debugging-dags

Diagnose failed Apache Airflow DAG runs and classify failure types.

3|Updated Jul 22, 2025

Search Tags:#root cause analysis #airflow #etl monitoring #log triage #dag debugging #workflow remediation #pipeline prevention

Ready to run

npx skills add https://github.com/minyeamer/linkmerce --skill debugging-dags-minyeamer

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

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.

Core Features & Use Cases

Systematic failure identification: locate the specific DAG/task/run using Airflow CLI health, stats, and error views.
Exception-focused log triage: extract the underlying exception from task logs and classify the failure into data, code, infrastructure, or dependency problems.
Context-driven prevention plan: compare the failed run to recent successes, review changes and data volume, and produce remediation plus prevention steps that include actionable commands.

Quick Start

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.

Frequently Asked Questions about debugging-dags

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I find the root cause of a failed Apache Airflow DAG run?▼

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.

What is the best way to triage Airflow task logs for ETL monitoring?▼

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.

How do I prevent recurring Airflow pipeline breakages after remediation?▼

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.

Does DAG debugging provide ready-to-use Airflow CLI commands for workflow remediation?▼

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.

Can I diagnose complex pipeline breakages across specific dag_id and dag_run_id values?▼

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.