smithery/rohitg00

k8s-cost

Optimize Kubernetes costs through resource right-sizing, unused resource detection, and cluster efficiency analysis. Use for cost optimization, resource analysis, and capacity planning.

Installation

$ npx skills add smithery/rohitg00 --skill k8s-cost

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Skill metadata

Parsed from SKILL.md frontmatter.

Version1.0.0
LicenseApache-2.0
More metadata
author
rohitg00
version
1.0.0
tools
8
category
observability

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 4,209 B
  • docs SUMMARY.md 201 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Kubernetes Cost Optimization

Cost analysis and optimization using kubectl-mcp-server's cost tools.

When to Apply

Use this skill when:

  • User mentions: "cost", "savings", "optimize", "expensive", "budget"
  • Operations: cost analysis, right-sizing, cleanup unused resources
  • Keywords: "how much", "reduce", "efficiency", "waste", "overprovisioned"

Priority Rules

Priority Rule Impact Tools
1 Find and delete unused PVCs CRITICAL findorphanedpvcs
2 Right-size overprovisioned pods HIGH getresourcerecommendations
3 Identify idle LoadBalancers HIGH get_services
4 Scale down non-prod off-hours MEDIUM scale_deployment
5 Consolidate small namespaces LOW Analysis

Quick Reference

Task Tool Example
Namespace cost getnamespacecost getnamespacecost(namespace)
Cluster cost getclustercost getclustercost()
Unused PVCs findorphanedpvcs findorphanedpvcs(namespace)
Right-sizing getresourcerecommendations getresourcerecommendations(namespace)

Quick Cost Analysis

Get Cost Summary

get_namespace_cost(namespace)
get_cluster_cost()

Find Unused Resources

find_unused_resources(namespace)
find_orphaned_pvcs(namespace)

Resource Right-Sizing

get_resource_recommendations(namespace)
get_pod_metrics(name, namespace)

Cost Optimization Workflow

1. Identify Overprovisioned Resources

get_resource_recommendations(namespace="production")

get_pod_metrics(name, namespace)
get_resource_usage(namespace)

2. Find Idle Resources

find_orphaned_pvcs(namespace)

find_unused_resources(namespace)

3. Analyze Node Utilization

get_nodes()
get_node_metrics()

Right-Sizing Guidelines

Current State Recommendation
CPU usage < 10% of request Reduce request by 50%
CPU usage > 80% of request Increase request by 25%
Memory < 50% of request Reduce request
Memory near limit Increase limit, monitor OOM

Cost by Resource Type

Compute (Pods/Deployments)

get_resource_usage(namespace)
get_pod_metrics(name, namespace)

Storage (PVCs)

get_pvc(namespace)
find_orphaned_pvcs(namespace)

Network (LoadBalancers)

get_services(namespace)

Multi-Cluster Cost Analysis

Compare costs across clusters:

get_cluster_cost(context="production")
get_cluster_cost(context="staging")
get_cluster_cost(context="development")

Cost Reduction Actions

Immediate Wins

  1. Delete unused PVCs: findorphanedpvcs() then delete
  2. Right-size pods: Apply getresourcerecommendations()
  3. Scale down dev/staging: Off-hours scaling

Medium-term Optimizations

  1. Use Spot/Preemptible nodes: For fault-tolerant workloads
  2. Implement HPA: Auto-scale based on demand
  3. Use KEDA: Scale to zero for event-driven workloads

Long-term Strategy

  1. Reserved instances: For stable production workloads
  2. Multi-tenant clusters: Consolidate small clusters
  3. Right-size node pools: Match workload requirements

Automated Analysis Script

For comprehensive cost analysis, see [scripts/find-overprovisioned.py](scripts/find-overprovisioned.py).

KEDA for Cost Savings

Scale to zero with KEDA:

keda_scaledobjects_list_tool(namespace)
keda_scaledobject_get_tool(name, namespace)

KEDA reduces costs by:

  • Scaling pods to 0 when idle
  • Event-driven scaling (queue depth, etc.)
  • Cron-based scaling for predictable patterns

Related Skills

  • [k8s-autoscaling](../k8s-autoscaling/SKILL.md) - HPA, VPA, KEDA
  • [k8s-troubleshoot](../k8s-troubleshoot/SKILL.md) - Resource debugging