warehouse-init

Name: warehouse-init
Availability: InStock
Author: miptah21

Generate a schema reference for a data warehouse with codebase context.

2|Updated Apr 8, 2026

Search Tags:#concept mapping #data observability #metadata discovery #warehouse schema #dbt context #SQL introspection #table documentation

Ready to run

npx skills add https://github.com/miptah21/skills --skill warehouse-init

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: warehouse-init
Source: https://github.com/miptah21/skills/tree/main/.agents/skills/warehouse-init
Command: npx skills add https://github.com/miptah21/skills --skill warehouse-init

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It solves the problem of repeatedly looking up table and column details across a data warehouse by generating a single, editable schema reference that’s ready for quick querying.

Core Features & Use Cases

Warehouse schema discovery: Scans configured databases/schemas to enumerate tables, columns, and row counts so you don’t rely on tribal knowledge.
Codebase-enriched metadata: Searches the repo for dbt/gusty SQL/documentation to attach business descriptions, primary keys, and important columns to the discovered objects.
Concept-to-table lookup support: Produces a .astro/warehouse.md quick reference and optionally integrates it into CLAUDE.md to improve query accuracy for complex analytics.

Quick Start

Run /astronomer-data:warehouse-init to generate .astro/warehouse.md containing all discovered table metadata for fast concept→table lookups.

Frequently Asked Questions about warehouse-init

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate a data warehouse schema reference for quick table lookups?▼

To generate a data warehouse schema reference, scan your configured databases to enumerate tables, columns, and row counts. This process creates a single editable schema file that merges warehouse metadata with repo documentation for instant concept-to-table lookups.

What is warehouse metadata discovery and how does it enrich dbt context?▼

Warehouse metadata discovery scans databases to list tables and columns, then enriches that metadata with dbt context by searching your repository for SQL documentation. This attaches business descriptions and primary keys directly to the discovered warehouse objects.

Can I use SQL introspection to document warehouse schemas without tribal knowledge?▼

Yes, SQL introspection automatically discovers databases, schemas, tables, and row counts to eliminate reliance on tribal knowledge. It flags large tables and optionally pre-populates a concept cache to improve analytics query accuracy.

How do I merge warehouse schema metadata with existing codebase documentation?▼

Merging warehouse schema metadata with codebase documentation involves scanning the repo for dbt or gusty SQL files. This attaches business descriptions, primary keys, and categorical value families to discovered tables, outputting a comprehensive .astro/warehouse.md reference.

What's the best way to map business concepts to data warehouse tables?▼

The best way to map business concepts to warehouse tables is generating a quick reference file like .astro/warehouse.md. This file combines discovered warehouse metadata with codebase context, enabling reliable concept-to-table lookup for complex analytics querying.

Does warehouse schema discovery work for flagging large tables in analytics projects?▼

Yes, warehouse schema discovery works for analytics projects by enumerating row counts and automatically flagging large tables during the scan. This ensures you are aware of massive datasets before running intensive analytics queries.