Agent skill

dbt

Use when building dbt models, adding tests, or designing data models. Covers dimensional modeling, model organization (staging/intermediate/marts), testing patterns, and warehouse-specific configurations.

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Install this agent skill to your Project

npx add-skill https://github.com/bbrewington/software-data-and-ai-tools/tree/main/claude-skills/dbt

SKILL.md

dbt

Comprehensive patterns for dbt data transformation projects.

Skill Modules

This skill is organized into the following modules. Read the relevant module(s) based on the task:

Module File Use When
Data Modeling data-modeling.md Designing dimensional models, defining grain, facts vs dimensions, SCDs
Model Layers model-layers.md Organizing staging/intermediate/marts, naming conventions, project structure
Testing Overview testing.md Understanding test types, contracts vs tests, test strategy, running tests
Generic Tests testing-generic.md Built-in tests, dbt-utils, dbt-expectations, custom generic tests
Unit Tests testing-unit.md Testing transformation logic with mocked data (dbt 1.8+)
Singular Tests testing-singular.md Complex business rules, cross-model validation
Package Testing testing-packages.md Integration tests for dbt package development

Quick Reference

Commands

bash
dbt run                          # Run all models
dbt run --select +model_name     # Model + upstream
dbt run --select model_name+     # Model + downstream
dbt run --select tag:daily       # Models with tag
dbt test                         # Run all tests
dbt test --select "test_type:unit"      # Unit tests only
dbt test --select "test_type:data"      # Data tests only
dbt build                        # Run + test in DAG order
dbt build --select state:modified+      # Modified + downstream (CI)
dbt docs generate                # Generate documentation
dbt source freshness             # Check source freshness

Naming Conventions

Layer Pattern Example
Staging stg__<source>__<table> stg__stripe__customers
Intermediate int_<entity>__<transform> int_customers__enriched
Fact fct_<event> fct_orders
Dimension dim_<entity> dim_customers

Column Naming

Type Convention Examples
IDs <entity>_id customer_id, order_id
Timestamps <event>_at created_at, updated_at, shipped_at
Dates <event>_date order_date, birth_date
Booleans is_<state> or has_<thing> is_active, has_subscription
Amounts <name>_<unit> amount_usd, quantity_units
Counts <thing>_count order_count, item_count

Testing Pyramid

          Integration Tests  ← Package development only
              Unit Tests     ← Complex transformation logic
           Singular Tests    ← Business rules
           Generic Tests     ← Keys, constraints, patterns
         Source Freshness    ← Pipeline health
             Contracts       ← Schema enforcement

What to Test (Quick Guide)

Always Sometimes Rarely
Primary keys (unique + not_null) Optional FKs Every column not_null
Required foreign keys Numeric ranges Columns in contracts
Business-critical derived fields String patterns Staging columns
Status/enum columns Row count bounds Intermediate models

Best Practices Summary

Do

  • Define grain for every model (what does one row represent?)
  • Use staging for renaming/casting only—no business logic
  • Test primary keys on every model (unique + not_null)
  • Use contracts for marts (enforce schema at build time)
  • Add tests when you find bugs (regression prevention)
  • Document models and columns in YAML
  • Use incremental models for large tables
  • Tag critical tests and run them more frequently

Don't

  • Skip the staging layer
  • Put business logic in staging models
  • Hardcode values (use {{ var() }} or {{ env_var() }})
  • Test every column (diminishing returns)
  • Ignore test warnings indefinitely
  • Forget source freshness checks
  • Create circular dependencies between models

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