Does this skill suggest specific code changes?

Yes, it identifies if the failure is code-based and provides the exact changes needed to resolve bugs, handle null values, or manage edge cases.

What information do I need to provide to start debugging?

You can provide a specific DAG ID and Run ID for a targeted investigation, or simply ask for a system health check to find and diagnose recent failures across your entire environment.

How does this differ from standard Airflow log viewing?

While basic tools just show logs, this skill performs structured analysis by comparing historical runs, checking infrastructure context, and providing specific prevention recommendations to avoid future failures.

Can it handle broken DAG files that won't import?

Yes, it uses system health checks to identify import errors and provides specific fixes for syntax or dependency issues preventing the DAG from loading.

Airflow DAG Diagnosis & Root Cause Analysis

Name: Airflow DAG Diagnosis & Root Cause Analysis
Author: astronomer

byastronomer

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分析与监控

Diagnoses complex Airflow DAG failures and provides structured root cause analysis with actionable remediation steps.

关于

This skill empowers data engineers to perform deep-dive investigations into failed Apache Airflow pipelines by systematically analyzing task logs, identifying historical patterns, and categorizing issues across data, code, infrastructure, and dependencies. It moves beyond simple error reporting to provide a comprehensive post-mortem, including impact assessments, immediate SQL or code fixes, and long-term prevention strategies to improve pipeline reliability and minimize downtime.

主要功能

Automated failure identification and system health monitoring
Structured remediation plans with ready-to-use CLI retry commands
Contextual analysis including git history and data volume spikes
Intelligent log analysis to isolate exceptions from Airflow boilerplate
77 GitHub stars
Multi-dimensional error categorization across data, code, and infra

使用场景

Performing deep-dive root cause analysis for persistent pipeline failures
Investigating data quality issues that cause downstream task crashes
Diagnosing infrastructure-related connection timeouts or resource exhaustion

关于

主要功能

Automated failure identification and system health monitoring
Structured remediation plans with ready-to-use CLI retry commands
Contextual analysis including git history and data volume spikes
Intelligent log analysis to isolate exceptions from Airflow boilerplate
77 GitHub stars
Multi-dimensional error categorization across data, code, and infra

使用场景

Performing deep-dive root cause analysis for persistent pipeline failures
Investigating data quality issues that cause downstream task crashes
Diagnosing infrastructure-related connection timeouts or resource exhaustion