# Gpu Cost Optimizer

> - Run GPU cost visibility, chargeback, and reduction for a research Kubernetes cluster: Kubecost/OpenCost per-namespace and per-label allocation, Goldilocks rightsizing, spot/preemptible-with-checkpointing decision rules, scale-to-zero of idle research environments, and a recurring cost-review workflow with a standard report. Use whenever the user asks about GPU cloud spend, cost allocation, chargeback or showback per researcher/team/project, Kubecost, OpenCost, FinOps for Kubernetes, rightsizing requests and limits, Goldilocks, idle GPUs burning money, spot or preemptible instances for training, checkpointing strategy, reserved capacity for training runs, or "why is our Lambda bill so high". For the sharing techniques that fix underutilization use sibling gpu-sharing-advisor; for the autoscaling that reclaims idle capacity use gpu-autoscaling-engineer; for quota enforcement per tenant use researcher-tenancy-provisioner.

## Facts
- Page: https://tashan.sh/capability/skill-cloud-byte-consulting-gpu-cost-optimizer
- tashan id: skill:Cloud-Byte-Consulting/gpu-cost-optimizer
- Source: https://github.com/Cloud-Byte-Consulting/plugins
- Type: skill
- Category: cloud
- tashan score: not scored (catalogued only — too little public evidence)
- Adoption: 9.0
- Upkeep: 95.0
- Freshness: 90.0
- Evidence coverage: 84% of the inputs this score can use
- Health: active
- Instruction depth: not yet graded
- License: Apache-2.0
- Official: no

## Install

```sh
cp -r gpu-cost-optimizer ~/.claude/skills/
```

## Security audit
Not scanned. We audit npm-published capabilities; this one has no npm package we can resolve, or has not reached the queue. This is not a clean bill of health.

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Measured 2026-08-22 by tashan (https://tashan.sh) from public evidence. Scorer s5.