What problem does it solve? Building and maintaining dbt pipelines for BigQuery requires deep knowledge of dbt project structure, profiles configuration, SQL optimization patterns, and BigQuery-specific quirks like BigLake 4-part naming. This Skill guides an agent through the full lifecycle of creating, modifying, troubleshooting, and validating dbt projects without common pitfalls like missing profiles.yml files or unsafe dbt run executions. ## Core Features & Use Cases - dbt Project Scaffolding: Initializes new dbt projects in a dedicated folder with dbt_project.yml, profiles.yml, models, and tests correctly configured for BigQuery. - SQL Optimization & Data Cleaning: Automatically rewrites anti-patterns (e.g., IN subqueries to EXISTS) and applies mandatory data cleaning protocols, with summary sections in every response. - Troubleshooting & Validation: Compiles pipelines with dbt compile, runs dbt test, diagnoses remote Cloud Composer DAG failures via gcloud logging, and never executes dbt run without explicit confirmation. - Use Case: Ask the agent to add a new incremental model joining two BigQuery datasets; it will fetch schemas with bq CLI, write the model with proper ref/source syntax and config block, add unit tests, and compile to verify correctness. ## Quick Start Create a new dbt model that aggregates daily sales from my BigQuery raw_orders table and validate it compiles correctly.
