gcp-managed-airflow-dag-authoring

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: Uses gcloud commands to inspect the target environment's Airflow 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, with Airflow 2 vs 3 compatibility guidance. - Local and Remote Validation: Validates DAGs with ruff or pylint, composer-dev local environments, GCS deployment, and Airflow CLI import-error checks. - Use Case: A data engineer needs to add a new ETL DAG to a Cloud Composer environment running Airflow 2; the skill discovers the environment version, writes compatible DAG code, and verifies it parses without import errors. ## Quick Start Help me author and validate a new Airflow DAG for my Managed Airflow environment named my-env in us-central1.