metabase-database-metadata

分类: 数据库与数据存储 | 上传者: metabase | 下载: 0 | 版本: v1.0（最新）
了解 Metabase 数据库元数据格式 — 从 Metabase 实例同步的数据库、表和字段的基于 YAML 的磁盘表示形式。当用户需要读取、编辑或理解由“@metabase/database-metadata”生成的元数据文件时，或者通过“.metabase/databases”文件夹推理项目的架构（列、类型、FK 关系）时使用。
更新日志: Source: GitHub https://github.com/metabase/agent-skills
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当前层级: tree/main/skills/metabase-database-metadata/
📄 README.md 851 B
📄 SKILL.md 7.1 KB
📄 spec.md 12.1 KB
SKILL.md

---
name: metabase-database-metadata
description: 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 `.metabase/databases` folder.
model: opus
allowed-tools: Read, Write, Edit, Glob, Grep, Bash, AskUserQuestion
---

## Metabase Database Metadata Format

Metabase represents database metadata — synced databases, their tables, and their fields — as a tree of YAML files. Files are **diff-friendly**: numeric IDs are omitted entirely, and foreign keys use natural-key tuples like `["Sample Database", "PUBLIC", "ORDERS"]` instead of database identifiers.

The format is defined by a specification bundled alongside this file as `spec.md` (upstream source: [metabase/database-metadata](https://github.com/metabase/database-metadata)). The same project ships a CLI (`@metabase/database-metadata` on npm) that converts the raw JSON from `GET /api/database/metadata` into the YAML tree described by the spec.

## Canonical layout

All metadata for a project lives under a top-level `.metabase/` directory:

- **`.metabase/databases/`** — the YAML tree. **This is the canonical source for the agent.** Read these files to understand the schema, columns, types, and FK relationships.
- **`.metabase/metadata.json`** — the raw API response. Potentially multi-megabyte (or multi-gigabyte) JSON with flat `databases` / `tables` / `fields` arrays. **Never open, grep, or pass it to tools.** It exists only as input to the extractor.

The `.metabase/` directory and the `.env` file described below should both be gitignored. On large warehouses the extracted metadata can reach gigabytes — committing it would make the repo painful or unusable.

## First-time setup

Do not run any of the steps below proactively at session start. Only run them when the user **explicitly asks** to fetch metadata, set up the workflow, or requests something that plainly requires knowledge of the database schema (e.g. "write a query against ORDERS", "describe what tables exist").

When setup is triggered:

### 1. Ensure a `.env` file with credentials

Check whether `.env` exists at the repo root and contains both `METABASE_URL` and `METABASE_API_KEY`.

- If `.env` is missing:
  - If `.env.template` exists, ask the user to copy it and fill in the values.
  - If neither exists, create `.env.template` with placeholders and ask the user to create `.env` from it:
    ```env
    METABASE_URL=https://metabase.example.com
    METABASE_API_KEY=
    ```
- If `.env` exists but is missing one of the required variables, ask the user to add it.

Do not invent, guess, or hardcode credentials. Always ask.

### 2. Ensure `.env` and `.metabase/` are gitignored

Read the repo's `.gitignore` and confirm both `.env` and `.metabase/` are listed. If either is missing, **ask the user before modifying `.gitignore`** — e.g.:

> `.env` and `.metabase/` are not in `.gitignore`. Committing them would leak credentials or bloat the repo (metadata can be gigabytes). Shall I add them?

Only edit `.gitignore` after the user confirms.

### 3. Fetch and extract

Once `.env` is valid and ignore rules are in place:

```sh
set -a; source .env; set +a

mkdir -p .metabase
curl -sf "$METABASE_URL/api/database/metadata" \
  -H "X-API-Key: $METABASE_API_KEY" \
  -o .metabase/metadata.json

rm -rf .metabase/databases
npx @metabase/database-metadata extract-metadata .metabase/metadata.json .metabase/databases
```

Then read the YAML tree under `.metabase/databases/` to answer the user's question.

## Session start behaviour

At the start of a session, do not run any fetch commands. Just observe what's on disk:

- If `.metabase/metadata.json` **and** `.metabase/databases/` both exist, **assume the tree is sufficiently up to date** and use it directly. Do not refetch.
- If the tree is missing or only partial, do nothing until the user asks for something that needs it — then fall into the first-time-setup flow above.

If something in the tree looks stale or inconsistent while you're using it, mention it to the user and let them decide whether to refetch. Never refresh silently.

## Refreshing (user-initiated only)

If the user explicitly asks to refresh metadata, re-run step 3 from first-time setup. Always remove `.metabase/databases` before re-extracting so stale files are not left behind.

## Entities

Three entity types, two file types:

| Entity | File | Description |
|--------|------|-------------|
| **Database** | `.metabase/databases/{db}/{db}.yaml` | A connected data source (Postgres, MySQL, BigQuery, etc.). Identified by name. |
| **Table** | `.metabase/databases/{db}/schemas/{schema}/tables/{table}.yaml` (or `.../tables/{table}.yaml` for schemaless DBs) | A physical table or view. Contains a `fields` array with all its columns nested inline. |
| **Field** | (nested inside a Table YAML, no separate file) | A column. Includes `base_type`, `database_type`, and optionally `effective_type`, `semantic_type`, `coercion_strategy`, `parent_id`, `fk_target_field_id`. |

## Foreign keys

Foreign keys use natural-key tuples, not numeric IDs:

- **Database FK**: the database name (string) — e.g. `"Sample Database"`
- **Table FK**: `[database, schema_or_null, table]` — e.g. `["Sample Database", "PUBLIC", "ORDERS"]`
- **Field FK**: `[database, schema_or_null, table, field, ...nested_field_names]` — e.g. `["Sample Database", "PUBLIC", "EVENTS", "DATA", "user", "name"]` for a JSON-unfolded column `DATA.user.name`

Field-level FKs show up as `parent_id` (nested field parent) and `fk_target_field_id` (referenced PK for FK columns).

## Type attributes on fields

- **`database_type`** — the raw native type string from the driver (`BIGINT`, `VARCHAR`, `JSONB`, etc.). Database-specific.
- **`base_type`** — the Metabase type matching the native type (`type/BigInteger`, `type/Text`, `type/Structured`, etc.).
- **`effective_type`** — the type Metabase treats the column as at query time. Only emitted when it differs from `base_type` (i.e. coercion is configured).
- **`coercion_strategy`** — the rule producing `effective_type` from `base_type` (e.g. `Coercion/ISO8601->DateTime`, `Coercion/UNIXMilliSeconds->DateTime`).
- **`semantic_type`** — business-domain label (`type/PK`, `type/FK`, `type/Email`, `type/Category`, `type/Latitude`, etc.). Drives UI and some analytical behavior.

See the extracted spec for the full type hierarchy and available coercion strategies.

## Reading the spec

This skill ships with a local snapshot of the spec as `spec.md`, alongside `SKILL.md`.

**Read it on demand, not eagerly.** Open `spec.md` only when you actually need detail beyond what `SKILL.md` summarizes — e.g. the full base-type / semantic-type hierarchy, the complete list of coercion strategies, or the exact folder-path rules. Do not open it at session start, and do not open it for tasks unrelated to the metadata tree.

If the bundled copy looks out of date with the upstream package, the skill's own `README.md` documents how to refresh it with `extract-spec`.
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metabase-database-metadata

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