Syncmeta
pawurb/hotpath-rs
Sync changes from the hotpath, hotpath-macros and hotpath-drain crates to their meta counterparts (hotpath-meta, hotpath-macros-meta and hotpath-drain-meta).
Manages scaling for GKE workloads using HPA and VPA. An agent skill from google/skills.
Install Gke Workload Scaling
$ npx skills add google/skills --skill gke-workload-scaling -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install google/skills gke-workload-scaling --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cloud/gke-workload-scaling .claude/skills/gke-workload-scaling && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "gke-workload-scaling" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-workload-scaling into .claude/skills/gke-workload-scaling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-workload-scaling", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/google/skills/tree/main/skills/cloud/gke-workload-scalingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add google/skills --skill gke-workload-scaling -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install google/skills gke-workload-scaling --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/cloud/gke-workload-scaling .agents/skills/gke-workload-scaling && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "gke-workload-scaling" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-workload-scaling into .agents/skills/gke-workload-scaling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-workload-scaling", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill gke-workload-scaling -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install google/skills gke-workload-scaling --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/cloud/gke-workload-scaling .cursor/skills/gke-workload-scaling && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "gke-workload-scaling" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-workload-scaling into .cursor/skills/gke-workload-scaling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-workload-scaling", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/google/skills.git --path skills/cloud/gke-workload-scaling--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add google/skills --skill gke-workload-scaling -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install google/skills gke-workload-scaling --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/cloud/gke-workload-scaling .gemini/skills/gke-workload-scaling && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "gke-workload-scaling" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-workload-scaling into .gemini/skills/gke-workload-scaling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-workload-scaling", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install google/skills gke-workload-scalingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add google/skills --skill gke-workload-scaling -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/cloud/gke-workload-scaling .github/skills/gke-workload-scaling && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "gke-workload-scaling" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-workload-scaling into .github/skills/gke-workload-scaling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-workload-scaling", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add google/skills --skill gke-workload-scaling -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install google/skills gke-workload-scaling --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/cloud/gke-workload-scaling .opencode/skills/gke-workload-scaling && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "gke-workload-scaling" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-workload-scaling into .opencode/skills/gke-workload-scaling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-workload-scaling", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
gke-workload-scaling- GitHub stars
- 21k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 516 words
- Files
- 3 (incl. assets)
- Skills in repo
- 147
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Manages scaling for GKE workloads using HPA and VPA. An agent skill from google/skills.
- Works in 3 steps: Manual Scaling → Horizontal Pod Autoscaling (HPA) → Vertical Pod Autoscaling (VPA)
- Configuring Horizontal Pod Autoscaler (HPA)
- SKILL.md covers Workflows, Best Practices and Rightsizing Workflow
- Calls kubectl and gcloud
What it does
Gke Workload Scaling is an agent skill from google/skills, published by the product's own GitHub organization. Manages scaling for GKE workloads using HPA and VPA. Use when configuring Horizontal Pod Autoscaler (HPA), configuring Vertical Pod Autoscaler (VPA), or applying best practices for GKE workload autoscaling. Do not use for cluster-level autoscaling (Cluster Autoscaler), static cluster sizing, or configuring node-level machine styles directly.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including assets (for example `assets/hpa-example.yaml` and `assets/vpa-example.yaml`).
It sits in DevOps & Cloud. It works with Google Kubernetes Engine and Prometheus. The repository describes itself as: Agent Skills for Google products and technologies. The licence is Apache-2.0.
When your agent uses it
- Configuring Horizontal Pod Autoscaler (HPA)
- Configuring Vertical Pod Autoscaler (VPA)
- Applying best practices for GKE workload autoscaling
- Cluster-level autoscaling (Cluster Autoscaler)
Example prompts
- “Use the gke-workload-scaling skill to manage scaling for GKE workloads using HPA and VPA. An agent skill from google/skills”
- “/gke-workload-scaling”
Workflow steps
3 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 7d97937. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
kubectlgcloud
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use kubectl and gcloud, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Gke Workload Scaling loads about 1.3k tokens when it runs. Until then it costs about 91 tokens; SKILL.md has 516 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from google/skills at commit 7d97937, republished under its Apache-2.0 licence (© google). 516 words, ~1,254 tokens.
Download SKILL.mdSave it as
.claude/skills/gke-workload-scaling/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- gke-workload-scaling
- description
- Manages scaling for GKE workloads using HPA and VPA. Use when configuring Horizontal Pod Autoscaler (HPA), configuring Vertical Pod Autoscaler (VPA), or applying best practices for GKE workload autoscaling. Do not use for cluster-level autoscaling (Cluster Autoscaler), static cluster sizing, or configuring node-level machine styles directly.
- metadata.version
- 1.0.0
- metadata.category
- Containers
GKE Workload Scaling
This skill provides workflows and best practices for scaling applications on Google Kubernetes Engine (GKE). It covers manual scaling, Horizontal Pod Autoscaling (HPA), and Vertical Pod Autoscaling (VPA).
Workflows
1. Manual Scaling
Scale a deployment to a fixed number of replicas. Useful for immediate manual intervention or testing.
Command:
kubectl scale deployment {deployment_name} --replicas={number} -n {namespace}
# Verify the scale event
kubectl get deployment {deployment_name} -n {namespace}2. Horizontal Pod Autoscaling (HPA)
Automatically scale the number of pods based on observed CPU utilization, memory utilization, or custom metrics.
Prerequisites:
- Metrics Server must be running (enabled by default on GKE).
- Containers clearly define resource requests/limits.
Quick Command:
kubectl autoscale deployment {deployment_name} --cpu-percent=50 --min=1 --max=10Manifest Approach (Recommended): Use a YAML manifest for version-controlled configuration. See assets/hpa-example.yaml for a template.
kubectl apply -f assets/hpa-example.yaml
# Verify HPA is created and fetching metrics
kubectl get hpaCustom Metrics & External Metrics: For GKE, the modern and recommended approach for scaling based on Cloud Monitoring metrics (e.g., Pub/Sub queue length) is to use the External metric type, which is natively supported by the GKE control plane without requiring the Custom Metrics Adapter. For application-specific metrics exposed via Prometheus, you can use Google Cloud Managed Service for Prometheus or the Prometheus Adapter.
3. Vertical Pod Autoscaling (VPA)
Automatically adjust the CPU and memory reservations for your pods to match actual usage. This is critical for right-sizing workloads.
Prerequisites:
- VPA must be enabled on the cluster.
- Autopilot: Enabled by default.
- Standard: Must be enabled manually.
Enable VPA on Standard Cluster:
gcloud container clusters update {cluster_name} --enable-vertical-pod-autoscaling --zone {zone}Update Modes:
Off: Calculates recommendations but does not apply them. Good for "dry run" analysis.Initial: Assigns resources only at pod creation time.Auto: Updates running pods by restarting them if recommendations differ significantly from requests.InPlaceOrRecreate: Attempts to update Pod resources without recreating the Pod. If in-place update is not possible, it reverts toAutomode (requires GKE 1.34+).
Example: See assets/vpa-example.yaml for a configuration template.
Show full SKILL.md (233 more words)Show less
Best Practices
- Define Resource Requests: HPA and VPA rely on accurate resource requests. Always define them in your container specs.
- Avoid Metric Conflicts: Do not configure HPA and VPA to use the same
metric (e.g., both CPU). This causes thrashing.
- Typical Pattern: HPA on CPU, VPA on Memory.
- Pod Disruption Budgets (PDBs): Define PDBs to ensure application availability during scaling events or node upgrades.
- HPA Lag: HPA has a stabilization window (default 5 mins) to prevent rapid fluctuation.
- VPA "Auto" Mode Risks: In "Auto" mode, VPA restarts pods to change
resources. Ensure your application handles restarts gracefully (e.g.,
handles SIGTERM).
- Note: By default, VPA requires at least 2 replicas to perform
evictions (to prevent a situation where the only running replica is
evicted, causing downtime). In GKE 1.22+, you can override this by
setting
minReplicasinPodUpdatePolicy.
- Note: By default, VPA requires at least 2 replicas to perform
evictions (to prevent a situation where the only running replica is
evicted, causing downtime). In GKE 1.22+, you can override this by
setting
Rightsizing Workflow
- Deploy VPA in
Offmode for 24+ hours - Read recommendations:
kubectl describe vpa {deployment_name}-vpa -n {namespace} - Compare
targetvalues against currentrequests - Apply with 20% buffer:
new_request = target * 1.2 - Use patch format or update deployment manifest to apply new resource requests
| Condition | Recommendation | Risk |
|---|---|---|
| CPU request >5x P95 actual | Reduce to P95 * 1.2 | Medium |
| Memory request >3x P95 actual | Reduce to P95 * 1.2 | Medium |
| CPU request >2x P95 actual | Rightsizing with 20% buffer | Low |
| No resource limits set | Add limits to prevent noisy-neighbor | Low |
© google, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 2 other files (assets) in skills/cloud/gke-workload-scaling of google/skills.
- SKILL.md
- assets/hpa-example.yaml
- assets/vpa-example.yaml
Open the folder on GitHubat commit 7d97937
Compare with similar skills
Gke Workload Scaling next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Gke Workload Scaling this skillgoogle/skills | 21k | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Syncmetapawurb/hotpath-rs | 1.9k | — | ~1.2k | Automated safety check: Notes | MIT | |
| Devopsnicepkg/auto-company | 192 | 2 repos | ~814 | Automated safety check: Pass | MIT | |
| KubeShark for KubernetesLukasNiessen/kubernetes-skill | 444 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Optimize Slurm TopologyNVlabs/alpasim | 1.3k | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| UModel Root Cause Analysisalibaba/UnifiedModel | 412 | — | ~1.9k | Automated safety check: Pass | Custom licence |
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Questions about Gke Workload Scaling
What does Gke Workload Scaling do?
Manages scaling for GKE workloads using HPA and VPA. An agent skill from google/skills. Gke Workload Scaling is an agent skill from google/skills, published by the product's own GitHub organization. Manages scaling for GKE workloads using HPA and VPA.
When should I use Gke Workload Scaling?
Gke Workload Scaling fits situations like: configuring Horizontal Pod Autoscaler (HPA); configuring Vertical Pod Autoscaler (VPA); applying best practices for GKE workload autoscaling; cluster-level autoscaling (Cluster Autoscaler).
How do I install Gke Workload Scaling in Claude Code?
Run `npx skills add google/skills --skill gke-workload-scaling -a claude-code`. Or copy the skill folder (skills/cloud/gke-workload-scaling in google/skills) into .claude/skills/gke-workload-scaling in your project. Claude Code loads it when a task matches its description.
How do I install Gke Workload Scaling in Codex?
Run `npx skills add google/skills --skill gke-workload-scaling -a codex`. Or copy the skill folder (skills/cloud/gke-workload-scaling in google/skills) into .agents/skills/gke-workload-scaling in your project. Codex loads it when a task matches its description.
Can I use Gke Workload Scaling in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add google/skills --skill gke-workload-scaling -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gke-workload-scaling, .gemini/skills/gke-workload-scaling, .github/skills/gke-workload-scaling and .opencode/skills/gke-workload-scaling in your project.
What does Gke Workload Scaling need to run?
Going by SKILL.md and its folder, Gke Workload Scaling needs the command-line tools its instructions call (kubectl and gcloud).
Does Gke Workload Scaling access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Gke Workload Scaling safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Gke Workload Scaling use?
Gke Workload Scaling is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Gke Workload Scaling use?
About 1.3k tokens (SKILL.md is roughly 5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Gke Workload Scaling?
Skills that share tags, products or a category with Gke Workload Scaling: Syncmeta (pawurb/hotpath-rs, 1.9k stars), Devops (nicepkg/auto-company, 192 stars), KubeShark for Kubernetes (LukasNiessen/kubernetes-skill, 444 stars) and Optimize Slurm Topology (NVlabs/alpasim, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Gke Workload Scaling?
google (a GitHub organization, an official publisher) maintains it in google/skills, which has 21,032 GitHub stars. The repository holds 147 skills in this directory. The repository was last updated on October 8, 2026.
Source: google/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.