debugging-dags

Diagnose Airflow DAG failures and identify root causes across pipelines.

419|61|Updated Jan 13, 2026
One-click install
npx skills add https://github.com/astronomer/agents --skill debugging-dags
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: debugging-dags
Source: https://github.com/astronomer/agents/tree/main/skills/debugging-dags
Command: npx skills add https://github.com/astronomer/agents --skill debugging-dags

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps data engineers diagnose Airflow DAG failures and perform structured root-cause analysis, enabling faster remediation and more reliable pipelines.

Core Features & Use Cases

  • Structured Investigation: Systematically identify failure points using task logs, states, and recent changes.
  • Root Cause & Remediation: Provide actionable root-cause analysis and recommended fixes to prevent recurrence.
  • Use Case: For a DAG with intermittent failures, this Skill traces the failure to upstream tasks and proposes concrete remediation steps.

Quick Start

To begin, provide the failing DAG ID and optional dag_run_id to initiate the diagnosis. Then, ask for a detailed root cause report or remediation plan.

Frequently Asked Questions about debugging-dags

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

FAQPage Schema
How do I diagnose Airflow DAG failures and find the root cause?

To diagnose Airflow DAG failures, this Skill inspects task logs, analyzes task states, and reviews upstream changes to pinpoint the root cause. It delivers a structured investigation workflow that yields actionable remediation steps to prevent future pipeline issues.

What is the best way to debug intermittent Airflow pipeline failures?

The best way to debug intermittent Airflow pipeline failures is to trace them back to upstream tasks using task state analysis and recent change reviews. This identifies the specific intermittent trigger and proposes concrete remediation steps to restore pipeline health.

How do I start root-cause-analysis for a failed DAG run?

To start root-cause-analysis for a failed DAG run, provide the failing DAG ID and optional dag_run_id. The system uses these inputs to initiate a structured investigation of your Airflow logs and task states to generate a detailed remediation plan.

Can I use this for ongoing pipeline health monitoring or only ad-hoc failure investigations?

You can use it for both ongoing pipeline health monitoring and ad-hoc failure investigations. The structured investigation steps apply to continuous task state analysis and log inspection, ensuring repeatable procedures for diagnosing complex Airflow pipelines.

What does pipeline-debugging for complex DAGs involve when failures occur?

Pipeline-debugging for complex DAGs involves log inspection, task state analysis, and upstream change review when failures occur. It systematically identifies failure points and provides recommended fixes to prevent the same pipeline issues from recurring.