b — Install the SDK (skip if already installed at correct version)

Check whether @metabase/embedding-sdk-react is already in the user's package.json.

  • If not installed: use the install command from llms.txt (the correct dist-tag matches the instance major version).
  • If already installed: verify the major version matches. Warn on mismatch and offer to update.

Step 4 — Generate embedding code

Use llms.txt as the authoritative reference for all API shapes. Write files directly into the user's project — edit existing files in place rather than creating new ones alongside them.

Code conventions (override anything in the docs)

  • JWT SSO only: API keys grant admin-level access and are not safe for end-user embeds. Use a server-side JWT signing endpoint; MetabaseProvider receives its URL. Never generate apiKey, METABASE_API_KEY, api-key, or x-api-key — not even as a placeholder. Deviate only if the user explicitly asks and acknowledges the security risk.
  • Instance URL from env: VITE_METABASE_URL (Vite), NEXT_PUBLIC_METABASE_URL (Next.js), etc. Never hardcode.
  • Dashboard IDs as inline literals: always hardcode dashboard IDs directly in JSX — e.g. <InteractiveDashboard dashboardId={7} />. Dashboard IDs are not secrets. Never use import.meta.env.VITE_METABASE_DASHBOARD_*, env vars, config objects, parseDashboardId helpers, or any indirection for dashboard IDs. The goal is clean, minimal code the user can instantly understand and tweak.
  • Secrets server-side only: JWT secrets must never appear in browser-accessible env vars or frontend code.

Theming

After generating the embedding code, inspect the user's app for existing styles — look at CSS variables, Tailwind config, or theme files. Set the theme prop on MetabaseProvider to match the app's look and feel. At minimum, align:

  • colors.brand — the app's primary/accent color
  • colors.background — to match the page background so the embed doesn't look like a white box on a dark page (or vice versa)
  • fontFamily — to match the app's font

Refer to llms.txt for the full theme shape. Keep it minimal — only set values that differ from Metabase defaults.

같은 저장소의 Skills

더 많은 Skills

모든 Skills
metabase
커뮤니티

ai-governance-checklist

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.

설치 수
1
GitHub Stars
42
업데이트
8월 26일
metabase
커뮤니티

ai-readiness-checklist

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.

설치 수
1
GitHub Stars
42
업데이트
8월 26일
metabase
커뮤니티

metabase-cli

Drive a Metabase instance from the terminal via the mb CLI. Authenticate with named profiles; inspect databases (list, get, full metadata rollup, schemas, tables in a schema) and trigger manual schema sync / field-values rescan; inspect tables, fields; list/get/create/update/archive cards (questions, models, metrics) and run them as JSON/CSV/XLSX; list/get/create/update dashboards and patch dashcards; list/get/create collections and traverse the hierarchy by id, entityid, or "root"/"trash" (with items and recursive tree); list/get/create/update/archive native query snippets, segments, and measures; author/update/run transforms and schedule transform-jobs; read/update settings; search content (cards, dashboards, collections, transforms, metrics); manage Enterprise workspaces; git-sync to/from a git remote (status, dirty, import, export, branches, stash, add/remove a collection from sync). Use whenever the user wants to interact with a Metabase from the terminal — "log into metabase", "what profiles do I have", "list cards", "run card 42 as CSV", "create a transform", "list dashboards", "move a dashcard", "list collections", "what's in collection 4", "show the collection tree", "list snippets", "create a segment", "archive a measure", "search metabase for X", "spin up a workspace", "import the latest changes", "add a directory to git sync", "set a setting", "what schemas are in this database", "trigger a sync", "rescan field values", or anything hitting mb .

설치 수
1
GitHub Stars
42
업데이트
8월 26일
metabase
커뮤니티

metabase-database-metadata

Understands the Metabase Database Metadata Format — a YAML-based on-disk representation of databases, tables, and fields synced from a Metabase instance. Use when the user needs to read, edit, or understand metadata files produced by @metabase/database-metadata, or when reasoning about a project's schema (columns, types, FK relationships) through the .metadata/databases folder.

설치 수
1
GitHub Stars
42
업데이트
8월 26일