gcp-managed-airflow-dag-authoring

Name: gcp-managed-airflow-dag-authoring
Availability: InStock
Author: SmileAfterBurn

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

Updated Aug 26, 2026

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

Ready to run

npx skills add https://github.com/SmileAfterBurn/pani-dumka-ai --skill gcp-managed-airflow-dag-authoring-smileafterburn

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Please help me install this Agent Skill.
Skill: gcp-managed-airflow-dag-authoring
Source: https://github.com/SmileAfterBurn/pani-dumka-ai/tree/main/.gemini/skills/gcp-managed-airflow-dag-authoring
Command: npx skills add https://github.com/SmileAfterBurn/pani-dumka-ai --skill gcp-managed-airflow-dag-authoring-smileafterburn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Writing Airflow DAGs for Managed Service for Apache Airflow (formerly Cloud Composer) often fails due to version mismatches, top-level code execution, and missing validation, causing import errors after deployment. ## Core Features & Use Cases - Environment Context Discovery: Inspect target environments with gcloud to determine Airflow version, installed PyPI packages, and the DAGs GCS bucket before writing code. - Authoring Best Practices: Enforce idempotency, avoid top-level code execution, set explicit catchup behavior, and use Airflow Variables and Connections instead of hardcoded credentials. - Local and Remote Validation: Lint with ruff, verify parsing with composer-dev, deploy to GCS, and confirm zero import errors via the Airflow CLI and Cloud Logging. - Use Case: You need to add a new daily ETL DAG to a Cloud Composer environment running Airflow 2. The skill guides you to check the environment image version, write compatible code, validate locally, deploy to the DAGs bucket, and confirm the DAG parses without errors. ## Quick Start Help me author and validate a new Airflow DAG for my Managed Airflow environment, checking the target Airflow version first.

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 validate an Airflow DAG before deploying to Cloud Composer?▼

Validate Airflow DAGs locally by running ruff for linting and using composer-dev to check import errors with the dags list-import-errors command. After deploying to the GCS bucket, run gcloud composer environments run with dags list-import-errors to confirm no parsing failures.

How do I find the Airflow version of a Cloud Composer environment?▼

Run gcloud composer environments describe with the environment name and location, formatting the output on config.softwareConfig.imageVersion. This reveals the exact Airflow and image version so your DAG code matches the target constraints.

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

Airflow 3 introduces breaking changes in imports, operators, and configuration compared to Airflow 2. Check your target environment image version first, then adjust the DAG code to the matching version and lint with Airflow 3 rulesets when targeting that version.

Why does my Airflow DAG cause performance issues during parsing?▼

Performance issues occur when database queries, API calls, or heavy computations run at the top level of the DAG file, since this code executes every few seconds during parsing. Move such logic inside tasks and access variables through Jinja templates.

Can I deploy an Airflow DAG to Cloud Composer without GCP access?▼

Without GCP access you can only work offline by relying on user-provided environment details and validating locally with ruff or composer-dev. Deployment to the GCS DAGs bucket and remote import-error verification require authorized GCP access.