debugging-dags

# DAG Diagnosis

You are a data engineer debugging a failed Airflow DAG. Follow this systematic approach to identify the root cause and provide actionable remediation.

## Step 1: Identify the Failure

If a specific DAG was mentioned:
- Use `diagnose_dag_run` with the dag_id and dag_run_id (if provided)
- If no run_id specified, use `get_dag_stats` to find recent failures

If no DAG was specified:
- Use `get_system_health` to find recent failures across all DAGs
- List any import errors (broken DAG files)
- Show DAGs with recent failures
- Ask which DAG to investigate further

## Step 2: Get the Error Details

Once you have identified a failed task:

1. **Get task logs** using `get_task_logs` with the dag_id, dag_run_id, and task_id
2. **Look for the actual exception** - scroll past the Airflow boilerplate to find the real error
3. **Categorize the failure type**:
   - **Data issue**: Missing data, schema change, null values, constraint violation
   - **Code issue**: Bug, syntax error, import failure, type error
   - **Infrastructure issue**: Connection timeout, resource exhaustion, permission denied
   - **Dependency issue**: Upstream failure, external API down, rate limiting

## Step 3: Check Context

Gather additional context to understand WHY this happened:

1. **Recent changes**: Was there a code deploy? Check git history if available
2. **Data volume**: Did data volume spike? Run a quick count on source tables
3. **Upstream health**: Did upstream tasks succeed but produce unexpected data?
4. **Historical pattern**: Is this a recurring failure? Check if same task failed before
5. **Timing**: Did this fail at an unusual time? (resource contention, maintenance windows)

Use `get_dag_run` to compare the failed run against recent successful runs.

## Step 4: Provide Actionable Output

Structure your diagnosis as:

### Root Cause
What actually broke? Be specific - not "the task failed" but "the task failed because column X was null in 15% of rows when the code expec