gke-cluster-creation
GitHub用于规划、执行 GKE 集群创建及生产就绪审计。默认使用 Autopilot 模式,涵盖从发现上下文、配置网络到验证设置的全流程。不用于应用部署。
Trigger Scenarios
Install
npx skills add google/skills --skill gke-cluster-creation -g -y
SKILL.md
Frontmatter
{
"name": "gke-cluster-creation",
"metadata": {
"category": "Containers"
},
"description": "Plans and executes GKE cluster creation, provisioning, and production readiness audits. Use when creating GKE clusters, provisioning GKE environments, or auditing GKE clusters. Don't use for application onboarding or deployment configuration (use gke-app-onboarding instead)."
}
GKE Cluster Creation
This reference guides creating GKE clusters. 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
- Discover context: Use
list_clustersto see existing clusters. Usegcloud config get-value projectif project unknown. - Gather inputs: project_id, region, cluster_name, environment type
- Select mode: Autopilot (default) vs Standard
- Configure networking: auto-create subnet (default) or bring-your-own
- Review golden path settings: present the config and confirm with user
- Create: Use MCP
create_clustertool. Fall back togcloudCLI only if MCP is unavailable. - 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.
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 \
--quiet
Via 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.
gcloud container clusters create-auto <CLUSTER_NAME> \
--region <REGION> \
--project <PROJECT_ID> \
--release-channel rapid \
--quiet
Warning: This does not apply golden path security hardening. Suitable for dev/test only.
3. Standard Regional (When Autopilot is Not an Option)
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
4. GPU/AI Workloads (Autopilot with ComputeClass)
Create a golden path Autopilot cluster, then apply a ComputeClass for GPU workloads:
# 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
Instructions
- ALWAYS ask for
project_idif not in context - ALWAYS ask for
region - ALWAYS ask for a unique
cluster_name - DEFAULT to golden path Autopilot unless customer specifies otherwise
- WARN about Day-0 decisions (networking, private nodes) that are hard to change later
- WARN about cost for GPU or multi-region clusters
- When using MCP
create_cluster, thecluster.nameshould be the short name (e.g.,my-cluster), not the full resource path
Version History
- aabe37a Current 2026-07-05 15:29


