What problem does it solve? Writing correct Dataform pipelines for BigQuery requires deep knowledge of SQLX syntax, incremental table semantics, source declarations, and GCS ingestion patterns. This Skill guides an AI agent through the full lifecycle of creating, modifying, compiling, and validating Dataform projects so pipelines are correct before execution. ## Core Features & Use Cases - Pipeline Generation & Modification: Creates SQLX actions, source declarations, and incremental tables following Dataform best practices, including BigLake Iceberg 4-part naming workarounds. - Automatic Data Cleaning & SQL Optimization: Applies mandatory data-cleaning protocols and BigQuery SQL optimizations to every transformation, with summary sections in each response. - Validation Workflow: Compiles projects with dataform compile, validates with dataform run --dry-run or bq query --dry_run, and never executes a real run without explicit user confirmation. - Use Case: You need to ingest CSV files from a GCS bucket into an existing BigQuery table. The Skill initializes or locates the Dataform repository, creates an external table via an operations file, builds an incremental SQLX action with schema-aligned columns, and validates the whole DAG before you approve execution. ## Quick Start Create a Dataform pipeline that loads CSV files from my GCS bucket into BigQuery and validate it with a dry run.
