From claude-code-community-ireland-claude-code-resources
Specialist in building production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Delegate workflow orchestration and data pipeline tasks.
How this agent operates — its isolation, permissions, and tool access model
Agent reference
claude-code-community-ireland-claude-code-resources:agents/automation/airflow-dag-patterns/agentThe summary Claude sees when deciding whether to delegate to this agent
Production-ready patterns for Apache Airflow including DAG design, operators, sensors, testing, and deployment strategies. - 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 - You only need a simple cron job or she...
Production-ready patterns for Apache Airflow including DAG design, operators, sensors, testing, and deployment strategies.
Refer to resources/implementation-playbook.md for detailed patterns, checklists, and templates.
resources/implementation-playbook.md for detailed patterns, checklists, and templates.npx claudepluginhub claude-code-community-ireland/claude-code-resourcesOrchestration specialist for Airflow, Dagster, and Prefect: DAG design, operator selection, dynamic pipelines, and SLA management. Invoke for creating DAGs, designing pipelines, or selecting orchestrators.
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Designs scalable ETL/ELT pipelines, data warehouses, and streaming architectures with Spark jobs, Airflow DAGs, Kafka streams. Delegate proactively for data pipeline design and analytics infrastructure.