Skill

airflow-dag-patterns

Builds production Apache Airflow DAGs using best practices for operators, sensors, testing, and deployment. For data pipelines, workflow orchestration, and batch jobs.

Python

data-engineering

From antigravity-bundle-data-engineering

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Tool Access

This skill uses the workspace's default tool permissions.

Supporting Assets

View in Repository

resources/implementation-playbook.md

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Parent Repo Stars30783

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Last CommitMar 29, 2026

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Apache Airflow DAG Patterns

Production-ready patterns for Apache Airflow including DAG design, operators, sensors, testing, and deployment strategies.

Use this skill when

Creating data pipeline orchestration with Airflow
Designing DAG structures and dependencies
Implementing custom operators and sensors
Testing Airflow DAGs locally
Setting up Airflow in production
Debugging failed DAG runs

Do not use this skill when

You only need a simple cron job or shell script
Airflow is not part of the tooling stack
The task is unrelated to workflow orchestration

Instructions

Identify data sources, schedules, and dependencies.
Design idempotent tasks with clear ownership and retries.
Implement DAGs with observability and alerting hooks.
Validate in staging and document operational runbooks.

Refer to resources/implementation-playbook.md for detailed patterns, checklists, and templates.

Safety

Avoid changing production DAG schedules without approval.
Test backfills and retries carefully to prevent data duplication.

Resources

resources/implementation-playbook.md for detailed patterns, checklists, and templates.