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airflow-dag-patterns

Build Apache Airflow data pipelines with clear task dependencies and schedules. This skill guides workflow design, retries, monitoring, local testing, and production deployment, with patterns for investigating failed runs.

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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.

sickn33/agentic-awesome-skills
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이럴 때 유용합니다

  • Designing data pipelines and task dependencies
  • Scheduling recurring batch jobs
  • Testing Airflow workflows locally
  • Investigating failed workflow runs

포함된 파일(2)

  • SKILL.md1.8 KB
  • resources/implementation-playbook.md13.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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.
  4. Validate in staging and document operational runbooks.

Refer to resources/implementation-playbook.md for detailed patterns, checklists, and templates.

Safety

  • Avoid changing production DAG schedules without approval.
  • Test backfills and retries carefully to prevent data duplication.

Resources

  • resources/implementation-playbook.md for detailed patterns, checklists, and templates.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

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