airflow-dag-patterns
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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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/wshobson/agents --skill airflow-dag-patterns- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads airflow-dag-patterns automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
# 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
# dags/example_dag.py
from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.operators.empty import EmptyOperator
default_args = {
'owner': 'data-team',
'depends_on_past': False,
'email_on_failure': True,
'email_on_retry': False,
'retries': 3,
'retry_delay': timedelta(minutes=5),
'retry_exponential_backoff': True,
'max_retry_delay': timedelta(hours=1),
}
with DAG(
dag_id='example_etl',
default_args=default_args,
description='Example ETL pipeline',
schedule='0 6 * * *', # Daily at 6 AM
start_date=datetime(2024, 1, 1),
catchup=False,
tags=['etl', 'example'],
max_active_runs=1,
) as dag:
start = EmptyOperator(task_id='start')
def extract_data(**context):
execution_date = context['ds']
# Extract logic here
return {'records': 1000}
extract = PythonOperator(
task_id='extract',
python_callable=extract_data,
)
end = EmptyOperator(task_id='end')
start >> extract >> end
Detailed patterns and worked examples
Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.
Best Practices
Do's
- Use TaskFlow API - Cleaner code, automatic XCom
- Set timeouts - Prevent zombie tasks
- Use
mode='reschedule'- For sensors, free up workers - Test DAGs - Unit tests and integration tests
- Idempotent tasks - Safe to retry
Don'ts
- Don't use
depends_on_past=True- Creates bottlenecks - Don't hardcode dates - Use
{{ ds }}macros - Don't use global state - Tasks should be stateless
- Don't skip catchup blindly - Understand implications
- Don't put heavy logic in DAG file - Import from modules
FAQ
What does the airflow-dag-patterns skill do?
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.
How do I install airflow-dag-patterns?
Run npx skills add https://github.com/wshobson/agents --skill airflow-dag-patterns in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/airflow-dag-patterns/ (personal) or .claude/skills/airflow-dag-patterns/ (project).
Which AI agents can use airflow-dag-patterns?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is airflow-dag-patterns safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed; Runlayer passed; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains airflow-dag-patterns?
It is published by wshobson in wshobson/agents. It has 11,050 recorded installs.
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