kubernetes-troubleshooting_skill

This skill helps you debug Kubernetes issues quickly by guiding you through pod failures, resource constraints, and networking problems with actionable steps.

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Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill nik-kale/sre-skills --skill kubernetes-troubleshooting

SKILL.md8.7 KB

Overview

This skill provides systematic debugging workflows for common Kubernetes problems like pod failures, resource exhaustion, image pull errors, and networking issues. It gives a concise set of diagnostic commands, targeted troubleshooting steps, and practical fixes to accelerate incident resolution. Use it to triage CrashLoopBackOff, OOMKilled, ImagePullBackOff, readiness failures, scheduling problems, and service connectivity issues.

How this skill works

The skill walks through a layered diagnostic approach: cluster-level checks, pod inspection, logs and exit codes, probe validation, and resource analysis. It maps common symptoms to likely causes and prescribes concrete kubectl commands and configuration fixes (resources, probes, imagePullSecrets, node affinity/taints). It also includes quick commands for interactive debugging and network/DNS tests from within the cluster.

When to use it

Pod stuck in CrashLoopBackOff or continuously restarting
Pods terminated with OOMKilled or unexpected exit codes
ImagePullBackOff or ErrImagePull errors
Pod stuck Pending or unschedulable due to resource/taint/affinity issues
Service unreachable, DNS failures, or endpoints not matching selectors
Cluster resource pressure or PVC/PV provisioning problems

Best practices

Start with kubectl get pods -A and kubectl get events -n <ns> to capture recent failures
Examine current and previous container logs (kubectl logs --previous) and describe output for events and exit codes
Validate readiness/liveness probes and increase initialDelay/timeout before changing app code
Use resource requests/limits conservatively and profile memory/cpu with kubectl top before raising limits
Create and reference imagePullSecrets for private registries; verify image tags and registry reachability
Debug networking by exec-ing into a debug pod and testing DNS and service endpoints

Example use cases

Triage a CrashLoopBackOff: check kubectl logs, previous logs, and container Last State to find startup errors or OOMs
Resolve ImagePullBackOff: verify image name/tag, check imagePullSecrets, and confirm registry connectivity
Fix Pending pods: inspect node capacity, taints, and pod affinity/requests to decide whether to add nodes or adjust requests
Diagnose service reachability: confirm service selector matches pod labels, check endpoints, and test from a debug pod
Investigate OOMKilled: run kubectl top, increase memory limits if appropriate, and profile application memory usage

FAQ

Start with kubectl get pods -n <ns>, kubectl describe pod <pod> -n <ns>, and kubectl logs <pod> -n <ns> (add --previous if restarting). Then check events with kubectl get events -n <ns> --sort-by='.lastTimestamp'.

How do I know if an OOMKilled was caused by the app or limits?

Check the pod describe output for OOM messages and the container exit code 137. Use kubectl top pod <pod> -n <ns> --containers to observe peak memory. If usage exceeds the limit, increase limits or profile the app for leaks.