Vercel Optimize Audit
vercel-labs/agent-skills
Runs a metrics-first audit of a deployed Vercel project, gating investigations on real signals to produce ranked, citation-backed cost and performance recommendations.
Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas.
Install Gke Cost Optimization
$ npx skills add google/skills --skill gke-cost-optimization -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install google/skills gke-cost-optimization --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-cost-optimization .claude/skills/gke-cost-optimization && 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-cost-optimization" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-cost-optimization into .claude/skills/gke-cost-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-cost-optimization", 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-cost-optimizationType 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-cost-optimization -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install google/skills gke-cost-optimization --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-cost-optimization .agents/skills/gke-cost-optimization && 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-cost-optimization" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-cost-optimization into .agents/skills/gke-cost-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-cost-optimization", 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-cost-optimization -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install google/skills gke-cost-optimization --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-cost-optimization .cursor/skills/gke-cost-optimization && 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-cost-optimization" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-cost-optimization into .cursor/skills/gke-cost-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-cost-optimization", 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-cost-optimization--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-cost-optimization -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install google/skills gke-cost-optimization --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-cost-optimization .gemini/skills/gke-cost-optimization && 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-cost-optimization" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-cost-optimization into .gemini/skills/gke-cost-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-cost-optimization", 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-cost-optimizationInstalls 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-cost-optimization -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-cost-optimization .github/skills/gke-cost-optimization && 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-cost-optimization" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-cost-optimization into .github/skills/gke-cost-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-cost-optimization", 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-cost-optimization -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-cost-optimization --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-cost-optimization .opencode/skills/gke-cost-optimization && 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-cost-optimization" agent skill from https://github.com/google/skills/tree/main/skills/cloud/gke-cost-optimization into .opencode/skills/gke-cost-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gke-cost-optimization", 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-cost-optimization- GitHub stars
- 21k
- Token cost
- ~2.1k tokens
- SKILL.md length
- 812 words
- Files
- 4 (incl. assets)
- Skills in repo
- 145
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas.
- Works in 7 steps: Prerequisite: Cost Allocation & Monitoring → Configure Resource Quotas → Pod Rightsizing (VPA & MPA) → …
- Optimizing GKE cluster
- SKILL.md covers Workflows & Optimization… and Cost & Utilization Monitoring
- Calls kubectl, gcloud and bq
What it does
Gke Cost Optimization is an agent skill from google/skills, published by the product's own GitHub organization. Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas. Use when optimizing GKE cluster or workload costs, configuring GKE cost allocation or quotas, rightsizing CPU/memory requests, or selecting Spot VMs and machine types. Don't use for general compute class provisioning or GPU Selection (use gke-compute-classes instead).
Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including assets (for example `assets/resource-quota-example.yaml`, `assets/spot-deployment-example.yaml` and `assets/vpa-recommendation-mode.yaml`).
It sits in DevOps & Cloud, covering Cloud cost optimization. It works with Google Kubernetes Engine. The repository describes itself as: Agent Skills for Google products and technologies. The licence is Apache-2.0.
When your agent uses it
- Optimizing GKE cluster
- Configuring GKE cost allocation
- Rightsizing CPU/memory requests
- Selecting Spot VMs and machine types
Example prompts
- “Use the gke-cost-optimization skill to optimiz GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas”
- “/gke-cost-optimization”
Workflow steps
7 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 8a1ac05. 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:
kubectlgcloudbq
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 Cost Optimization loads about 2.1k tokens when it runs. Until then it costs about 101 tokens; SKILL.md has 812 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 8a1ac05, republished under its Apache-2.0 licence (© google). 812 words, ~2,084 tokens.
Download SKILL.mdSave it as
.claude/skills/gke-cost-optimization/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- gke-cost-optimization
- description
- Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas. Use when optimizing GKE cluster or workload costs, configuring GKE cost allocation or quotas, rightsizing CPU/memory requests, or selecting Spot VMs and machine types. Don't use for general compute class provisioning or GPU Selection (use gke-compute-classes instead).
- metadata.version
- 1.0.0
- metadata.category
- CloudObservabilityAndMonitoring
GKE Cost Optimization
This reference covers strategies and workflows for reducing Google Kubernetes Engine (GKE) costs while maintaining a secure and reliable posture.
Workflows & Optimization Strategies
1. Prerequisite: Cost Allocation & Monitoring
To enable GKE cost allocation (--enable-cost-allocation) for billing tracking
across namespaces and labels, inspect live cluster utilization (kubectl top),
or run historical cost breakdown queries in BigQuery (bq), use the
gke-cost-analysis skill. Once tracking is active and waste is diagnosed,
apply the optimization workflows below.
2. Configure Resource Quotas
Resource quotas restrict total resource consumption across tenants in
multi-tenant clusters, preventing runaway costs. Template:
assets/resource-quota-example.yaml
(set namespace + hard limits, then kubectl apply -f).
3. Pod Rightsizing (VPA & MPA)
Adjust pod resource requests to match actual utilization. Over-provisioned requests are one of the largest sources of waste.
- Use VPA in Recommendation Mode (
updateMode: "Off"— recommends without evicting):
# 1. Deploy VPA in recommendation mode (template: assets/vpa-recommendation-mode.yaml)
kubectl apply -f assets/vpa-recommendation-mode.yaml
# 2. Wait 24+ hours for data collection, then read recommendations
kubectl get vpa {deployment_name}-vpa -o jsonpath='{.status.recommendation}'- Optimization Rules:
| Condition | Action | Savings |
|---|---|---|
| CPU request >5x P95 actual | Reduce to P95 * 1.2 | High |
| Memory request >3x P95 actual | Reduce to P95 * 1.2 | High |
| CPU request >2x P95 actual | Reduce to P95 * 1.2 | Medium |
| No resource requests set | Add requests (enables bin-packing) | Medium |
- Use MPA: Reconcile HPA and VPA recommendations when scaling both horizontally and vertically to avoid conflicting scale events.
- Review Cost Recommendations: Check Google Cloud Console (
Cost Management>GKE Cost Optimization) for built-in rightsizing suggestions.
4. Spot VMs via ComputeClasses & NodeSelector
Use Spot VMs for fault-tolerant workloads to achieve 60-90% cost reduction.
4.1 ComputeClass Configuration
For a Spot-first ComputeClass with On-Demand fallback (priority ordering,
activeMigration, machine family selection), use the gke-compute-classes
skill — ComputeClass YAML generation and priority configuration are its domain,
not this skill's.
4.2 Direct Workload Spot Selection (nodeSelector)
For stateless or batch workloads in GKE Autopilot, target Spot capacity directly
using nodeSelector:
[!WARNING] Preemption Warning: Spot VMs are interruptible and can be preempted at any time with a 30-second notice. Workloads must be fault-tolerant and run with at least 2 replicas for high availability. Always explicitly warn users about this preemption risk when recommending Spot VMs.
The exact Pod-level selector is:
nodeSelector:
cloud.google.com/gke-spot: "true"Full worked Deployment (replicas >= 2, terminationGracePeriodSeconds: 25,
preStop hook): assets/spot-deployment-example.yaml.
Spot-Suitable Workloads:
| Workload | Spot-Suitable? |
|---|---|
| Batch / data processing | Yes |
| Dev / test environments | Yes |
| Stateless web/API (replicas >= 2) | Yes (with PDBs) |
| Jobs with checkpointing | Yes |
| Stateful workloads (databases) | No |
| Single-replica critical services | No |
5. Machine Type Selection
When choosing node shapes or configuring ComputeClasses:
| Family | Use Case | Relative Cost |
|---|---|---|
| e2 | General purpose, burstable | Lowest |
| t2a / t2d | Scale-out (Arm/AMD), price-performance optimized | Low |
| n4a | Axion Arm-based, general-purpose price-performance | Low |
| n4 / n4d | General purpose (Intel/AMD), flexible shapes | Low-Medium |
| c4a | Axion Arm-based, general-purpose, high efficiency | Medium |
| c3 / c4 | Compute-optimized (Intel) | Medium-High |
| c3d / c4d | Compute-optimized (AMD), high throughput | Medium-High |
| ek-standard | Autopilot enhanced | Medium |
| m3 / x4 | Memory-optimized, SAP HANA, large databases | High |
| g2 (L4 GPU) | AI inference | High |
| a3 (H100 GPU) | AI training | Highest |
| a4 / a4x | Ultra-scale AI (Blackwell GPUs) | Highest |
Show full SKILL.md (346 more words)Show less
6. Committed Use Discounts (CUDs)
For steady-state workloads with predictable baseline usage, purchase 1-year or 3-year CUDs:
- Resource-based CUDs (committed to a machine family/region): roughly high-30s% discount for 1-year, ~55% for 3-year (varies by machine family).
- Flexible CUDs (spend-based, portable across families/regions): lower discounts (~28% 1-year, ~46% 3-year) in exchange for flexibility.
- Autopilot: Autopilot-specific CUDs were retired in January 2026 — new commitments covering Autopilot usage are spend-based Compute Flexible CUDs (existing Autopilot CUD commitments run out their term).
- Applied automatically to matching usage across the region.
- Purchase via Google Cloud Console > Billing > Committed use discounts.
Size the commitment to the steady-state baseline only. A commitment bills for the full term whether or not you use it, so over-committing to peak usage converts a discount into waste. Measure the floor of actual usage over a representative period, commit to that, and cover everything above it with the elastic options already in this skill:
- Baseline (always running) → resource-based CUDs.
- Variable / bursty → autoscaling on on-demand capacity.
- Interruption-tolerant (batch, CI, stateless workers) → Spot VMs, which stack with autoscaling and need no commitment.
When recommending CUDs, state the split explicitly rather than implying the whole footprint should be committed.
7. Cluster Management & Multi-Tenancy
- Idle dev clusters: GKE has no stop/start operation, and the cluster
management fee accrues as long as the cluster exists. To cut idle costs,
scale node pools to zero (
gcloud container clusters resize {cluster_name} --node-pool {pool_name} --num-nodes 0) or delete and recreate the cluster via IaC (Terraform/Config Connector). - Right-size node pools (Standard): Use Cluster Autoscaler with appropriate min/max limits.
- Cheap warm headroom instead of overprovisioned nodes: Standby capacity
buffers (Preview, GKE 1.36.0-gke.2253000+) keep pre-initialized nodes
suspended — you pay only disk + IP instead of full node price, with ~30s
resume. See the
gke-cluster-autoscalerskill. - Multi-tenant consolidation: Share a single cluster across multiple engineering teams instead of maintaining per-team clusters, using Namespaces and ResourceQuotas to isolate workloads.
Cost & Utilization Monitoring
To inspect live node/pod utilization (kubectl top nodes/pods), view cluster
cost budgets (gcloud billing budgets list), or query detailed billing reports
in BigQuery (bq query), refer to the gke-cost-analysis skill.
© 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 3 other files (assets) in skills/cloud/gke-cost-optimization of google/skills.
- SKILL.md
- assets/resource-quota-example.yaml
- assets/spot-deployment-example.yaml
- assets/vpa-recommendation-mode.yaml
Open the folder on GitHubat commit 8a1ac05
Compare with similar skills
Gke Cost Optimization 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 Cost Optimization this skillgoogle/skills | 21k | — | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| Vercel Optimize Auditvercel-labs/agent-skills | 32k | 8 repos | ~4.3k | Automated safety check: Pass | None | |
| Cloud Cost Optimizationwshobson/agents | 40k | 13 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Trigger.dev Cost Savings Auditpapermark/papermark | 9.2k | — | ~1.3k | Automated safety check: Pass | Custom licence | |
| Kubernetes SpecialistJeffallan/claude-skills | 12k | 1 repos | ~2.1k | Automated safety check: Pass | MIT | |
| Axiom Cost Controlopenclaw/clawhub | 9.5k | — | ~1.7k | Automated safety check: Pass | MIT |
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Related
Works with
Categories
Questions about Gke Cost Optimization
What does Gke Cost Optimization do?
Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas. Gke Cost Optimization is an agent skill from google/skills, published by the product's own GitHub organization. Optimizes GKE costs, rightsizes workloads, and configures Spot VMs, CUDs, cost allocation, and resource quotas.
When should I use Gke Cost Optimization?
Gke Cost Optimization fits situations like: optimizing GKE cluster; configuring GKE cost allocation; rightsizing CPU/memory requests; selecting Spot VMs and machine types.
How do I install Gke Cost Optimization in Claude Code?
Run `npx skills add google/skills --skill gke-cost-optimization -a claude-code`. Or copy the skill folder (skills/cloud/gke-cost-optimization in google/skills) into .claude/skills/gke-cost-optimization in your project. Claude Code loads it when a task matches its description.
How do I install Gke Cost Optimization in Codex?
Run `npx skills add google/skills --skill gke-cost-optimization -a codex`. Or copy the skill folder (skills/cloud/gke-cost-optimization in google/skills) into .agents/skills/gke-cost-optimization in your project. Codex loads it when a task matches its description.
Can I use Gke Cost Optimization 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-cost-optimization -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-cost-optimization, .gemini/skills/gke-cost-optimization, .github/skills/gke-cost-optimization and .opencode/skills/gke-cost-optimization in your project.
What does Gke Cost Optimization need to run?
Going by SKILL.md and its folder, Gke Cost Optimization needs the command-line tools its instructions call (kubectl, gcloud and bq).
Does Gke Cost Optimization 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 Cost Optimization 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 Cost Optimization use?
Gke Cost Optimization 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 Cost Optimization use?
About 2.1k tokens (SKILL.md is roughly 8.3k 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 Cost Optimization?
Skills that share tags, products or a category with Gke Cost Optimization: Vercel Optimize Audit (vercel-labs/agent-skills, 32k stars), Cloud Cost Optimization (wshobson/agents, 40k stars), Trigger.dev Cost Savings Audit (papermark/papermark, 9.2k stars) and Kubernetes Specialist (Jeffallan/claude-skills, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Gke Cost Optimization?
google (a GitHub organization, an official publisher) maintains it in google/skills, which has 20,994 GitHub stars. The repository holds 145 skills in this directory. The repository was last updated on October 6, 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.