/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.
One skill from wshobson-agents.
shell
$ npx -y skills add wshobson/agents --skill airflow-dag-patterns --agent claude-codeInstalls just this skill. Get the whole plugin for auto-invocation.
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How this skill gets triggered: by you, by Claude, or both.
- Fires itselfClaude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/airflow-dag-patterns
Context preview
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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.
Stats
Stars38,171
Forks4,092
LanguagePython
LicenseMIT
Ships with wshobson-agents
SKILL.md
airflow-dag-patterns.SKILL.md
--- name: airflow-dag-patterns 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. ## 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
