Generate a schema reference for a data warehouse with codebase context.
npx skills add https://github.com/miptah21/skills --skill warehouse-init
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
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.
.astro/warehouse.md quick reference and optionally integrates it into CLAUDE.md to improve query accuracy for complex analytics.Run /astronomer-data:warehouse-init to generate .astro/warehouse.md containing all discovered table metadata for fast concept→table lookups.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
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.
AradotsoIngest warehouse metadata, dbt docs, LookML, and wiki content into a semantic context layer.
astronomerGenerate a version-controlled .astro/warehouse.md with per-table metadata from warehouse system catalogs.
nrakowAutomate UDF versus dbt macro decisions for SQL transformations.
fcakyonQuery Supabase databases to list tables, inspect schemas, and explore relationships.