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.
Best use case
airflow-dag-patterns is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using airflow-dag-patterns should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/airflow-dag-patterns/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How airflow-dag-patterns Compares
| Feature / Agent | airflow-dag-patterns | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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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