kubernetes-troubleshooting

Diagnose Kubernetes and OpenShift failures from offline evidence bundles with cited root-cause analysis.

1|Updated Jan 18, 2024
One-click install
npx skills add https://github.com/brockners/ciscolive --skill kubernetes-troubleshooting-brockners
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: kubernetes-troubleshooting
Source: https://github.com/brockners/ciscolive/tree/main/CLUS26/EXP9/skills/kubernetes-troubleshooting
Command: npx skills add https://github.com/brockners/ciscolive --skill kubernetes-troubleshooting-brockners

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Diagnosing Kubernetes incidents from support bundles, must-gather archives, and raw kubectl output is slow and error-prone, often leading to premature conclusions. This Skill applies a disciplined, evidence-driven workflow that separates observed facts from inferences and produces a defensible root-cause analysis. ## Core Features & Use Cases - Evidence Bundle Intake: Inventories tarballs, must-gather data, pod logs, events, manifests, and node artifacts while treating customer data as read-only. - Hypothesis-Driven Diagnosis: Maintains competing hypotheses with confidence tracking across CrashLoopBackOff, ImagePullBackOff, Pending pods, DNS, CNI, ingress, storage, and kubelet issues. - Timeline and Change Correlation: Normalizes timestamps across logs and events to correlate rollouts, Helm releases, and node maintenance with observed symptoms. - Use Case: Given a must-gather archive from an OpenShift cluster where pods are stuck in CrashLoopBackOff, extract the bundle, correlate previous container logs with events and Service/Endpoint state, and receive a cited root-cause report with next actions. ## Quick Start Use the kubernetes-troubleshooting skill to analyze the uploaded support bundle and produce a hypothesis-driven root-cause analysis with cited evidence.

Frequently Asked Questions about kubernetes-troubleshooting

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

FAQPage Schema
How do I troubleshoot a Kubernetes support bundle offline?▼

Extract the bundle into scratch space, inventory logs, events, and kubectl outputs, then run broad keyword searches for errors before forming hypotheses. This Skill guides that workflow and produces a root-cause report with file and line citations.

How to diagnose CrashLoopBackOff from pod logs and events?▼

Check previous container logs, exit codes, termination reasons, restart cadence, probes, config and env references, and dependency readiness. Correlate these with Service, Endpoints, and DNS state before concluding the cause.

Can this analyze OpenShift must-gather data?▼

Yes, the Skill explicitly handles OpenShift must-gather archives along with tarballs, kubectl outputs, Helm and Kustomize files, node data, and CNI/DNS/ingress/storage artifacts. It reads must-gather indexes and collection metadata before raw logs.

Does it run commands against a live cluster?▼

No, it prefers offline analysis of evidence files and read-only tools like rg, tar, jq, and yq. kubectl is used only against offline files unless live-cluster access is explicitly requested and available.

Why does the analysis distinguish facts from inferences?▼

Labeling claims as Fact, Inference, or Unknown prevents premature conclusions from noisy logs. Every important claim must cite a file path, line number, or command section so the root cause is defensible.

What evidence is needed when the diagnosis is inconclusive?▼

The Skill names the missing discriminator, such as cluster-wide events, pod descriptions, previous container logs, Service and Endpoint YAML, node conditions, kubelet logs, CNI logs, or manifests from before and after the change.