From vanguard-frontier-agentic
Produces Kubernetes pod CPU/memory rightsizing recommendations from user-supplied p50/p95/p99 utilization metrics with headroom, savings estimates, and Karpenter eligibility flags.
How this skill is triggered — by the user, by Claude, or both
Slash command
/vanguard-frontier-agentic:rightsize-recommendationThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Produce CPU and memory rightsizing recommendations for Kubernetes pods based on user-supplied utilization percentile metrics. All math is performed on the inputs provided by the caller; no cluster connection or live metric fetch is performed.
Produce CPU and memory rightsizing recommendations for Kubernetes pods based on user-supplied utilization percentile metrics. All math is performed on the inputs provided by the caller; no cluster connection or live metric fetch is performed.
No kubectl. No WebFetch. No cluster credentials accepted.
Use this skill when:
inference — computed from caller-supplied inputs using the documented methodologyassumed — derived from a default assumption where caller data was missing (state the assumption)excluded — cost savings figure excluded because unit price was not supplied by the callerBilledCost (current), EffectiveCost (projected after rightsizing), ChargeCategory = Usage, ServiceCategory = Containers.The caller must supply, per pod or workload:
no-limit if unsetIf any field is missing, ask one clarifying question per gap. Do not fabricate metric values.
| Output | Formula | Rationale |
|---|---|---|
| Recommended CPU request | p95 CPU + 20% | p95 covers normal burst; 20% headroom absorbs measurement noise and brief spikes |
| Recommended CPU limit | p99 CPU + 30% | p99 covers rare spikes; 30% headroom prevents throttling during outlier bursts |
| Output | Formula | Rationale |
|---|---|---|
| Recommended memory request | p95 memory + 20% | Same headroom logic as CPU; memory pressure leads to OOMKill rather than throttling |
| Recommended memory limit | p99 memory + 30% | Tighter limits risk OOMKill; 30% buffer is conservative for stateful workloads |
Round all recommendations up to the nearest 10 millicores (CPU) or 16 MiB (memory) for cleaner manifests.
If the caller supplies unit price ($/vCPU-hour and $/GiB-hour):
cpu_savings_per_month = (current_cpu_request - recommended_cpu_request) / 1000
× unit_price_per_vcpu_hour × 730
memory_savings_per_month = (current_memory_request - recommended_memory_request) / 1024
× unit_price_per_gib_hour × 730
Label: inference. Note: savings are approximate; they assume the freed capacity is not replaced by new workloads and that the node pool scales down proportionally.
If unit price is not supplied, state the savings formula and mark the $/mo cell as excluded — unit price not provided by caller.
A pod is flagged as consolidation-eligible when all of the following are true:
PodDisruptionBudget with maxUnavailable: 0 or minAvailable: 100% applies to the pod.pod-anti-affinity or topologySpreadConstraints rule that would prevent the pod from co-locating with other pods of the same owner.hostPath or local PersistentVolume mount.nodeName node selector pinning the pod to a specific node.If the caller has not supplied enough information to confirm all five conditions, state which conditions could not be verified and flag as not-verified — [missing conditions]. Do not assume eligibility when data is incomplete; unknown blockers present a consolidation risk.
Return, per pod or workload:
Pod / Workload: <name> (<namespace>)
CPU
Current request: <current> m
Recommended request: <p95 × 1.20, rounded> m [inference]
Current limit: <current> m (or "no-limit")
Recommended limit: <p99 × 1.30, rounded> m [inference]
Memory
Current request: <current> MiB
Recommended request: <p95 × 1.20, rounded> MiB [inference]
Current limit: <current> MiB (or "no-limit")
Recommended limit: <p99 × 1.30, rounded> MiB [inference]
Monthly savings estimate
CPU: $<amount>/mo [inference] or [excluded — unit price not provided]
Memory: $<amount>/mo [inference] or [excluded — unit price not provided]
Total: $<amount>/mo
Karpenter consolidation
Eligible: <Yes | No | Not-verified>
Blockers: <list any confirmed blockers, or list missing data preventing verification, or "None confirmed">
Metric window: <N days> [inference: window meets 7-14 day requirement]
Confidence: <Normal | Reduced — window < 7 days>
Load these only when needed:
npx claudepluginhub raishin/vanguard-frontier-agentic --plugin vanguard-frontier-agenticImplement cloud cost optimization for Kubernetes using Kubecost, HPA/VPA autoscaling, spot instances, and resource quotas. Use for rising costs, misaligned requests, or showback reporting.
Analyzes Kubernetes manifests and live cluster metrics to recommend pod right-sizing, estimate costs, detect over-provisioned containers, resource waste, and configuration drift.
Optimizes Kubernetes costs using CAST AI APIs for spot instance strategies, workload right-sizing, savings analysis, and policy configuration.