debugging-dags

Original：🇺🇸 English

Translated

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".

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Sourcenecatiarslan/airflow-vscode-extension

Added on2026-02-07

NPX Install

npx skill4agent add necatiarslan/airflow-vscode-extension debugging-dags

SKILL.md Content

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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:

Use

analyse_dag_latest_run

or

get_dag_run_detail

Focus on the failed task logs in the analysis
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.