Agent skill · software engineering · rmyndharis
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.
Why this skill is useful
Provides specific patterns and best practices for designing and deploying Apache Airflow DAGs that the AI wouldn't reliably generate on its own.
What it needs
About 1k tokens when loaded. Last updated 2026-08-02. 1,255 stars on the source repository.
What this skill does
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.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills rmyndharis/airflow-dag-patterns