Individual skill
debugging-dags
Comprehensive DAG failure diagnosis and root-cause analysis with structured investigation and prevention recommendations. Use when deep failure investigation is needed, a DAG fails to import/parse or 'airflow dags list' errors on a file; a task or run is failing and must be diagnosed and fixed; requests like 'why did X fail', 'my dag keeps failing — find and fix it', or fixing a broken DAG so it loads cleanly. For simple 'why did it fail / show logs', the airflow skill handles it directly.
skills/debugging-dags/SKILL.md
Source commit 35b9610bb2710c37ca11326c4cbf7818b6725fec
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- Last checked
- Nov 6, 2026
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- Repository last updated
- Nov 2, 2026
- License
- Apache-2.0
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- agents Repository topic
- ai Repository topic
- airflow Repository topic
- data-engineering Repository topic
- mcp Repository topic
- skills Repository topic
- claude Repository topic
- cursor Repository topic
- apache-airflow Repository topic
- agentic-workflow Repository topic
- testing Derived category
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# 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.
## Running the CLI
These commands assume `af` is on PATH. Run via `astro otto` to get it automatically, or install standalone with `uv tool install astro-airflow-mcp`.
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## Step 1: Identify the Failure
If a specific DAG was mentioned:
- Run `af runs diagnose <dag_id> <dag_run_id>` (if run_id is provided)