What problem does it solve? Building and maintaining dbt pipelines for BigQuery involves repetitive setup, SQL optimization, schema discovery, and error-prone configuration of profiles and projects. This Skill guides an AI agent through the entire dbt workflow so models are correct, optimized, and safely compiled before execution. ## Core Features & Use Cases - dbt Project Scaffolding: Initializes new dbt projects with dbt_project.yml, profiles.yml, and proper folder structure under a dedicated directory. - SQL Optimization & Data Cleaning: Automatically rewrites inefficient SQL patterns (e.g., IN subqueries to EXISTS) and applies data cleaning protocols to every pipeline. - Validation & Troubleshooting: Compiles models with dbt compile, runs tests, and diagnoses remote orchestration failures via Cloud Logging. - Use Case: Ask the agent to create a new dbt model that transforms raw events from a BigQuery source table into a cleaned, documented, unit-tested staging model with proper ref and source references. ## Quick Start Create a dbt model that aggregates daily revenue from my BigQuery orders table and compile the project to verify it works.
