What problem does it solve?
This Skill helps analytics engineers and data teams build, modify, and validate dbt models using software engineering discipline so transformations are correct, well-tested, and safe for downstream consumers, reducing costly data incidents and rework.
Core Features & Use Cases
- Model construction & refactoring: Plan model outputs, mock SQL, and implement transformations using {{ ref }} and {{ source }} to avoid hardcoded table names.
- Data discovery & validation: Use dbt show to inspect source and model rows, profile columns, and prevent modeling errors before committing changes.
- Testing and debugging: Add high-value dbt tests, review run_results.json and dbt logs, and follow structured debugging steps for parsing, compilation, and data issues.
- Impact analysis & cost controls: Assess downstream dependencies, use selective builds (--select, state:modified+), and apply limits/deferral to minimize warehouse costs.
- Use case: Onboard to an unfamiliar project, discover relevant source tables, plan and implement a staging model with tests, and validate results with dbt show and targeted dbt build.
Quick Start
Audit and implement a new dbt model by planning the desired output, discovering source columns with dbt show, writing SQL using ref() and source(), adding focused tests, and validating results with dbt show and a targeted dbt build.
