---
name: airflow-dag-patterns-e9ed96182486
description: Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
---

# 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

1. Identify data sources, schedules, and dependencies.
2. Design idempotent tasks with clear ownership and retries.
3. Implement DAGs with observability and alerting hooks.
4. 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.


---
Source attribution: rmyndharis
Original project: https://github.com/rmyndharis/antigravity-skills/blob/364ea63c190fcf7cac035f7fa91f0406fa392584/skills/airflow-dag-patterns/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Yudhi Armyndharis

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: rmyndharis
Original project: https://github.com/rmyndharis/antigravity-skills/blob/364ea63c190fcf7cac035f7fa91f0406fa392584/skills/airflow-dag-patterns/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering
Roles: Backend Developer, Data Analyst, Data Engineer, DevOps / SRE, ML / AI Engineer
Specialties: API, Backend, Cross Platform
Tools: Apache Airflow
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 102d9805e220c57cac0ca46ac577f886026e83a5075a1638d3721a6739fcf46e