airflow-dag-patterns skill

by wshobson·wshobson/agents·40k stars·MIT

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 the airflow-dag-patterns skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/wshobson/agents.git /tmp/agents
mkdir -p ~/.claude/skills
cp -r /tmp/agents/plugins/data-engineering/skills/airflow-dag-patterns ~/.claude/skills/airflow-dag-patterns

available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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

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 dependsonpast=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

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