dbt Model Spec Skill

A dbt model is only trustworthy if its grain is unambiguous, its sources are declared, and it's tested. This skill specs a model the way a good analytics engineer would — naming the grain first, mapping lineage, defining each column, choosing the right materialization, and writing the dbt tests that keep it correct — so the model is reviewable before a line of SQL ships.

Required Inputs

Ask for these only if they aren't already provided:

• What the model represents and its grain (one row per ___ — the single most important decision).
• Layer — staging, intermediate, or mart (dimension/fact). Conventions differ per layer.
• Sources / upstream refs — the raw tables or models it builds on.
• The business logic — joins, filters, aggregations, and any business rules.

Output Format

dbt Model: [model_name]