airflow-dags

What problem does it solve?

It helps you author and debug Apache Airflow pipelines by turning orchestration requirements into correct, production-ready DAG definitions.

Core Features & Use Cases

• DAG Authoring Patterns: define DAGs via the classic context manager, the recommended @dag TaskFlow decorator, or the legacy constructor style.
• TaskFlow-Centric Composition: implement tasks with @task, pass values through XCom automatically, and control XCom unpacking with multiple_outputs=True.
• Production-Oriented Orchestration: configure sensors (poke vs reschedule), TaskGroups, dynamic task mapping (expand/partial), branching and trigger rules, pools, callbacks, and cross-DAG pipelines with TriggerDagRunOperator and ExternalTaskSensor.

Use case example: You need a daily finance pipeline that waits for a landing file, validates and transforms data, loads into a warehouse, runs a data quality check, and fails fast with clear alerting.

Quick Start

Load the airflow-dags skill and ask: "Generate an Airflow DAG using the @dag and @task TaskFlow API that waits for a file, runs ingest/transform/load with a TaskGroup structure, uses mode='reschedule' for the sensor, and applies best practices like catchup=False and max_active_runs=1."