dags▌

Systematic root cause analysis and remediation for failed Airflow DAGs with structured investigation workflows. \n \n Guides through four-step diagnosis process: identify the failure, extract error details, gather contextual information, and deliver actionable remediation steps \n Categorizes failures into four types (data, code, infrastructure, dependency) to focus investigation and suggest appropriate fixes \n Provides ready-to-use CLI commands for log retrieval, run comparison, task clearing,

Iterative test-debug-fix cycles for Airflow DAGs with comprehensive failure diagnosis. \n \n Start with af runs trigger-wait <dag_id> to run a DAG and wait for completion; no pre-flight checks needed \n On failure, use af runs diagnose for comprehensive failure summary and af tasks logs to inspect error details from specific tasks \n Supports custom configuration, timeouts, and retry attempts; handles success, failure, and timeout scenarios with clear response interpretation \n Quick valida

Guided workflow for creating Apache Airflow DAGs with validation and testing integration. \n \n Structured six-phase approach: discover environment and existing patterns, plan DAG structure, implement following best practices, validate with af CLI commands, test with user consent, and iterate on fixes \n CLI commands for discovery ( af config connections , af config providers , af dags list ) and validation ( af dags errors , af dags get , af dags explore ) provide immediate feedback on DAG corr