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Official agent skill

Gke Cluster Creation

by google in google/skills

Plans and executes GKE cluster creation, provisioning, and production readiness audits using pre-defined templates (Autopilot, Standard Regional, GPU/AI Inference, AI Hypercompute).

OfficialApache-2.0Auto-check passedDevOps & Cloud

Install Gke Cluster Creation

skills CLI
$ npx skills add google/skills --skill gke-cluster-creation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install google/skills gke-cluster-creation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cloud/gke-cluster-creation .claude/skills/gke-cluster-creation && rm -rf skills-src

Use ~/.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/

Facts

Skill name
gke-cluster-creation
GitHub stars
21k
Token cost
~3.1k tokens
SKILL.md length
791 words
Files
1
Skills in repo
147
Repo updated
First seen
Licence
Apache-2.0

At a glance

Plans and executes GKE cluster creation, provisioning, and production readiness audits using pre-defined templates (Autopilot, Standard Regional, GPU/AI Inference, AI Hypercompute).

  • Works in 5 steps: Golden Path Autopilot (Production) → Autopilot Dev/Test → Standard Regional (High Availability /… → …
  • Creating GKE clusters
  • SKILL.md covers Workflow, Mode Selection, Best Practices and Templates, plus 1 more section
  • Calls gcloud and kubectl; reaches googleapis.com

What it does

Gke Cluster Creation is an agent skill from google/skills, published by the product's own GitHub organization. Plans and executes GKE cluster creation, provisioning, and production readiness audits using pre-defined templates (Autopilot, Standard Regional, GPU/AI Inference, AI Hypercompute). Use when creating GKE clusters, provisioning GKE environments, selecting cluster modes, or auditing GKE clusters. Don't use for application onboarding or deployment configuration (use gke-app-onboarding instead).

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in DevOps & Cloud, covering Platform engineering. It works with Google Kubernetes Engine and Google Cloud. The repository describes itself as: Agent Skills for Google products and technologies. The licence is Apache-2.0.

When your agent uses it

  • Creating GKE clusters
  • Provisioning GKE environments
  • Selecting cluster modes
  • Auditing GKE clusters

Example prompts

  • “Use the gke-cluster-creation skill to plan and executes GKE cluster creation, provisioning, and production readiness audits using pre-defined…”
  • “/gke-cluster-creation”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Golden Path Autopilot (Production)
  2. Autopilot Dev/Test
  3. Standard Regional (High Availability / Custom Requirements)
  4. GPU Inference & AI Workloads (L4 / ComputeClass)
  5. AI Hypercompute (A3 HighGPU / Large Model Serving)

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:

    • gcloud
    • kubectl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • googleapis.com

    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 Cluster Creation loads about 3.1k tokens when it runs. Until then it costs about 104 tokens; SKILL.md has 791 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~104
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

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). 791 words, ~3,115 tokens.

Download SKILL.mdSave it as .claude/skills/gke-cluster-creation/SKILL.md (or your agent's skills folder).
name
gke-cluster-creation
description
Plans and executes GKE cluster creation, provisioning, and production readiness audits using pre-defined templates (Autopilot, Standard Regional, GPU/AI Inference, AI Hypercompute). Use when creating GKE clusters, provisioning GKE environments, selecting cluster modes, or auditing GKE clusters. Don't use for application onboarding or deployment configuration (use gke-app-onboarding instead).
metadata.version
1.0.0
metadata.category
Containers

GKE Cluster Creation

This reference guides creating Google Kubernetes Engine (GKE) clusters by providing a set of best-practice templates and guiding through mode selection and customization. The golden path Autopilot configuration is the default for all new clusters.

MCP Tools: list_clusters, create_cluster, get_cluster, list_operations, get_operation

Workflow

  1. Discover context: Use list_clusters to see existing clusters. Use gcloud config get-value project if project unknown.
  2. Gather inputs: project_id, location (region or zone), cluster_name, environment type. If missing essential details, ask the user before taking action.
  3. Select mode & explain trade-offs: If the user hasn't specified a template or mode, present the available templates (e.g., Autopilot, Standard Regional, GPU Inference, AI Hypercompute) and explain key trade-offs (Cost vs. Availability, Autopilot vs. Standard node management).
  4. Configure networking: auto-create subnet (default) or bring-your-own.
  5. Review golden path settings: present the default configuration block (gcloud command or create_cluster JSON payload) and confirm with the user before creation.
  6. Create: Use MCP create_cluster tool or gcloud CLI.
  7. Track: Use get_operation to monitor creation progress.
  8. Verify: Use get_cluster with readMask="*" to confirm golden path settings applied.

Mode Selection

CriteriaAutopilot (Golden Path)Standard
Node managementGoogle-managedSelf-managed
PricingPay per pod resourcePay per node (VM)
: : request : :
Node customizationVia ComputeClassesFull control
DaemonSetsAllowed (withFull control
: : restrictions) : :
GPU/TPUSupported viaSupported via node pools
: : ComputeClasses : :
Best forMost production workloadsKernel tuning, custom OS,
: : : privileged workloads :

Rule: Default to Autopilot unless the customer has a specific requirement that Autopilot cannot satisfy.

Best Practices

When guiding the user or generating configurations, adhere to these GKE best practices:

Security & Networking
  1. Private Clusters: Default to private clusters (enablePrivateNodes: true) with a private control plane and restricted public endpoints (enable-master-authorized-networks) to minimize attack surface.
  2. VPC-Native Networking: Use VPC-native clusters (useIpAliases: true / --enable-ip-alias) to enable alias IP ranges and pod-level firewall rules.
  3. Workload Identity: Prefer Workload Identity (workloadPool: <PROJECT_ID>.svc.id.goog) for securely granting GKE workloads access to Google Cloud services instead of static service account keys.
  4. Shielded GKE Nodes: Enable Shielded GKE Nodes (--enable-shielded-nodes, --enable-secure-boot) against rootkits and bootkits.
  5. Least Privilege (RBAC): Institute strict Role-Based Access Control limits (scoped-rbs-bindings).
Cost Optimization
  1. Autoscaling: Enable Cluster Autoscaler and Horizontal/Vertical Pod Autoscaler (--enable-autoscaling, --enable-vertical-pod-autoscaling) to adjust resources based on demand.
  2. Right-Sizing & Spot VMs: Choose appropriate machine types and node counts. Consider Spot VMs (--spot) for fault-tolerant, non-critical batch or inference workloads.
High Availability & Reliability
  1. Regional Clusters: Use Regional Clusters for production environments to ensure control plane replication across multiple zones (--region instead of --zone). Note: Standard regional creates nodes across 3 zones by default.
  2. Pod Disruption Budgets: Recommend setting Pod Disruption Budgets for application stability during node maintenance.
  3. Release Channels: Subscribe to a release channel (REGULAR or STABLE) for automated, safer cluster upgrades.

Templates

1. Golden Path Autopilot (Production)

This is the default. All settings match ../gke-golden-path/assets/golden-path-autopilot.yaml.

Via gcloud:

bash
gcloud container clusters create-auto <CLUSTER_NAME> \
  --region <REGION> \
  --project <PROJECT_ID> \
  --release-channel regular \
  --enable-private-nodes \
  --enable-master-authorized-networks \
  --enable-dns-access \
  --enable-secret-manager \
  --secret-manager-rotation-interval=120s \
  --scoped-rbs-bindings \
  --monitoring=SYSTEM,API_SERVER,SCHEDULER,CONTROLLER_MANAGER,STORAGE,POD,DEPLOYMENT,STATEFULSET,DAEMONSET,HPA,CADVISOR,KUBELET,DCGM \
  --quiet

Via MCP (create_cluster):

json
{
  "parent": "projects/<PROJECT_ID>/locations/<REGION>",
  "cluster": {
    "name": "<CLUSTER_NAME>",
    "autopilot": { "enabled": true },
    "privateClusterConfig": { "enablePrivateNodes": true },
    "masterAuthorizedNetworksConfig": {
      "privateEndpointEnforcementEnabled": true
    },
    "releaseChannel": { "channel": "REGULAR" },
    "secretManagerConfig": {
      "enabled": true,
      "rotationConfig": { "enabled": true, "rotationInterval": "120s" }
    },
    "rbacBindingConfig": {
      "enableInsecureBindingSystemAuthenticated": false,
      "enableInsecureBindingSystemUnauthenticated": false
    }
  }
}
Show full SKILL.md (310 more words)Show less
2. Autopilot Dev/Test

Relaxes some golden path defaults for cost savings and easier access in non-production.

Via gcloud:

bash
gcloud container clusters create-auto <CLUSTER_NAME> \
  --region <REGION> \
  --project <PROJECT_ID> \
  --release-channel rapid \
  --quiet

Via MCP (create_cluster):

json
{
  "parent": "projects/<PROJECT_ID>/locations/<REGION>",
  "cluster": {
    "name": "<CLUSTER_NAME>",
    "autopilot": { "enabled": true },
    "releaseChannel": { "channel": "RAPID" }
  }
}

Warning: This does not apply golden path security hardening. Suitable for dev/test only.

3. Standard Regional (High Availability / Custom Requirements)

Best when Autopilot cannot be used (e.g., custom kernel tuning, specific node OS requirements). Creates 3 nodes across zones by default.

Via gcloud:

bash
gcloud container clusters create <CLUSTER_NAME> \
  --region <REGION> \
  --project <PROJECT_ID> \
  --num-nodes 3 \
  --machine-type e2-standard-4 \
  --disk-type pd-balanced \
  --enable-autoscaling --min-nodes 1 --max-nodes 10 \
  --enable-shielded-nodes --enable-secure-boot \
  --workload-pool=<PROJECT_ID>.svc.id.goog \
  --enable-private-nodes \
  --enable-master-authorized-networks \
  --enable-vertical-pod-autoscaling \
  --enable-dataplane-v2 \
  --release-channel regular \
  --quiet

Via MCP (create_cluster):

json
{
  "parent": "projects/<PROJECT_ID>/locations/<REGION>",
  "cluster": {
    "name": "<CLUSTER_NAME>",
    "initialNodeCount": 3,
    "nodeConfig": {
      "machineType": "e2-standard-4",
      "diskType": "pd-balanced",
      "diskSizeGb": 100,
      "oauthScopes": ["https://www.googleapis.com/auth/cloud-platform"],
      "shieldedInstanceConfig": {
        "enableSecureBoot": true,
        "enableIntegrityMonitoring": true
      },
      "workloadMetadataConfig": {
        "mode": "GKE_METADATA"
      }
    },
    "privateClusterConfig": { "enablePrivateNodes": true },
    "releaseChannel": { "channel": "REGULAR" },
    "workloadIdentityConfig": {
      "workloadPool": "<PROJECT_ID>.svc.id.goog"
    }
  }
}
4. GPU Inference & AI Workloads (L4 / ComputeClass)

Best for: AI/ML Inference, small model serving. Can be provisioned via Autopilot + ComputeClass or via Standard node pool with g2-standard-4 (nvidia-l4). Note: Requires g2-standard-4 quota.

Autopilot ComputeClass / GIQ approach:

bash
# 1. Create golden path cluster (same as template 1)
gcloud container clusters create-auto <CLUSTER_NAME> \
  --region <REGION> --project <PROJECT_ID> \
  --enable-private-nodes --enable-master-authorized-networks \
  --enable-dns-access --enable-secret-manager --scoped-rbs-bindings \
  --quiet

# 2. Apply GPU ComputeClass (see gke-compute-classes.md)
kubectl apply -f gpu-compute-class.yaml

# 3. Or use GIQ for inference (see gke-inference.md)
gcloud container ai profiles manifests create \
  --model=gemma-2-9b-it --model-server=vllm --accelerator-type=nvidia-l4 --quiet > inference.yaml
kubectl apply -f inference.yaml

Standard Node Pool approach via MCP (create_cluster):

json
{
  "parent": "projects/<PROJECT_ID>/locations/<REGION>",
  "cluster": {
    "name": "<CLUSTER_NAME>",
    "initialNodeCount": 1,
    "nodeConfig": {
      "machineType": "g2-standard-4",
      "accelerators": [
        {
          "acceleratorCount": "1",
          "acceleratorType": "nvidia-l4"
        }
      ],
      "diskSizeGb": 100,
      "oauthScopes": ["https://www.googleapis.com/auth/cloud-platform"]
    }
  }
}
5. AI Hypercompute (A3 HighGPU / Large Model Serving)

Best for: Large-scale LLM / AI model training and hypercompute inference. Note: High hourly cost and strict quota requirements (a3-highgpu-8g / nvidia-h100-80gb-hbm3).

Via gcloud:

bash
gcloud container clusters create <CLUSTER_NAME> \
  --region <REGION> \
  --project <PROJECT_ID> \
  --num-nodes 1 \
  --machine-type a3-highgpu-8g \
  --accelerator type=nvidia-h100-80gb-hbm3,count=8 \
  --disk-size 200 \
  --scopes https://www.googleapis.com/auth/cloud-platform \
  --workload-pool=<PROJECT_ID>.svc.id.goog \
  --release-channel regular \
  --quiet

Via MCP (create_cluster):

json
{
  "parent": "projects/<PROJECT_ID>/locations/<REGION>",
  "cluster": {
    "name": "<CLUSTER_NAME>",
    "initialNodeCount": 1,
    "nodeConfig": {
      "machineType": "a3-highgpu-8g",
      "accelerators": [
        {
          "acceleratorCount": "8",
          "acceleratorType": "nvidia-h100-80gb-hbm3"
        }
      ],
      "diskSizeGb": 200,
      "oauthScopes": ["https://www.googleapis.com/auth/cloud-platform"]
    }
  }
}

Instructions

  • ALWAYS ask for project_id if not in context.
  • ALWAYS ask for region (or location).
  • ALWAYS ask for a unique cluster_name.
  • DEFAULT to golden path Autopilot unless customer specifies otherwise or has custom node/kernel/hypercompute requirements.
  • ALWAYS WARN when deviating to GKE Standard, highlighting that it deviates from the golden path and explaining the added operational/management overhead (manually managing node pools, upgrades, and autoscaling).
  • EXPLAIN TRADE-OFFS when presenting templates or mode choices to the user if they haven't specified one (e.g., Autopilot vs Standard, Cost vs Availability).
  • PRESENT THE CONFIGURATION block (gcloud command or JSON payload) and ask for confirmation before calling any creation tool.
  • WARN about Day-0 decisions (networking, private nodes) that are hard to change later.
  • WARN explicitly about cost and quota requirements when the user selects GPU (g2-standard-4, a3-highgpu-8g), TPU, or multi-region/regional clusters (--region defaults to 3 zones).
  • When using MCP create_cluster, the cluster.name parameter should be the short name (e.g., my-cluster), not the full resource path (projects/<PROJECT_ID>/locations/<REGION>/clusters/<CLUSTER_NAME>). The parent parameter defines the scope (projects/<PROJECT_ID>/locations/<REGION>).

© 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

Just SKILL.md in skills/cloud/gke-cluster-creation of google/skills.

Open the folder on GitHubat commit 7d97937

Compare with similar skills

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Works with

Categories

Questions about Gke Cluster Creation

What does Gke Cluster Creation do?

Plans and executes GKE cluster creation, provisioning, and production readiness audits using pre-defined templates (Autopilot, Standard Regional, GPU/AI Inference, AI Hypercompute). Gke Cluster Creation is an agent skill from google/skills, published by the product's own GitHub organization. Plans and executes GKE cluster creation, provisioning, and production readiness audits using pre-defined templates (Autopilot, Standard Regional, GPU/AI Inference, AI Hypercompute).

When should I use Gke Cluster Creation?

Gke Cluster Creation fits situations like: creating GKE clusters; provisioning GKE environments; selecting cluster modes; auditing GKE clusters.

How do I install Gke Cluster Creation in Claude Code?

Run `npx skills add google/skills --skill gke-cluster-creation -a claude-code`. Or copy the skill folder (skills/cloud/gke-cluster-creation in google/skills) into .claude/skills/gke-cluster-creation in your project. Claude Code loads it when a task matches its description.

How do I install Gke Cluster Creation in Codex?

Run `npx skills add google/skills --skill gke-cluster-creation -a codex`. Or copy the skill folder (skills/cloud/gke-cluster-creation in google/skills) into .agents/skills/gke-cluster-creation in your project. Codex loads it when a task matches its description.

Can I use Gke Cluster Creation 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-cluster-creation -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-cluster-creation, .gemini/skills/gke-cluster-creation, .github/skills/gke-cluster-creation and .opencode/skills/gke-cluster-creation in your project.

What does Gke Cluster Creation need to run?

Going by SKILL.md and its folder, Gke Cluster Creation needs the command-line tools its instructions call (gcloud and kubectl).

Does Gke Cluster Creation access the network?

SKILL.md names 1 domain. In commands or code: googleapis.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Gke Cluster Creation 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 Cluster Creation use?

Gke Cluster Creation 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 Cluster Creation use?

About 3.1k tokens (SKILL.md is roughly 12k 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 Cluster Creation?

Skills that share tags, products or a category with Gke Cluster Creation: Devops (nicepkg/auto-company, 192 stars), Kcli Cluster Deployment (karmab/kcli, 653 stars), Google Agents CLI Publish (pifferologo/cloud-agents-cli, 129 stars) and Deploying (GoogleCloudPlatform/race-condition, 234 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gke Cluster Creation?

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.