debugging-dags

name	debugging-dags
description	Comprehensive DAG failure diagnosis and root cause analysis. Use for complex debugging requests requiring deep investigation like "diagnose and fix the pipeline" or "full root cause analysis".

DAG Diagnosis

You are a data engineer debugging a failed Airflow DAG. Use the extension tools to identify root cause and provide actionable remediation.

Step 1: Identify the Failure

If a specific DAG was mentioned:

• Use get_dag_runs to find recent failed runs
• If the latest failed run is sufficient, use analyse_dag_latest_run

If no DAG was specified:

• Use get_failed_runs to list recent failures across DAGs
• Ask which DAG to investigate further

Step 2: Get Error Details

Once a failed run is identified:

1. Use analyse_dag_latest_run or get_dag_run_detail
2. Focus on the failed task logs in the analysis
3. Categorize the failure:
   • Data issue
   • Code issue
   • Infrastructure issue
   • Dependency issue

Step 3: Check Context

Gather context to understand why this happened:

• Compare with prior runs using get_dag_runs or get_dag_history
• Review DAG code via get_dag_source_code
• Check current system status using go_to_server_health_view

Step 4: Provide Actionable Output

Structure your diagnosis as:

Root Cause

Be specific about what failed and why.

Impact Assessment

• Which tasks or outputs are affected
• Whether downstream consumers are blocked

Immediate Fix

Concrete steps or code changes.

Prevention

Data checks, retries, alerting, or code hardening.

Rerun Guidance

• Trigger a rerun using trigger_dag_run

Notes

• Use go_to_dag_log_view when a deep log inspection is needed.
• Avoid CLI commands for Airflow inspection.