FrancoStino/airflow-dag-patterns
bundled-skills/airflow-dag-patterns/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add FrancoStino/opencode-skills-antigravity airflow-dag-patternsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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TrivyContainer and dependency vulnerability scanner
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Last scanned: Apr 15, 2026, 6:22 AM13.3s2 files scanned
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
- Identify data sources, schedules, and dependencies.
- Design idempotent tasks with clear ownership and retries.
- Implement DAGs with observability and alerting hooks.
- 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.mdfor 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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