Use when you need to troubleshoot Kubernetes cluster issues. Diagnoses pod failures, network problems, resource constraints, and cluster health issues. Provides step-by-step debugging guidance and automated remediation suggestions for EKS, AKS, GKE, and on-premise clusters.
SkillScore91Maintained
Automatic
◆DevOps & SRE pack
Last verified 8/4/2026
What it changes
Passed 19 of 21 test cases with the skill, and 8 of 21 without it.
Without skill36%
With skill91%
Checks it passed that the model alone missed
Sets the cloudProvider parameter to 'all'
Lists Cloud Provider Detection as the initial step
Enterprise-grade automation solution for k8s troubleshoot operations across AWS, Azure, GCP, and on-premise environments to maximize operational efficiency while maintaining security and compliance standards.
When to Use
Pod Failures: When pods are stuck in CrashLoopBackOff, pending, or failing to start
Network Issues: When services can't communicate or have connectivity problems
Resource Problems: When nodes are resource constrained or applications are throttled
Cluster Health: When cluster components are unhealthy or degraded
Performance Issues: When applications are slow or have high latency
Storage Problems: When persistent volumes aren't mounting or have issues
DNS Resolution: When name resolution isn't working within the cluster
Gotchas
Common Pitfalls
Symptom vs Cause: Kubernetes symptoms often don't directly indicate root causes
Resource Requests: Missing or incorrect resource requests can cause scheduling issues
Image Pull Issues: Registry access problems can cause pod startup failures
Namespace Isolation: Issues in one namespace may affect cluster-wide resources
Edge Cases
Cluster Autoscaling: Autoscaling events can cause temporary resource unavailability
Node Drain: Node maintenance can cause pod rescheduling and temporary issues
Certificate Rotation: Certificate rotation can cause temporary connectivity issues
Multi-Tenant Clusters: Resource contention between tenants can cause complex issues
Performance Issues
Cluster Size: Large clusters (>1000 nodes) can have API server latency
Watch Connections: Too many watch connections can overwhelm the API server
Log Volume: High log volume can affect cluster performance and troubleshooting
Resource Metrics: Collecting detailed metrics can impact cluster performance
Security Considerations
Privileged Escalation: Troubleshooting may require elevated permissions
Sensitive Data: Logs and diagnostics may contain sensitive information
Cluster Access: Troubleshooting tools need appropriate RBAC permissions
Audit Logs: All troubleshooting actions should be logged for security compliance