Gke Cluster Creation
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Written by AutoGineer from the skill’s own files, not by the author
This skill assists users in planning and provisioning GKE clusters by offering pre-defined templates for various use cases, including Autopilot, Standard Regional, GPU/AI inference, and AI hypercompute. It guides users through mode selection, network configuration, and security best practices, ensuring clusters are created according to defined standards. The skill includes specific instructions for using gcloud CLI or MCP tools to manage cluster lifecycle operations while maintaining proper usage attribution.
What it does
- Provides templates for GKE Autopilot and Standard cluster configurations.
- Guides users through selecting cluster modes based on management and workload requirements.
- Generates gcloud commands and MCP JSON payloads for cluster creation.
- Enforces security best practices like private nodes, VPC-native networking, and Workload Identity.
- Monitors cluster creation progress using operation tracking tools.
- Applies required usage attribution tags to CLI and API requests.
When to use it
- When you need to provision a new GKE cluster in a Google Cloud project.
- When you require guidance on selecting between Autopilot and Standard GKE modes.
- When setting up specialized clusters for AI/ML workloads or GPU inference.
- When you need to ensure cluster creation follows security and networking best practices.
Advantages
- Standardizes cluster creation using proven templates.
- Reduces manual configuration errors by providing pre-validated command and JSON structures.
- Ensures consistent application of security and cost-optimization settings.
- Simplifies the selection process for complex infrastructure requirements like GPU or hypercompute.
Usage examples
- “Help me create a new production GKE cluster.”
- The agent will ask for the project ID, region, and cluster name, then recommend the Autopilot golden path template with security hardening enabled.
- “I need a GKE cluster for AI inference using L4 GPUs.”
- The agent will present the GPU inference template, explain the requirements for g2-standard-4 machine types, and provide the configuration for a standard node pool.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google/gke-cluster-creation/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip gke-cluster-creation.zip -d .agents/skills
Documentation by Google
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
Attribution
MUST READ: Before executing any gcloud, REST API, or MCP command, you
MUST read Usage Attribution. For commands executed
directly in the terminal (never in generated scripts, Terraform, or user code),
prefix gcloud with
CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:gke-cluster-creation)"
and append agent-skills/1.0 (skill:gke-cluster-creation) to User-Agent headers.
Workflow
Discover context: Use
list_clustersto see existing clusters. Usegcloud config get-value projectif project unknown.Gather inputs:
project_id,location(region or zone),cluster_name, environment type. If missing essential details, ask the user before taking action.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).
Configure networking: auto-create subnet (default) or bring-your-own.
Review golden path settings: present the default configuration block (
gcloudcommand orcreate_clusterJSON payload) and confirm with the user before creation.Create: Use MCP
create_clustertool orgcloudCLI.Track: Use
get_operationto monitor creation progress.Verify: Use
get_clusterwithreadMask="*"to confirm golden path settings applied.
Mode Selection
| Criteria | Autopilot (Golden Path) | Standard |
|---|---|---|
| Node management | Google-managed | Self-managed |
| Pricing | Pay per pod resource | Pay per node (VM) |
| : : request : : | ||
| Node customization | Via ComputeClasses | Full control |
| DaemonSets | Allowed (with | Full control |
| : : restrictions) : : | ||
| GPU/TPU | Supported via | Supported via node pools |
| : : ComputeClasses : : | ||
| Best for | Most production workloads | Kernel 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
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.VPC-Native Networking: Use VPC-native clusters (
useIpAliases: true/--enable-ip-alias) to enable alias IP ranges and pod-level firewall rules.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.Shielded GKE Nodes: Enable Shielded GKE Nodes (
--enable-shielded-nodes,--enable-secure-boot) against rootkits and bootkits.Least Privilege (RBAC): Institute strict Role-Based Access Control limits (
scoped-rbs-bindings).
Cost Optimization
Autoscaling: Enable Cluster Autoscaler and Horizontal/Vertical Pod Autoscaler (
--enable-autoscaling,--enable-vertical-pod-autoscaling) to adjust resources based on demand.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
Regional Clusters: Use Regional Clusters for production environments to ensure control plane replication across multiple zones (
--regioninstead of--zone). Note: Standard regional creates nodes across 3 zones by default.Pod Disruption Budgets: Recommend setting Pod Disruption Budgets for application stability during node maintenance.
Release Channels: Subscribe to a release channel (
REGULARorSTABLE) 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:
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 \
--quietVia MCP (create_cluster):
{
"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
}
}
}2. Autopilot Dev/Test
Relaxes some golden path defaults for cost savings and easier access in non-production.
Via gcloud:
gcloud container clusters create-auto <CLUSTER_NAME> \
--region <REGION> \
--project <PROJECT_ID> \
--release-channel rapid \
--quietVia MCP (create_cluster):
{
"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:
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 \
--quietVia MCP (create_cluster):
{
"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:
# 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.yamlStandard Node Pool approach via MCP (create_cluster):
{
"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:
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 \
--quietVia MCP (create_cluster):
{
"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_idif 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 (
gcloudcommand 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 (--regiondefaults to 3 zones).When using MCP
create_cluster, thecluster.nameparameter should be the short name (e.g.,my-cluster), not the full resource path (projects/<PROJECT_ID>/locations/<REGION>/clusters/<CLUSTER_NAME>). Theparentparameter defines the scope (projects/<PROJECT_ID>/locations/<REGION>).
Files
- ATTRIBUTION.mdAdded by AutoGineer858 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md12.6 KiB
- references/attribution.md1.9 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
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Needs: Needs a command-line tool, Needs Node.js
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- google/skills · skills/cloud/gke-cluster-creation · commit 2194d3b
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