managed-airflow-dag-authoring

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, installed packages, and deployment constraints, and mistakes often surface only as import errors after deployment. ## Core Features & Use Cases - Environment Context Discovery: Uses gcloud commands to inspect the target environment's image version, PyPI packages, and DAGs GCS bucket before writing code. - Authoring Best Practices: Enforces idempotency, no top-level code execution, explicit catchup settings, and Airflow Variables/Connections instead of hardcoded credentials. - Local and Remote Validation: Covers ruff/pylint linting, composer-dev local parsing checks, GCS deployment, and Airflow CLI import-error verification. - Use Case: When asked to create a new DAG for a Cloud Composer environment, the Skill first discovers the Airflow version, writes compatible DAG code, then validates it locally and on the target environment. ## Quick Start Ask the assistant to author a new Airflow DAG for your Managed Airflow environment and validate it against the target environment's version constraints.