dbt Transformation Patterns

What it does

This skill gives an AI agent a set of best-practice conventions for dbt, a tool that turns raw database tables into clean, tested analytics tables using SQL. Once installed, the agent organizes models into staging, intermediate and marts layers, applies naming prefixes like stg_, int_, dim_ and fct_, suggests data quality tests and documentation, and recommends incremental models for large tables. It works from written guidance and example project layouts rather than running anything itself.

Try asking

• “Set up a dbt project structure for our Stripe and Shopify data with staging, intermediate and marts folders”Copy
• “Make my fct_orders model incremental because it has over 5 million rows”Copy
• “Suggest tests and column documentation for my customer dimension model”Copy
• “Review my dbt naming conventions and layering against best practices”Copy

Get better results

• Give the agent your existing folder tree and dbt_project.yml so it fits your current conventions rather than the example ones
• Ask for the reasoning behind a layering or materialization choice, and check it against your warehouse's cost and performance
• Review generated SQL and test definitions before merging, and run them in a dev target first, as the skill advises

What's inside

• references/details.md
• SKILL.md