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Breaks down how the skill works and what it produces, so you can quickly judge whether it fits your scenario and value.
When creating data pipelines, orchestrating workflows, scheduling batch jobs, designing DAG structures, implementing custom operators/sensors, testing DAGs locally, or setting up Airflow in production.
references/details.md for deeper patterns and worked examplesProduction-grade Airflow DAG code with best-practice structure, testable and deployable pipelines, and guidance avoiding common anti-patterns.
Use Cases
A data engineer needs to build a daily ETL pipeline extracting records and loading to warehouse. Using this skill, they scaffold a DAG with PythonOperator, retries, and scheduling macros. It ensures idempotent tasks and proper dependency wiring, reducing pipeline failures and on-call incidents.
A team migrates cron jobs to Airflow but suffers from zombie tasks and hard-coded dates. The skill provides style-guide rules (no global state, use ds macros, set timeouts) and testing patterns. They refactor to observable, reschedule-mode sensors and gain stable batch orchestration.
Skill Relationships
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Skill File