/warehouse-init

name: warehouse-init
description: Initialize warehouse schema discovery. Generates .astro/warehouse.md with all table metadata for instant lookups. Run once per project, refresh when schema changes. Use when user says "/astronomer-data:warehouse-init" or asks to set up data discovery.

Initialize Warehouse Schema

Generate a comprehensive, user-editable schema reference file for the data warehouse.

**Scripts:** `../analyzing-data/scripts/` — All CLI commands below are relative to the `analyzing-data` skill's directory. Before running any `scripts/cli.py` command, `cd` to `../analyzing-data/` relative to this file.

What This Does

1. Discovers all databases, schemas, tables, and columns from the warehouse 2. **Enriches with codebase context** (dbt models, gusty SQL, schema docs) 3. Records row counts and identifies large tables 4. Generates `.astro/warehouse.md` - a version-controllable, team-shareable reference 5. Enables instant concept→table lookups without warehouse queries

Process

Step 1: Read Warehouse Configuration

cat ~/.astro/agents/warehouse.yml

Get the list of databases to discover (e.g., `databases: [HQ, ANALYTICS, RAW]`).

Step 2: Search Codebase for Context (Parallel)

**Launch a subagent to find business context in code:**

Task(
    subagent_type="Explore",
    prompt="""
    Search for data model documentation in the codebase:

    1. dbt models: **/models/**/*.yml, **/schema.yml
       - Extract table descriptions, column descriptions
       - Note primary keys and tests

    2. Gusty/declarative SQL: **/dags/**/*.sql with YAML frontmatter
       - Parse frontmatter for: description, primary_key, tests
       - Note schema mappings

    3. AGENTS.md or CLAUDE.md files with data layer documentation

    Return a mapping of:
      table_name -> {description, primary_key, important_columns, layer}
    """
)

Step 3: Parallel Warehouse Discovery

**Launch one subagent per database** using the Task tool:

For each database in configured_databases:
    Task(
        subagent_type="general-purpose",
        prompt="""
        Discover all metadata for database {DATABASE}.

        Use the CLI to run SQL queries:
        # Scripts are relative to ../analyzing-data/
        uv run scripts/cli.py exec "df = run_sql('...')"
        uv run scripts/cli.py exec "print(df)"

        1. Query schemas:
           SELECT SCHEMA_NAME FROM {DATABASE}.INFORMATION_SCHEMA.SCHEMATA

        2. Query tables with row counts:
           SELECT TABLE_SCHEMA, TABLE_NAME, ROW_COUNT, COMMENT
           FROM {DATABASE}.INFORMATION_SCHEMA.TABLES
           ORDER BY TABLE_SCHEMA, TABLE_NAME

        3. For important schemas (MODEL_*, METRICS_*, MART_*), query columns:
           SELECT TABLE_NAME, COLUMN_NAME, DATA_TYPE, COMMENT
           FROM {DATABASE}.INFORMATION_SCHEMA.COLUMNS
           WHERE TABLE_SCHEMA = 'X'

        Return a structured summary:
        - Database name
        - List of schemas with table counts
        - For each table: name, row_count, key columns
        - Flag any tables with >100M rows as "large"
        """
    )

**Run all subagents in parallel** (single message with multiple Task calls).

Step 4: Discover Categorical Value Families

For key categorical columns (like OPERATOR, STATUS, TYPE, FEATURE), discover value families:

uv run cli.py exec "df = run_sql('''
SELECT DISTINCT column_name, COUNT(*) as occurrences
FROM table
WHERE column_name IS NOT NULL
GROUP BY column_name
ORDER BY occurrences DESC
LIMIT 50
''')"
uv run cli.py exec "print(df)"

Group related values into families by common prefix/suffix (e.g., `Export*` for ExportCSV, ExportJSON, ExportParquet).

Step 5: Merge Results

Combine warehouse metadata + codebase context:

1. **Quick Reference table** - concept → table mappings (pre-populated from code if found) 2. **Categorical Columns** - value families for key filter columns 3. **Database sections** - one per database 4. **Schema subsections** - tables grouped by schema 5. **Table details** - columns, row counts, **descriptions from code**, warnings

Step 6: Generate warehouse.md

Write the file to:

• `.astro/warehouse.md` (default - project-specific, version-controllable)
• `~/.astro/agents/warehouse.md` (if `--global` flag)

Output Format

# Warehouse Schema

> Generated by `/astronomer-data:warehouse-init` on {DATE}. Edit freely to add business context.

## Quick Reference

| Concept | Table | Key Column | Date Column |
|---------|-------|------------|-------------|
| customers | HQ.MODEL_ASTRO.ORGANIZATIONS | ORG_ID | CREATED_AT |
<!-- Add your concept mappings here -->

## Categorical Columns

When filtering on these columns, explore value families first (values often have variants):

| Table | Column | Value Families |
|-------|--------|----------------|
| {TABLE} | {COLUMN} | `{PREFIX}*` ({VALUE1}, {VALUE2}, ...) |
<!-- Populated by /astronomer-data:warehouse-init from actual warehouse data -->

## Data Layer Hierarchy

Query downstream first: `reporting` > `mart_*` > `metric_*` > `model_*` > `IN_*`

| Layer | Prefix | Purpose |
|-------|--------|---------|
| Reporting | `reporting.*` | Dashboard-optimized |
| Mart | `mart_*` | Combined analytics |
| Metric | `metric_*` | KPIs at various grains |
| Model | `model_*` | Cleansed sources of truth |
| Raw | `IN_*` | Source data - avoid |

## {DATABASE} Database

### {SCHEMA} Schema

#### {TABLE_NAME}
{DESCRIPTION from code if found}

| Column | Type | Description |
|--------|------|-------------|
| COL1 | VARCHAR | {from code or inferred} |

- **Rows:** {ROW_COUNT}
- **Key column:** {PRIMARY_KEY from code or inferred}
{IF ROW_COUNT > 100M: - **⚠️ WARNING:** Large table - always add date filters}

## Relationships

{Inferred relationships based on column names like *_ID}

Command Options

| Option | Effect | |--------|--------| | `/astronomer-data:warehouse-init` | Generate .astro/warehouse.m