What does this skill do, and when should you use it?
This skill focuses on scaling application workloads running on Google Kubernetes Engine. It covers fixed replica changes for Deployments, Horizontal Pod Autoscaling (HPA), and Vertical Pod Autoscaling (VPA) for resource right-sizing. It includes kubectl and gcloud examples, YAML manifest references, prerequisites, update modes, and operational best practices. It explicitly excludes Cluster Autoscaler, static cluster sizing, and direct node machine-style configuration.
Guides users through kubectl scale for fixed Deployment replicas, kubectl autoscale or an HPA manifest for CPU, memory, or custom-metric scaling, and kubectl get commands for verification. It shows how to enable VPA on a Standard GKE cluster with gcloud and explains Off, Initial, Auto, and InPlaceOrRecreate update modes. It also describes reading VPA recommendations with kubectl describe vpa, comparing target values with current requests, and applying a 20% buffer when calculating new requests.
- A GKE operator needs to set a Deployment to a fixed replica count for immediate intervention or testing.
- A platform engineer wants Pods to scale horizontally from CPU, memory, or custom metrics.
- A Kubernetes team needs to right-size CPU and memory requests based on observed workload usage.
- A team wants to run VPA in Off mode for at least 24 hours before reviewing recommendations and changing manifests.
How do you install this skill?
- Before applying scaling changes or enabling VPA, confirm the target cluster, namespace, deployment, and side effects, and prepare a verifiable rollback plan.
- Validate the GKE version, Metrics Server, VPA support, and HPA/VPA metric interactions; the generic names and default namespace in the examples are not production-ready by themselves.
- The README links to gke-scaling while the assessed path is gke-workload-scaling; verify the path mapping during discovery or installation.
- Shell / CLI
- Local filesystem
kubectlgcloud CLIGoogle Kubernetes Engine (GKE) cluster
Run npx skills add google/skills and select the desired skill during installation. The README does not document the exact installation directory or command-line selection flags; the target file is skills/cloud/gke-workload-scaling/SKILL.md.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/google/skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/cloud/gke-workload-scaling" ~/.claude/skills/
rm -rf "$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?
Once installed, send your agent any of these to trigger it:
- Configure HPA for the GKE deployment deployment_name with 1–10 replicas and check its resource requests.
In an Agent Skills-compatible client, use a request such as “Configure HPA for the GKE deployment deployment_name with 1–10 replicas and check its resource requests.” The workflow assumes access to a GKE cluster and kubectl; VPA enablement on Standard clusters also uses gcloud. Authentication steps are not documented in the supplied source.
What are this skill's strengths and limitations?
- Covers manual scaling, HPA, and VPA in one focused GKE workflow.
- Provides copyable kubectl and gcloud commands plus referenced HPA and VPA YAML templates.
- Addresses resource requests, metric conflicts, PDBs, HPA stabilization, and VPA restart risks.
- Includes a concrete P95-based right-sizing process with a 20% buffer.
- Does not cover Cluster Autoscaler, static cluster capacity, or node machine configuration.
- HPA requires Metrics Server, and reliable HPA/VPA behavior depends on accurate resource requests.
- VPA Auto mode can restart running Pods, so applications must handle termination gracefully.
- The supplied material does not include a test suite, compatibility matrix, or complete cluster authentication procedure.
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 Workload Autoscaling this page ✓ Google · Official | 49 · Use with care | ★ 21k | 12d ago | Apache-2.0 |
| GKE ComputeClasses Advisor ✓ 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 AI Inference Deployment Assistant ✓ Google · Official | 46 · Use with care | ★ 21k | 12d ago | Apache-2.0 |
| GKE Production Golden Path ✓ Google · Official | 54 · Use with care | ★ 21k | 12d ago | Apache-2.0 |
The skill positions HPA as the mechanism for changing Pod count and VPA as the mechanism for changing Pod CPU and memory resources. It recommends avoiding the same metric for both. Cluster Autoscaler is named as a separate area that this skill does not address.
How did FollowSkills review this skill?
The skill clearly scopes itself to GKE workload autoscaling and discloses restart and eviction risks for HPA/VPA; however, kubectl scale, autoscale, gcloud update, and VPA Auto operations mutate an external cluster without explicit confirmation, least-privilege guidance, or rollback procedures, so points are deducted. No credential handling or covert exfiltration is shown.
The workflows, manifests, prerequisites, and verification commands are broadly consistent and cover manual scaling, HPA, VPA, and rightsizing; however, there is no abnormal-input handling, diagnostic failure feedback, compatibility validation, or test evidence. Several GKE version and Metrics Server claims cannot be confirmed from static files, limiting the score.
The name, audience, scenarios, and exclusions for Cluster Autoscaler, static sizing, and direct node-machine configuration are clear; however, required inputs, environment assumptions, non-fit boundaries, and semantic trigger details remain incomplete. Chinese-language support and mainland-China reachability are not addressed, so points are deducted.
The documentation is readable and layered through workflows, best practices, and asset templates. The repository supplies installation guidance, an Apache-2.0 license, issue-reporting route, and an active-development note. The skill lacks its own version, changelog, explicit maintainer, and thorough troubleshooting; the README link also differs from the assessed path name, so points are deducted.
The commands and HPA/VPA YAML templates provide a useful starting point for the core scaling tasks, with a rightsizing workflow included; however, the templates use generic names and the default namespace, and production checks, rollback, and verification coverage are incomplete. Static review cannot establish directly runnable results, so the score follows the static ceiling.
The supplied skill files, example manifests, README, license, and pinned revision are auditable evidence; however, there is no committed key-path test suite, CI execution evidence, or independent reproduction. The conclusion therefore rests mainly on static documentation and receives limited credit.
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.
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