What does this skill do, and when should you use it?
This skill is for engineers managing GKE ComputeClasses, including Spot VM fallback, accelerator and machine-family selection, zone targeting, and node-pool auto-creation. It provides configuration guidance, YAML template rules, scheduling diagnostics, and access-governance advice. It also covers stateful PVs, reservations, Autopilot mode, Karpenter migration, and node-security boundaries. It does not cover cluster-level Node Auto Provisioning configuration or general GKE cluster creation.
Guides an agent in designing ComputeClasses around cost, accelerators, machine families, zones, CUDs/Reservations, and workload constraints; produces constrained YAML examples with placeholders; explains priority ordering, Spot and reservation fallback, node-pool auto-creation, GPU/Spot taints, PV topology, and Pod scheduling; directs users to check the ComputeClass CRD with kubectl and use the repository's reference documents, log script, and YAML assets for troubleshooting and governance.
- A GKE platform engineer needs Spot VMs for horizontally scalable stateless workloads with on-demand fallback when capacity is unavailable.
- A machine-learning team needs workloads placed on specific accelerators such as L4, H100, or v5p.
- An infrastructure team needs a reliable priority fallback chain based on CUDs, Reservations, machine families, and zones.
- An operator needs to diagnose Pending or noScaleUp Pods caused by GPU or Spot taints, selector conflicts, zonal PV constraints, or storage topology.
- A platform administrator needs to separately control who can modify ComputeClass objects and which workloads can consume them.
How do you install this skill?
- Review every YAML against the target GKE version, region, quotas, CUDs/Reservations, and actual CRD schema before use; the documentation explicitly says not to deploy the example templates directly.
- The logging script reads project logs and can append to a user-selected path; verify gcloud/kubectl identity, least-privilege logging access, sensitivity of log contents, and output-file permissions first.
- The skill depends on GKE, gcloud, kubectl, jq, and version-specific features; mainland-China reachability, a compatibility matrix, and executable test evidence are not supplied.
- Shell / CLI
- Local filesystem
kubectl
Run npx skills add google/skills, then select GKE ComputeClasses during installation. The README does not document a specific installation directory or a separate single-skill installation command.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/google/skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/cloud/gke-compute-classes" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
After installation, use a prompt such as Help me configure a GKE ComputeClass for GPU workloads with on-demand fallback and troubleshoot pending pods. Supply existing CUDs/Reservations, workload statefulness, cluster pools, target region/zones, and Pod requests for more specific guidance. Do not use it for cluster-level Node Auto Provisioning configuration or general GKE cluster creation.
What are this skill's strengths and limitations?
- Covers ComputeClass configuration, cost optimization, scheduling diagnostics, and access governance.
- Addresses practical failure modes involving GPU and Spot taints, disk generations, reservation fallback, and scarce large machine shapes.
- Provides entry points to reference documentation, YAML assets, and a log script.
- Uses CUDs/Reservations, Pod requests, and zone constraints to shape more reliable fallback strategies.
- Recommendations depend on the GKE version, region, available capacity, workload constraints, and existing reservations; generic templates are not production-ready by themselves.
- The source provides guidance and examples but does not document a test suite or actual cluster validation results.
- Users must provide environment context and perform validation and troubleshooting with kubectl or repository assets.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| GKE ComputeClasses Advisor this page ✓ Google · Official | 57 · Use with care | ★ 21k | 12d ago | Apache-2.0 |
| GKE Cluster Autoscaler Guide ✓ Google · Official | 52 · Use with care | ★ 21k | 12d ago | Apache-2.0 |
| GKE Workload Autoscaling ✓ Google · Official | 49 · Use with care | ★ 21k | 12d ago | Apache-2.0 |
| GKE Cost Optimization ✓ Google · Official | 47 · Use with care | ★ 21k | 12d ago | Apache-2.0 |
| GKE AI Inference Deployment Assistant ✓ Google · Official | 46 · Use with care | ★ 21k | 12d ago | Apache-2.0 |
How did FollowSkills review this skill?
The skill requires placeholders, an “EXAMPLE TEMPLATE - DO NOT DEPLOY” label, collection of critical environment context, and refusal to disable node security or embed service-account keys. It also addresses access governance, prompt-injection resistance, Spot eviction, and operational safeguards. It does not fully disclose data flows, require explicit execution confirmation, define rollback procedures, or provide dependency-security review, so points are deducted.
The material contains detailed schema constraints, edge cases, scheduling explanations, and diagnostic errors in the logging script, making the happy path plausible. No executable tests, CI coverage, or complete contents of the referenced files are provided, and several version- and behavior-specific claims cannot be reproduced from static evidence, so the score remains conservative under the static cap.
The audience, use cases, and non-fit boundary are clear, covering Spot, GPU/TPU, machine families, zones, governance, and troubleshooting, with additional Pod, PV, and Autopilot boundaries. Chinese-language guidance, explicit input/output contracts, and positive/negative trigger examples are missing; reachability of required GKE/GCP services from mainland China is not addressed, so points are deducted.
The skill has layered sections, an index, quick actions, example assets, limitation notes, and repository-level installation guidance. The README supplies Apache-2.0 licensing, installation, maintenance issue paths, and basic provenance through the official organization and revision. Skill-level versioning, changelog, named maintenance ownership, dependency-version matrix, FAQs, and the indexed reference files are absent from the supplied evidence, so points are deducted.
The content directly targets ComputeClass configuration, optimization, and troubleshooting, with templates for several representative scenarios and an actionable log-monitoring script. Without execution results, real task validation, or comparison with alternatives, direct usability on the target GKE versions and regions is unverified; the static ceiling therefore limits this dimension to 7.
The supplied files include revision-scoped YAML and shell material, version conditions, error messages, and reference paths, providing some auditability. There is no committed test suite, CI result, third-party execution evidence, or cross-source corroboration; external URLs appear only in comments and this review cannot add evidence, so the score is low.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →