What problem does it solve? Writing Airflow DAGs for Google Cloud's Managed Service for Apache Airflow (formerly Cloud Composer) requires matching the target environment's Airflow version, installed packages, and deployment constraints, and unvalidated DAGs often fail with import errors after deployment. ## Core Features & Use Cases - Environment Context Discovery: Inspect the target Composer environment with gcloud to determine the Airflow image version, installed PyPI packages, and DAGs GCS bucket before writing code. - Authoring Best Practices: Enforce idempotent tasks, no top-level code execution, explicit catchup settings, and Airflow Variables/Connections instead of hardcoded credentials, with Airflow 2 vs 3 compatibility guidance. - Local and Remote Validation: Lint with ruff, verify parsing via composer-dev, deploy to the GCS bucket, and confirm no import errors through the Airflow CLI and Cloud Logging. - Use Case: When asked to create a new scheduled data pipeline DAG, the skill discovers the target environment runs Airflow 2, writes compatible DAG code, and validates it parses without import errors before finishing. ## Quick Start Ask the AI to create and validate an Airflow DAG for your Cloud Composer environment, providing the environment name and region.
