airflow-dag-patterns is agent-read markdown (skill) from rudycity/superagent: 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..
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What the file says
# Apache Airflow DAG Patterns
Production-ready patterns for Apache Airflow including DAG design, operators, sensors, testing, and deployment strategies.
## When to Use This Skill
- 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
## Core Concepts
### 1. DAG Design Principles
| Principle | Description |
| --------------- | ----------------------------------- |
| **Idempotent** | Running twice produces same result |
| **Atomic** | Tasks succeed or fail completely |
| **Incremental** | Process only new/changed data |
| **Observable** | Logs, metrics, alerts at every step |
### 2. Task Dependencies
```python
# Linear
task1 >> task2 >> task3
# Fan-out
task1 >> [task2, task3, task4]
# Fan-in
[task1, task2, task3] >> task4
# Complex
task1 >> task2 >> task4
task1 >> task3 >> task4
```
## Quick Start
```python
# dags/example_dag.py
from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.python import PythonOperator
…
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Source
GitHub
rudycity/superagent · 23 stars · license MIT · pushed 2026-09-22 · branch main
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