/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 agentic-awesome-skills.
shell
$ npx -y skills add sickn33/agentic-awesome-skills --skill airflow-dag-patterns --agent claude-codeInstalls just this skill. Get the whole plugin for auto-invocation.
How it fires
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
The summary Claude sees to decide when to auto-load this skill.
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
Stars43,759
Forks6,464
LanguagePython
LicenseMIT
Ships with agentic-awesome-skills
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." risk: safe source: community date_added: "2026-02-27" --- # 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.
