airflow-dag-patterns

Generate Apache Airflow DAG patterns with task dependencies and testing examples.

2|Updated Jan 18, 2026

Search Tags:#workflow #data pipeline #orchestration #data engineering #airflow #dag

Ready to run

npx skills add https://github.com/as4584/antigravity-skills --skill airflow-dag-patterns-as4584

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: airflow-dag-patterns
Source: https://github.com/as4584/antigravity-skills/tree/main/agents-wshobson/plugins/data-engineering/skills/airflow-dag-patterns
Command: npx skills add https://github.com/as4584/antigravity-skills --skill airflow-dag-patterns-as4584

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides battle-tested patterns and best practices for building production-ready Apache Airflow Directed Acyclic Graphs (DAGs), ensuring reliable and maintainable data pipeline orchestration.

Core Features & Use Cases

DAG Design: Implements principles like idempotency, atomicity, and observability.
Task Orchestration: Demonstrates various dependency patterns (linear, fan-out, fan-in, complex).
Modern Airflow Features: Includes examples using the TaskFlow API and dynamic DAG generation.
Error Handling & Alerts: Shows how to implement robust failure callbacks and notifications.
Testing: Provides examples for unit testing DAGs and task logic.
Use Case: Develop a complex daily ETL pipeline that extracts data from multiple sources, transforms it, loads it into a data warehouse, and includes robust error alerting and retry mechanisms.

Quick Start

Use the airflow-dag-patterns skill to generate an example Airflow DAG file named example_dag.py in the dags/ directory.

Frequently Asked Questions about airflow-dag-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build production-ready Airflow DAGs with best practices?▼

Production-ready Airflow DAGs require idempotent, atomic, and observable task orchestration. You achieve this by implementing battle-tested DAG design patterns for task dependencies, error handling callbacks, and automated retry mechanisms to ensure reliable data pipeline execution.

What is the best way to structure task dependencies in an Airflow data pipeline?▼

Task dependencies in an Airflow data pipeline should be structured using linear, fan-out, and fan-in patterns. Utilizing the TaskFlow API allows you to define complex task orchestration clearly while maintaining idempotency and atomicity throughout the workflow.

How do I handle errors and alerts in Apache Airflow workflows?▼

Handling errors and alerts in Apache Airflow workflows involves implementing robust failure callbacks and notification mechanisms. These patterns trigger automated alerts and retries during task failures, ensuring your data pipeline remains resilient and observable.

Can I use dynamic DAG generation and the TaskFlow API for complex data engineering?▼

Yes, modern Airflow features like the TaskFlow API and dynamic DAG generation support complex data engineering. These features simplify task orchestration and allow you to programmatically generate workflows for scalable and maintainable data pipeline scheduling.

How do I unit test Airflow DAGs and task logic?▼

Unit testing Airflow DAGs and task logic involves validating your Directed Acyclic Graph structure and task behavior. Using provided testing patterns, you can verify task dependencies and data processing logic before deploying batch jobs to production.

When should I use Airflow sensors in a data pipeline?▼

Airflow sensors should be used in a data pipeline when you need to pause task execution until a specific external condition is met. They are essential for workflow scheduling when waiting for file arrivals or upstream data processing to complete.