skills.rest
rory-data
rory-dataCOMMUNITY
Explore all skills by this author →
GitHub

debugging-dags

Diagnose failed Airflow DAGs and provide root-cause remediation.

Updated Jan 15, 2026
Search Tags:#debugging#logs#observability#diagnosis#airflow#root-cause#dag
One-click install
npx skills add https://github.com/rory-data/copilot --skill debugging-dags-rory-data
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: debugging-dags
Source: https://github.com/rory-data/copilot/tree/main/skills/debugging-dags
Command: npx skills add https://github.com/rory-data/copilot --skill debugging-dags-rory-data

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This guide provides a structured, end-to-end approach to diagnosing and remediating failed Airflow DAGs, enabling faster root-cause analysis and prevention.

Core Features & Use Cases

  • Systematic failure identification across specific DAG runs or across all DAGs, with built-in health checks and import-error detection.
  • Task-log based error extraction, categorization into data, code, infrastructure, or dependency issues, and actionable remediation plans.
  • Post-diagnosis context gathering and prevention guidance to reduce recurrence in production pipelines.
  • Use Case: When a DAG fails in production, quickly determine whether the issue is data, code, infra, or upstream; then outline concrete fixes.

Quick Start

Provide the dag_id and optional dag_run_id to begin diagnosing a failed Airflow DAG.

Frequently Asked Questions about debugging-dags

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

FAQPage Schema
How do I debug a failed Airflow DAG and find the root cause?▼

To debug a failed Airflow DAG, you need to analyze task logs and system health data to categorize the issue into data, code, infrastructure, or dependency problems, which then yields an actionable remediation plan.

Why does my Airflow DAG run keep failing in production?▼

Airflow DAG runs fail due to data anomalies, code errors, infrastructure bottlenecks, or upstream dependency issues, requiring systematic log extraction and health checks to isolate the specific failure category.

What is the best way to run a health check across multiple Airflow DAGs?▼

Running a health check across multiple Airflow DAGs involves systematically scanning DAG metadata and task logs to detect import errors and categorize failures into data, code, infrastructure, or dependency issues.

Can I diagnose Airflow task log errors automatically to prevent future pipeline failures?▼

You can diagnose Airflow task log errors by extracting and categorizing them, then applying post-diagnosis context gathering to generate prevention guidance that reduces pipeline failure recurrence.

Do I need dag_run_id to diagnose a failed Airflow DAG?▼

You do not need dag_run_id to start; providing the dag_id is sufficient to begin diagnosing a failed Airflow DAG, while including the optional dag_run_id offers more targeted root-cause analysis.

What should I do when Airflow DAG debugging is not working for complex failures?▼

When Airflow DAG debugging is not working for complex failures, apply a structured diagnosis approach that gathers dag metadata, task logs, and system health data to produce root cause, impact, and immediate fix guidance.

Related Skills

Explore More Data & Analytics Skills→
razorpayrazorpay
31

query-dag-failure-debugger

Identifies root buckets for Airflow DAG failures and outputs structured triage with confidence and next steps.

Official
Advanced
zineyuzineyu

trellis-break-loop

Analyze bug fixes to identify root causes and prevention strategies.

Community
Advanced
minyeamerminyeamer
3

airflow

Query and manage Apache Airflow workflows using the af CLI.

Community
Intermediate
DoubleslashSEDoubleslashSE
4

error-patterns

Classify infrastructure errors by severity, category, and pattern type.

Community
Basic