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AubreyHan
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gcp-composer-troubleshooting

Diagnose failed Cloud Composer and Airflow pipelines via Cloud Logging and DAG retrieval.

Updated Jun 10, 2026
Search Tags:#troubleshooting#root cause analysis#logging#gcloud#apache airflow#dag failures#cloud composer
One-click install
npx skills add https://github.com/AubreyHan/SKILL_Repo --skill gcp-composer-troubleshooting-aubreyhan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gcp-composer-troubleshooting
Source: https://github.com/AubreyHan/SKILL_Repo/tree/main/gcp-composer-troubleshooting
Command: npx skills add https://github.com/AubreyHan/SKILL_Repo --skill gcp-composer-troubleshooting-aubreyhan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps troubleshoot failed Cloud Composer and Apache Airflow pipelines by tracing errors back to their actual source instead of guessing from symptoms. It is designed for incidents where a DAG, task, or orchestration run has failed and you need a clear root cause analysis.

Core Features & Use Cases

  • Log-driven diagnosis: Uses Composer and Cloud Logging evidence to identify failing DAGs, task errors, and traceback details.
  • Remote code verification: Compares the running environment against the code stored in Cloud Storage so analysis is based on the source of truth.
  • RCA reporting: Produces a concise root cause explanation with the specific failure point, supporting evidence, and a recommended fix.
  • Use case: A scheduled pipeline fails after a recent deployment, and you need to determine whether the issue is caused by an import error, bad task logic, or a mismatch between local and remote DAG code.

Quick Start

Ask the skill to investigate a failed Cloud Composer DAG by providing the environment name, DAG ID, run time, and any error messages you already have.

Frequently Asked Questions about gcp-composer-troubleshooting

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

FAQPage Schema
How do I find the root cause of Cloud Composer DAG failures?▼

Diagnosing failed Cloud Composer pipelines requires tracing errors to their root cause using Cloud Logging analysis and remote DAG retrieval from Cloud Storage, producing evidence-based RCA reports with specific failure points and recommended fixes.

Why does my Apache Airflow task throw an import error after deployment?▼

Apache Airflow task import errors after deployment often indicate a mismatch between local and remote DAG code. Compare the running environment against code stored in Cloud Storage to determine if the issue is an import error, bad task logic, or a code mismatch.

What is the best way to troubleshoot orchestration incidents in Composer environments?▼

Troubleshooting orchestration incidents in Composer environments requires log-driven diagnosis using Cloud Logging to identify failing DAGs and task exceptions, combined with remote DAG retrieval from Cloud Storage for evidence-based root cause analysis reporting.

Can I use gcloud to diagnose task exceptions in Apache Airflow?▼

Yes, gcloud commands can retrieve Cloud Logging entries to diagnose Apache Airflow task exceptions and traceback details. Combine this log analysis with remote DAG retrieval from Cloud Storage to verify the running code and produce evidence-based root cause analysis.

Do I need Cloud Storage access to troubleshoot Cloud Composer pipelines?▼

Yes, Cloud Storage access is required to troubleshoot Cloud Composer pipelines effectively. Remote DAG retrieval from Cloud Storage enables comparison of the running environment against the source of truth, ensuring accurate evidence-based root cause analysis.

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