airflow-dag-analyzer

# Airflow DAG Analyzer Analyzes, validates, and optimizes Apache Airflow DAGs for reliability and performance. ## Overview This skill examines Apache Airflow DAG definitions to identify performance bottlenecks, reliability issues, and best practice violations. It provides recommendations for task dependency optimization, parallelism configuration, error handling, and resource management. ## Capabilities - **DAG structure analysis and validation** - Parse and validate DAG structure - **Task dependency optimization** - Identify bottlenecks and suggest parallel execution - **Parallelism and concurrency recommendations** - Optimize pool and slot allocation - **SLA and timeout configuration** - Recommend appropriate timeouts and SLAs - **Retry and failure handling patterns** - Validate retry logic and alerting - **Sensor optimization** - Smart sensors, deferrable operators, reschedule mode - **Resource pool allocation** - Optimize pool usage and worker distribution - **DAG scheduling optimization** - Catchup, backfill, and schedule interval tuning - **Cross-DAG dependency detection** - Identify external dependencies and triggers ## Input Schema ```json { "dagCode": { "type": "string", "description": "The Python DAG definition code", "required": true }, "dagId": { "type": "string", "description": "The DAG identifier" }, "executionHistory": { "type": "object", "description": "Historical execution metrics", "properties": { "runs"...