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k8s-troubleshoot

Diagnose failing Kubernetes pods, nodes, and workloads from status, events, and logs.

Updated Aug 23, 2026
Search Tags:#troubleshooting#kubernetes#observability#incident response#kubectl#cluster health#pod diagnostics
One-click install
npx skills add https://github.com/spigell/my-agents --skill k8s-troubleshoot-spigell
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: k8s-troubleshoot
Source: https://github.com/spigell/my-agents/tree/main/agents/shared/src/tools/ask_kubernetes_expert/skills/k8s-troubleshoot
Command: npx skills add https://github.com/spigell/my-agents --skill k8s-troubleshoot-spigell

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you debug and troubleshoot Kubernetes pods, nodes, and related workloads when services are failing, containers crash, nodes become unhealthy, or users need clear diagnostic explanations.

Core Features & Use Cases

  • Actionable pod triage: Checks pod status first, then drills into events and conditions to identify why a workload is stuck or unstable.
  • Crash and failure forensics: Inspects current and previous container logs and aligns observed behavior with resource limits and runtime metrics.
  • Workload ecosystem context: Verifies endpoints, reviews network policies, and checks node health signals to connect failures to underlying causes.

Use it when you see symptoms like Pending pods, CrashLoopBackOff, ImagePullBackOff, OOMKilled, or NotReady nodes and you need a structured diagnostic workflow that maps symptoms to the next best kubectl-backed tool calls.

Quick Start

Use the k8s-troubleshoot skill to diagnose why the pod myapp-xyz in namespace prod is stuck with CrashLoopBackOff by analyzing pod status, events, logs (including previous), and related metrics.

Frequently Asked Questions about k8s-troubleshoot

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

FAQPage Schema
How do I troubleshoot a Kubernetes pod stuck in CrashLoopBackOff?▼

Troubleshoot a Kubernetes pod stuck in CrashLoopBackOff by checking pod status, drilling into events and conditions, and inspecting current and previous container logs. This structured diagnostic workflow aligns observed crashes with resource limits and runtime metrics to pinpoint the failure cause.

Why are my Kubernetes pods stuck in Pending status?▼

Kubernetes pods stuck in Pending status often result from node pressure conditions, resource constraints, or scheduling failures. Diagnose the issue by checking pod status first, then reviewing cluster health signals, events, and node conditions to identify the underlying cause preventing scheduling.

How do I diagnose ImagePullBackOff errors in my Kubernetes cluster?▼

Diagnose ImagePullBackOff errors in your Kubernetes cluster by analyzing pod status and events to identify image pull failures. The diagnostic workflow checks workload ecosystem context, including network policies and endpoints, to ensure connectivity to the container registry is properly configured.

Can I debug OOMKilled containers using kubectl diagnostic tools?▼

Debug OOMKilled containers using kubectl diagnostic tools by inspecting current and previous container logs and aligning observed behavior with resource limits and runtime metrics. The workflow verifies node health signals and resource allocation to connect memory failures to their underlying causes.

What is the best way to investigate NotReady nodes in Kubernetes?▼

Investigate NotReady nodes in Kubernetes by checking node health signals and pressure conditions across the cluster. The diagnostic workflow reviews pod status, events, and conditions, then verifies endpoints and network policies to connect node failures to workload impact.

Does this Kubernetes troubleshooting workflow support targeting specific namespaces and label selectors?▼

This Kubernetes troubleshooting workflow supports targeted execution using namespaces, label and field selectors, and context targeting. It applies diagnostic tool ordering across single or multiple clusters to isolate failing workloads within specific environments efficiently.

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