cat ~/.astro/agents/warehouse.yml

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}
    """
)

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"
        """
    )

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)"

# 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
 

# Scripts are relative to ../analyzing-data/
uv run cli.py concept import -p .astro/warehouse.md
uv run cli.py concept learn customers HQ.MART_CUST.CURRENT_ASTRO_CUSTS -k ACCT_ID

## Data Warehouse Quick Reference
 
When querying the warehouse, use these table mappings:
 
| Concept | Table | Key Column | Date Column |
|---------|-------|------------|-------------|
{rows from warehouse.md Quick Reference}
 
**Large tables (always filter by date):** {list tables with >100M rows}
 
> Auto-generated by `/astronomer-data:warehouse-init`. Run `/astronomer-data:warehouse-init --refresh` to update.

Generated .astro/warehouse.md
 
Summary:
  - {N} databases, {N} schemas, {N} tables
  - {N} tables enriched with code descriptions
  - {N} concepts cached for instant lookup
 
Next steps:
  1. Edit .astro/warehouse.md to add business context
  2. Commit to version control
  3. Run /astronomer-data:warehouse-init --refresh when schema changes

# Scripts are relative to ../analyzing-data/
uv run scripts/cli.py cache status
uv run scripts/cli.py cache clear --stale-only
uv run scripts/cli.py cache clear

User: /astronomer-data:warehouse-init
 
Agent:
→ Reading warehouse configuration...
→ Found 1 warehouse with databases: HQ, PRODUCT
 
→ Searching codebase for data documentation...
  Found: AGENTS.md with data layer hierarchy
  Found: 45 SQL files with YAML frontmatter in dags/declarative/
 
→ Launching parallel warehouse discovery...
  [Database: HQ] Discovering schemas...
  [Database: PRODUCT] Discovering schemas...
 
→ HQ: Found 29 schemas, 401 tables
→ PRODUCT: Found 1 schema, 0 tables
 
→ Merging warehouse metadata with code context...
  Enriched 45 tables with descriptions from code
 
→ Generated .astro/warehouse.md
 
Summary:
  - 2 databases
  - 30 schemas
  - 401 tables
  - 45 tables enriched with code descriptions
  - 8 large tables flagged (>100M rows)
 
Next steps:
  1. Review .astro/warehouse.md
  2. Add concept mappings to Quick Reference
  3. Commit to version control
  4. Run /astronomer-data:warehouse-init --refresh when schema changes