Debugging Dags

When your Airflow pipeline breaks at 3am, you need more than just error logs. This walks you through a structured four-step investigation: identify the failure with `af runs diagnose`, pull task logs to find the actual exception buried under Airflow boilerplate, check context like recent deploys or data volume spikes, then output a diagnosis with root cause, impact assessment, and concrete remediation steps. It's designed for deep investigations when something is genuinely broken and you need to understand why, not just surface-level log checking. The systematic approach helps you avoid the usual debugging trap of jumping straight to fixes without understanding what actually went wrong or how to prevent it next time.

skills.sh installs: 1.2K
GitHub stars: 459
last updated: Aug 21, 2026

View on GitHub

Install

$ npx -y skills add astronomer/agents --skill debugging-dags --agent claude-code

Installs into .claude/skills of the current project.

Files

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.