Airflow DAG Patterns
Guides the agent to design, test, and deploy production Apache Airflow data pipelines
- Repository
- sickn33/agentic-awesome-skills
- Stars
- 49,023
- Licence
- MIT
- Files
- 2 files
What it does
Once installed, the agent follows established Apache Airflow practices when you ask it to build data pipelines or scheduled workflows. It plans tasks, dependencies, retries and idempotency, and points to a playbook of patterns, checklists and templates for operators, sensors, local testing and deployment. It is written as guidance rather than an executable tool, so the agent still writes the code and you review it.
Great for
- Data engineers building or reviewing Airflow DAGs
- Teams setting up scheduled batch jobs that need retries, dependencies and monitoring
- Developers debugging failed Airflow runs who want a structured checklist
Skip it if
- Simple scheduled tasks where a cron job or shell script is enough, since the skill itself says Airflow is not needed there
- Changing live production DAG schedules or backfills without review, because mistakes can duplicate or corrupt data
Try asking
- “Design an Airflow DAG that loads daily sales CSVs from S3 into Postgres with retries and alerting”
- “Review my existing Airflow DAG for idempotency and failure handling”
- “Write pytest tests for my Airflow DAG and explain how to run them locally”
- “Create a custom sensor that waits for a file to land before my transform task runs”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 2 original files, the MIT licence and an attribution note.
airflow-dag-patterns.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip airflow-dag-patterns.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip airflow-dag-patterns.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Give the agent your Airflow version, executor and existing DAG code so its suggestions match your environment.
- Ask for a staging validation plan and review any backfill or schedule change before running it in production.
- Point the agent at resources/implementation-playbook.md by name if you want its detailed checklists applied.
What's inside
- resources/implementation-playbook.md
- SKILL.md
Redistributed unmodified from sickn33/agentic-awesome-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.