Diagnose failed Apache Airflow DAG runs and classify failure types.
npx skills add https://github.com/minyeamer/linkmerce --skill debugging-dags-minyeamer
Please help me install this Agent Skill. Skill: debugging-dags Source: https://github.com/minyeamer/linkmerce/tree/main/.agents/skills/debugging-dags Command: npx skills add https://github.com/minyeamer/linkmerce --skill debugging-dags-minyeamer
It helps you diagnose why an Apache Airflow DAG fails and identify the real root cause so you can apply a targeted fix and stop the same failure from recurring.
Ask the AI to run a full root-cause diagnosis for the specific failing DAG run by providing the dag_id and dag_run_id.
High-intent search queries and answers about installing and using this skill.
Diagnosing a failed Apache Airflow DAG run involves locating the failing DAG, extracting the underlying exception from task logs, and classifying the failure type across data, code, infrastructure, or dependency problems to pinpoint the root cause.
Triaging Airflow task logs for ETL monitoring requires extracting the specific underlying exception from the logs and classifying the failure type to determine if the issue stems from data, code, infrastructure, or dependency problems.
Preventing recurring Airflow pipeline breakages involves comparing the failed run to recent successes, reviewing data volume and code changes, and applying context-driven prevention recommendations alongside immediate remediation steps.
Yes, DAG debugging provides ready-to-use Airflow CLI commands as part of its workflow remediation output, helping you execute immediate fixes and prevention steps directly on the failing pipeline infrastructure.
Yes, you can diagnose complex pipeline breakages across specific dag_id and dag_run_id values by running an end-to-end investigation that assesses impact and identifies root causes across data, code, infrastructure, and dependencies.
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