warehouse-init

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 |

## Categorical Columns

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

| Table | Column | Value Families |
|-------|--------|----------------|
| {TABLE} | {COLUMN} | `{PREFIX}*` ({VALUE1}, {VALUE2}, ...) |

## 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