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volcano-job-diagnose

Diagnose Volcano batch job statuses and issues using kubectl.

225|28|Updated Mar 2, 2026
Search Tags:#devops#kubernetes#diagnostics#sre#volcano#batch job
One-click install
npx skills add https://github.com/scitix/siclaw --skill volcano-job-diagnose
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: volcano-job-diagnose
Source: https://github.com/scitix/siclaw/tree/main/skills/core/volcano-job-diagnose
Command: npx skills add https://github.com/scitix/siclaw --skill volcano-job-diagnose

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps SREs and DevOps engineers quickly identify and understand the root causes of failures or unexpected behavior in Volcano batch jobs, reducing Mean Time To Resolution (MTTR).

Core Features & Use Cases

  • Job Status Overview: Provides a summary of the Volcano Job's phase, task counts (pending, running, succeeded, failed), and configured policies.
  • Task & Pod Analysis: Lists individual task statuses and, in verbose mode, details about associated Kubernetes pods.
  • PodGroup Association: Verifies the link between the Job and its PodGroup, crucial for gang scheduling.
  • Event Log Review: Displays recent Kubernetes events related to the job for debugging.
  • Use Case: When a critical training job in a Kubernetes cluster using Volcano fails, this Skill can be invoked to rapidly diagnose whether the issue lies with job configuration, resource allocation, pod scheduling, or task execution policies.

Quick Start

Diagnose the Volcano Job named 'my-training-job' in the 'training' namespace.

Frequently Asked Questions about volcano-job-diagnose

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

FAQPage Schema
How do I diagnose a pending Volcano batch job in Kubernetes?▼

To diagnose a pending Volcano batch job, examine the job phase, task statuses, PodGroup associations, and Kubernetes events to identify scheduling bottlenecks or resource allocation failures.

Why does my Volcano job have partial scheduling or repeated restarts?▼

Partial scheduling or repeated restarts in Volcano jobs often stem from gang scheduling mismatches, PodGroup issues, or task execution policies, which can be detected by reviewing Kubernetes events and task statuses.

Can I troubleshoot Volcano job failures using kubectl?▼

Yes, you can troubleshoot Volcano job failures using kubectl to perform read-only queries of job phases, task counts, PodGroup links, and Kubernetes events, providing a structured diagnosis of execution issues.

What is the best way to identify root causes of Volcano batch job failures?▼

The best way to identify root causes of Volcano batch job failures is to systematically analyze job configuration, resource allocation, pod scheduling, and task execution policies using Kubernetes events and PodGroup associations.

Does Volcano job diagnostics require write access to the Kubernetes cluster?▼

No, Volcano job diagnostics requires only read-only access to query Kubernetes resources, ensuring that investigating batch job execution issues does not modify the cluster state.

When should I check PodGroup associations for Volcano job issues?▼

You should check PodGroup associations when experiencing unexpected job failures or gang scheduling delays, as verifying the link between the Volcano Job and its PodGroup is crucial for debugging task execution.

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