init

name	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 "/data:init" or asks to set up data discovery.
hooks	{"PreToolUse":[{"matcher":"Bash","hooks":[{"type":"command","command":"uv run ${CLAUDE_PLUGIN_ROOT}/skills/analyzing-data/scripts/cli.py ensure","once":true}]}],"Stop":[{"hooks":[{"type":"command","command":"uv run ${CLAUDE_PLUGIN_ROOT}/skills/analyzing-data/scripts/cli.py stop"}]}]}

Initialize Warehouse Schema

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

Scripts: $CLAUDE_PLUGIN_ROOT/skills/analyzing-data/scripts/

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/ai/config/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/ai/config/warehouse.md (if --global flag)

Output Format

# Warehouse Schema

> Generated by `/data: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 /data: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
/data:init	Generate .astro/warehouse.md
/data:init --refresh	Regenerate, preserving user edits
/data:init --database HQ	Only discover specific database
/data:init --global	Write to ~/.astro/ai/config/ instead

Step 7: Pre-populate Cache

After generating warehouse.md, populate the concept cache:

# 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

Step 8: Offer CLAUDE.md Integration (Ask User)

Ask the user:

Would you like to add the Quick Reference table to your CLAUDE.md file?

This ensures the schema mappings are always in context for data queries, improving accuracy from ~25% to ~100% for complex queries.

Options:

1. Yes, add to CLAUDE.md (Recommended) - Append Quick Reference section
2. No, skip - Use warehouse.md and cache only

If user chooses Yes:

1. Check if .claude/CLAUDE.md or CLAUDE.md exists
2. If exists, append the Quick Reference section (avoid duplicates)
3. If not exists, create .claude/CLAUDE.md with just the Quick Reference

Quick Reference section to add:

## 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 `/data:init`. Run `/data:init --refresh` to update.

If yes: Append the Quick Reference section to .claude/CLAUDE.md or CLAUDE.md.

After Generation

Tell the user:

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 /data:init --refresh when schema changes

Refresh Behavior

When --refresh is specified:

1. Read existing warehouse.md
2. Preserve all HTML comments (<!-- ... -->)
3. Preserve Quick Reference table entries (user-added)
4. Preserve user-added descriptions
5. Update row counts and add new tables
6. Mark removed tables with <!-- REMOVED --> comment

Cache Staleness & Schema Drift

The runtime cache has a 7-day TTL by default. After 7 days, cached entries expire and will be re-discovered on next use.

When to Refresh

Run /data:init --refresh when:

• Schema changes: Tables added, renamed, or removed
• Column changes: New columns added or types changed
• After deployments: If your data pipeline deploys schema migrations
• Weekly: As a good practice, even if no known changes

Signs of Stale Cache

Watch for these indicators:

• Queries fail with "table not found" errors
• Results seem wrong or outdated
• New tables aren't being discovered

Manual Cache Reset

If you suspect cache issues:

# 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

Codebase Patterns Recognized

Pattern	Source	What We Extract
**/models/**/*.yml	dbt	table/column descriptions, tests
**/dags/**/*.sql	gusty	YAML frontmatter (description, primary_key)
AGENTS.md, CLAUDE.md	docs	data layer hierarchy, conventions
**/docs/**/*.md	docs	business context

Example Session

User: /data: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 /data:init --refresh when schema changes