install
source · Clone the upstream repo
git clone https://github.com/ComeOnOliver/skillshub
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/ComeOnOliver/skillshub "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/rohitg00/kubectl-mcp-server/k8s-autoscaling" ~/.claude/skills/comeonoliver-skillshub-k8s-autoscaling && rm -rf "$T"
manifest:
skills/rohitg00/kubectl-mcp-server/k8s-autoscaling/SKILL.mdsource content
Kubernetes Autoscaling
Comprehensive autoscaling using HPA, VPA, and KEDA with kubectl-mcp-server tools.
When to Apply
Use this skill when:
- User mentions: "HPA", "VPA", "KEDA", "autoscale", "scale to zero"
- Operations: configuring autoscaling, checking scaling status
- Keywords: "scale automatically", "event-driven", "right-size"
Priority Rules
| Priority | Rule | Impact | Tools |
|---|---|---|---|
| 1 | Verify metrics-server for HPA | CRITICAL | |
| 2 | Set resource requests before HPA | CRITICAL | |
| 3 | Use KEDA for scale-to-zero | HIGH | |
| 4 | Check VPA recommendations | MEDIUM | |
Quick Reference
| Task | Tool | Example |
|---|---|---|
| List KEDA ScaledObjects | | |
| Get ScaledObject | | |
| List ScaledJobs | | |
| Check KEDA | | |
HPA (Horizontal Pod Autoscaler)
Basic CPU-based scaling:
apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: my-app-hpa spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: my-app minReplicas: 2 maxReplicas: 10 metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 70
Apply and verify:
kubectl_apply(hpa_yaml, namespace) get_hpa(namespace)
VPA (Vertical Pod Autoscaler)
Right-size resource requests:
apiVersion: autoscaling.k8s.io/v1 kind: VerticalPodAutoscaler metadata: name: my-app-vpa spec: targetRef: apiVersion: apps/v1 kind: Deployment name: my-app updatePolicy: updateMode: "Auto"
KEDA (Event-Driven Autoscaling)
Detect KEDA Installation
keda_detect_tool()
List ScaledObjects
keda_scaledobjects_list_tool(namespace) keda_scaledobject_get_tool(name, namespace)
List ScaledJobs
keda_scaledjobs_list_tool(namespace)
Trigger Authentication
keda_triggerauths_list_tool(namespace) keda_triggerauth_get_tool(name, namespace)
KEDA-Managed HPAs
keda_hpa_list_tool(namespace)
See KEDA-TRIGGERS.md for trigger configurations.
Common KEDA Triggers
Queue-Based Scaling (AWS SQS)
apiVersion: keda.sh/v1alpha1 kind: ScaledObject metadata: name: sqs-scaler spec: scaleTargetRef: name: queue-processor minReplicaCount: 0 maxReplicaCount: 100 triggers: - type: aws-sqs-queue metadata: queueURL: https://sqs.region.amazonaws.com/... queueLength: "5"
Cron-Based Scaling
triggers: - type: cron metadata: timezone: America/New_York start: 0 8 * * 1-5 end: 0 18 * * 1-5 desiredReplicas: "10"
Prometheus Metrics
triggers: - type: prometheus metadata: serverAddress: http://prometheus:9090 metricName: http_requests_total query: sum(rate(http_requests_total{app="myapp"}[2m])) threshold: "100"
Scaling Strategies
| Strategy | Tool | Use Case |
|---|---|---|
| CPU/Memory | HPA | Steady traffic patterns |
| Custom metrics | HPA v2 | Business metrics |
| Event-driven | KEDA | Queue processing, cron |
| Vertical | VPA | Right-size requests |
| Scale to zero | KEDA | Cost savings, idle workloads |
Cost-Optimized Autoscaling
Scale to Zero with KEDA
Reduce costs for idle workloads:
keda_scaledobjects_list_tool(namespace)
Right-Size with VPA
Get recommendations and apply:
get_resource_recommendations(namespace)
Troubleshooting
HPA Not Scaling
get_hpa(namespace) get_pod_metrics(name, namespace) describe_pod(name, namespace)
KEDA Not Triggering
keda_scaledobject_get_tool(name, namespace) get_events(namespace)
Common Issues
| Symptom | Check | Resolution |
|---|---|---|
| HPA unknown | Metrics server | Install metrics-server |
| KEDA no scale | Trigger auth | Check TriggerAuthentication |
| VPA not updating | Update mode | Set updateMode: Auto |
| Scale down slow | Stabilization | Adjust stabilizationWindowSeconds |
Best Practices
- Always Set Resource Requests - HPA requires requests to calculate utilization
- Use Multiple Metrics - Combine CPU + custom metrics for accuracy
- Stabilization Windows - Prevent flapping with scaleDown stabilization
- Scale to Zero Carefully - Consider cold start time
Related Skills
- k8s-cost - Cost optimization
- k8s-troubleshoot - Debug scaling issues