What is the Airflow DAG Patterns skill?

It is a specialized extension for Claude Code that provides expert guidance and code patterns for building robust, production-ready Apache Airflow workflows.

Does this skill support Airflow 2.0+ features?

Yes, it includes comprehensive patterns for the TaskFlow API, decorators, and modern Airflow 2.x best practices for cleaner and more pythonic DAGs.

Can it help with dynamic DAG creation?

Absolutely, it includes factory patterns for dynamically generating multiple similar DAGs from JSON configurations or Airflow Variables.

How does it handle error management in pipelines?

The skill provides templates for failure callbacks, retries, exponential backoffs, and cleanup tasks using various trigger rules like ALL_DONE or ONE_FAILED.

Is testing included in these Airflow patterns?

Yes, it covers unit testing strategies specifically for DAG loading and task logic to ensure your pipelines are reliable before deployment.

Apache Airflow DAG Patterns

Name: Apache Airflow DAG Patterns
Author: as4584

byas4584

云基础设施

Builds production-grade Apache Airflow DAGs using best practices for operators, sensors, testing, and deployment.

关于

This skill provides Claude with specialized knowledge to design, implement, and optimize Apache Airflow workflows according to industry best practices. It encompasses production-ready patterns including the TaskFlow API, dynamic DAG generation, complex task dependencies, and robust error handling strategies. Whether you are building idempotent data pipelines, implementing custom sensors for external dependencies, or setting up automated unit tests for your DAGs, this skill ensures your orchestration logic remains observable, scalable, and maintainable.

主要功能

Custom sensor development and external dependency management
Implementation of TaskFlow API (Airflow 2.0+) patterns
0 GitHub stars
Dynamic DAG generation and factory patterns
Production-ready error handling and failure callbacks
Standardized DAG testing and validation strategies

使用场景

Designing idempotent ETL/ELT pipelines with incremental loading
Migrating legacy PythonOperator code to modern TaskFlow API structures
Orchestrating complex multi-cloud workflows with cross-DAG dependencies

关于

主要功能

Custom sensor development and external dependency management
Implementation of TaskFlow API (Airflow 2.0+) patterns
0 GitHub stars
Dynamic DAG generation and factory patterns
Production-ready error handling and failure callbacks
Standardized DAG testing and validation strategies

使用场景

Designing idempotent ETL/ELT pipelines with incremental loading
Migrating legacy PythonOperator code to modern TaskFlow API structures
Orchestrating complex multi-cloud workflows with cross-DAG dependencies