gcp-managed-airflow-dag-authoring

Author and validate Apache Airflow DAGs for Google Cloud Managed Service for Apache Airflow environments.

1|Updated Aug 30, 2026

Search Tags:#validation #gcp #workflow orchestration #gcloud #airflow #dag #cloud composer

Ready to run

npx skills add https://github.com/FeexSystems/3WM-SONIK-LABS --skill gcp-managed-airflow-dag-authoring-feexsystems

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Please help me install this Agent Skill.
Skill: gcp-managed-airflow-dag-authoring
Source: https://github.com/FeexSystems/3WM-SONIK-LABS/tree/main/.gemini/skills/gcp-managed-airflow-dag-authoring
Command: npx skills add https://github.com/FeexSystems/3WM-SONIK-LABS --skill gcp-managed-airflow-dag-authoring-feexsystems

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about gcp-managed-airflow-dag-authoring

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I write an Airflow DAG for Google Cloud Composer?▼

First discover the target environment's Airflow version and installed packages using gcloud composer environments describe, then write the DAG following best practices like idempotency and no top-level code execution, and validate it locally or by deploying to the environment's GCS bucket.

How do I validate an Airflow DAG before deploying to Cloud Composer?▼

Run static analysis with ruff or pylint, then use composer-dev to check parsing locally with dags list-import-errors. For remote validation, upload the DAG to the environment's GCS bucket and run gcloud composer environments run with dags list-import-errors.

What is the difference between Airflow 2 and Airflow 3 for DAG authoring?▼

Airflow 3 introduces breaking changes that affect DAG code compatibility, so the target environment's version must be identified first. The skill references a companion migration skill to adjust code for the specific Airflow version in use.

Why does my Airflow DAG fail with import errors after deployment?▼

Import errors commonly come from top-level code execution, missing PyPI packages in the environment, or Airflow version incompatibilities. Check the scheduler logs in Cloud Logging and run dags list-import-errors via the Airflow CLI to identify the cause.

When should I not use this Airflow DAG authoring skill?▼

Do not use it when writing Python code unrelated to Airflow DAGs. It is specifically scoped to authoring and validating DAGs for Managed Service for Apache Airflow environments.