What problem does it solve?
Writing Airflow DAGs for Managed Service for Apache Airflow (MSAA, formerly Cloud Composer) requires matching the target environment's Airflow version and installed packages, and undeployed DAGs often fail with import errors only discovered 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: Inspect the target MSAA environment with gcloud to retrieve the Airflow image version, installed PyPI packages, and the DAGs GCS bucket before writing code.
- Best-Practice Authoring: 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 or pylint, verify parsing with composer-dev, deploy to the GCS bucket, and confirm via Airflow CLI import-error checks and Cloud Logging queries.
- Use Case: You need to add a new daily ETL DAG to an existing Cloud Composer environment. The Skill first discovers the environment runs Airflow 2 with specific provider packages, then helps you write a compatible DAG, lint it locally, deploy it to the DAGs bucket, and verify it appears with no import errors.
Quick Start
Help me author a new Airflow DAG for my Managed Airflow environment and validate that it parses without import errors.
