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 configuration, and unvalidated DAGs often fail with import errors only after deployment. This Skill guides environment discovery, version-aware authoring, and local and remote validation so DAGs parse correctly before and after deployment. ## Core Features & Use Cases - Environment Context Discovery: Uses gcloud commands to inspect the target environment's Airflow image version, installed PyPI packages, and DAGs GCS bucket before writing code. - Best-Practice Authoring: Enforces 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: Runs ruff or pylint linting, composer-dev local parsing checks, GCS deployment, and Airflow CLI import-error verification plus Cloud Logging monitoring. - Use Case: When asked to create a new scheduled data pipeline DAG for a Cloud Composer environment, the Skill first discovers the environment's Airflow version, writes compatible DAG code, then deploys it to the DAGs bucket and confirms zero import errors. ## Quick Start Ask the assistant to create and validate a new Airflow DAG for your Managed Airflow environment, providing the environment name and region if available.
