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K8s Troubleshooter

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ahmedasmar
k8s-troubleshooter

Systematic Kubernetes troubleshooting and incident response. Use this skill whenever the user mentions Kubernetes, K8s, kubectl, pods, containers, or clusters. Triggers include diagnosing CrashLoopBackOff, ImagePullBackOff, OOMKilled, or Pending pods, responding to production incidents, troubleshooting node NotReady or DiskPressure, debugging service connectivity or networking, investigating PVC or storage failures, analyzing performance degradation, checking cluster health, troubleshooting Helm releases, and conducting post-incident reviews.

Overview

Publisherahmedasmar
Repositorydevops-claude-skills
Skill namek8s-troubleshooter
Stars
203
Forks
38
Bundled files
5
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ahmedasmar on GitHub. Read the source before you install it.

Installation

Install the K8s Troubleshooter AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ahmedasmar/devops-claude-skills/tree/main/k8s-troubleshooter/skills
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ahmedasmar/devops-claude-skills.git /tmp/devops-claude-skills
mkdir -p .claude/skills
cp -r /tmp/devops-claude-skills/k8s-troubleshooter/skills .claude/skills/k8s-troubleshooter
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable K8s Troubleshooter in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads K8s Troubleshooter on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so K8s Troubleshooter is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Kubernetes Troubleshooter & Incident Response

Systematic approach to diagnosing and resolving Kubernetes issues in production environments.

Core Troubleshooting Workflow

Follow this systematic approach for any Kubernetes issue:

1. Gather Context

  • What is the observed symptom?
  • When did it start?
  • What changed recently (deployments, config, infrastructure)?
  • What is the scope (single pod, service, node, cluster)?
  • What is the business impact (severity level)?

2. Initial Triage

Run cluster health check:

bash
# Check node status and health
kubectl get nodes

# Find non-running pods across all namespaces
kubectl get pods -A --field-selector status.phase!=Running

# Check node resource usage
kubectl top nodes

This provides an overview of:

  • Node health status
  • Pending and failed pods across all namespaces
  • Node resource utilization

3. Deep Dive Investigation

Based on triage results, focus investigation:

For Namespace-Level Issues:

bash
python3 scripts/check_namespace.py <namespace>

This provides comprehensive namespace health:

  • Pod status (running, pending, failed, crashlooping)
  • Service health and endpoints
  • Deployment availability
  • PVC status
  • Resource quota usage
  • Recent events
  • Actionable recommendations

For Pod Issues:

bash
# Get full pod details (status, events, conditions, resource config)
kubectl describe pod <pod-name> -n <namespace>

# Check current and previous container logs
kubectl logs <pod-name> -n <namespace>
kubectl logs <pod-name> -n <namespace> --previous

# Get events specific to the pod
kubectl get events -n <namespace> --field-selector involvedObject.name=<pod-name>

This reveals:

  • Pod phase and readiness
  • Container statuses and states
  • Restart counts and exit codes
  • Recent events and scheduling decisions
  • Resource requests and limits

For additional investigations:

  • Check all namespace events: kubectl get events -n <namespace> --sort-by='.lastTimestamp'

4. Identify Root Cause

Consult references/common_issues.md for detailed information on:

  • ImagePullBackOff / ErrImagePull
  • CrashLoopBackOff
  • Pending Pods
  • OOMKilled
  • Node issues (NotReady, DiskPressure)
  • Networking failures
  • Storage/PVC issues
  • Resource quotas and throttling
  • RBAC permission errors

Each issue includes:

  • Symptoms
  • Common causes
  • Diagnostic commands
  • Remediation steps
  • Prevention strategies

5. Apply Remediation

Follow remediation steps from common_issues.md based on root cause identified.

Always:

  • Test fixes in non-production first if possible
  • Document actions taken
  • Monitor for effectiveness
  • Have rollback plan ready

6. Verify & Monitor

After applying fix:

  • Verify issue is resolved
  • Monitor for recurrence (15-30 minutes minimum)
  • Check related systems
  • Update documentation

Incident Response

For production incidents, follow structured response in references/incident_response.md:

Severity Assessment:

  • SEV-1 (Critical): Complete outage, data loss, security breach
  • SEV-2 (High): Major degradation, significant user impact
  • SEV-3 (Medium): Minor impairment, workaround available
  • SEV-4 (Low): Cosmetic, minimal impact

Incident Phases:

  1. Detection - Identify and assess
  2. Triage - Determine scope and impact
  3. Investigation - Find root cause
  4. Resolution - Apply fix
  5. Post-Incident - Document and improve

Common Incident Scenarios:

  • Complete cluster outage
  • Service degradation
  • Node failure
  • Storage issues
  • Security incidents

See references/incident_response.md for detailed playbooks.

Quick Reference Commands

Cluster Overview

bash
kubectl cluster-info
kubectl get nodes
kubectl get pods --all-namespaces | grep -v Running
kubectl get events --all-namespaces --sort-by='.lastTimestamp' | tail -20

Pod Diagnostics

bash
kubectl describe pod <pod> -n <namespace>
kubectl logs <pod> -n <namespace>
kubectl logs <pod> -n <namespace> --previous
kubectl exec -it <pod> -n <namespace> -- /bin/sh
kubectl get pod <pod> -n <namespace> -o yaml

Node Diagnostics

bash
kubectl describe node <node>
kubectl top nodes
kubectl top pods --all-namespaces
ssh <node> "systemctl status kubelet"
ssh <node> "journalctl -u kubelet -n 100"

Service & Network

bash
kubectl describe svc <service> -n <namespace>
kubectl get endpoints <service> -n <namespace>
kubectl get networkpolicies --all-namespaces

Storage

bash
kubectl get pvc,pv --all-namespaces
kubectl describe pvc <pvc> -n <namespace>
kubectl get storageclass

Resource & Configuration

bash
kubectl describe resourcequota -n <namespace>
kubectl describe limitrange -n <namespace>
kubectl get rolebindings,clusterrolebindings -n <namespace>

Diagnostic Scripts

check_namespace.py

Namespace-level health check and diagnostics:

  • Pod health (running, pending, failed, crashlooping, image pull errors)
  • Service health and endpoints
  • Deployment availability status
  • PersistentVolumeClaim status
  • Resource quota usage and limits
  • Recent namespace events
  • Health status assessment
  • Actionable recommendations

Usage:

bash
# Human-readable output
python3 scripts/check_namespace.py <namespace>

# JSON output for automation
python3 scripts/check_namespace.py <namespace> --json

# Include more events
python3 scripts/check_namespace.py <namespace> --events 20

Best used when troubleshooting issues in a specific namespace or assessing overall namespace health.

Cluster-Level Diagnostics (kubectl)

For cluster-wide health checks, use kubectl directly:

bash
# Node health and status
kubectl get nodes
kubectl top nodes

# Find non-running pods across all namespaces
kubectl get pods -A --field-selector status.phase!=Running

# System pod health
kubectl get pods -n kube-system

Pod-Level Diagnostics (kubectl)

For detailed pod investigation, use kubectl directly:

bash
# Full pod details (status, events, conditions, resource config)
kubectl describe pod <pod-name> -n <namespace>

# Current and previous container logs
kubectl logs <pod-name> -n <namespace>
kubectl logs <pod-name> -n <namespace> --previous

# Events specific to the pod
kubectl get events -n <namespace> --field-selector involvedObject.name=<pod-name>

Reference Documentation

references/common_issues.md

Comprehensive guide to common Kubernetes issues with:

  • Detailed symptom descriptions
  • Root cause analysis
  • Step-by-step diagnostic procedures
  • Remediation instructions
  • Prevention strategies

Covers:

  • Pod issues (ImagePullBackOff, CrashLoopBackOff, Pending, OOMKilled)
  • Node issues (NotReady, DiskPressure)
  • Networking issues (pod-to-pod communication, service access)
  • Storage issues (PVC pending, volume mount failures)
  • Resource issues (quota exceeded, CPU throttling)
  • Security issues (vulnerabilities, RBAC)

Read this when you identify a specific issue type but need detailed remediation steps.

references/incident_response.md

Structured incident response framework including:

  • Incident response phases (Detection → Triage → Investigation → Resolution → Post-Incident)
  • Severity level definitions
  • Detailed playbooks for common incident scenarios
  • Communication guidelines
  • Post-incident review template
  • Best practices for prevention, preparedness, response, and recovery

Read this when responding to production incidents or planning incident response procedures.

references/performance_troubleshooting.md

Comprehensive performance diagnosis and optimization guide covering:

  • High Latency Issues - API response time, request latency troubleshooting
  • CPU Performance - Throttling detection, profiling, optimization
  • Memory Performance - OOM issues, leak detection, heap profiling
  • Network Performance - Latency, packet loss, DNS resolution
  • Storage I/O Performance - Disk performance testing, optimization
  • Application-Level Metrics - Prometheus integration, distributed tracing
  • Cluster-Wide Performance - Control plane, scheduler, resource utilization

Read this when:

  • Investigating slow application response times
  • Diagnosing CPU or memory performance issues
  • Troubleshooting network latency or connectivity
  • Optimizing storage I/O performance
  • Setting up performance monitoring

references/helm_troubleshooting.md

Complete guide to Helm troubleshooting including:

  • Release Issues - Stuck releases, missing resources, state problems
  • Installation Failures - Chart conflicts, validation errors, template rendering
  • Upgrade and Rollback - Failed upgrades, immutable field errors, rollback procedures
  • Values and Configuration - Values not applied, parsing errors, secret handling
  • Chart Dependencies - Dependency updates, version conflicts, subchart values
  • Hooks and Lifecycle - Hook failures, cleanup issues
  • Repository Issues - Chart access problems, version mismatches

Read this when:

  • Working with Helm-deployed applications
  • Troubleshooting chart installations or upgrades
  • Debugging Helm release states
  • Managing chart dependencies

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the K8s Troubleshooter AI skill do?

Systematic Kubernetes troubleshooting and incident response. Use this skill whenever the user mentions Kubernetes, K8s, kubectl, pods, containers, or clusters. Triggers include diagnosing CrashLoopBackOff, ImagePullBackOff, OOMKilled, or Pending pods, responding to production incidents, troubleshooting node NotReady or DiskPressure, debugging service connectivity or networking, investigating PVC or storage failures, analyzing performance degradation, checking cluster health, troubleshooting Helm releases, and conducting post-incident reviews.

Why use K8s Troubleshooter on TypingMind?

Because you install it once and use it with any model. K8s Troubleshooter is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install K8s Troubleshooter in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ahmedasmar/devops-claude-skills/tree/main/k8s-troubleshooter/skills. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use K8s Troubleshooter?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with K8s Troubleshooter?

As many as you like. As long as a model supports skills, you can use K8s Troubleshooter with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the K8s Troubleshooter AI skill free?

It is published on GitHub by ahmedasmar. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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