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. It is especially useful for teams working in multi. 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.
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.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "airflow-dag-patterns" skill to help with this workflow task. Context: 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.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
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.
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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.
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