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google/gke-cluster-creation

google

gke-cluster-creation

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).

New~1.5kUpdated Jun 28, 2026

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

  1. Discover context: Use list_clusters to see existing clusters. Use gcloud config get-value project if project unknown.
  2. Gather inputs: project_id, region, cluster_name, environment type
  3. Select mode: Autopilot (default) vs Standard
  4. Configure networking: auto-create subnet (default) or bring-your-own
  5. Review golden path settings: present the config and confirm with user
  6. Create: Use MCP create_cluster tool. Fall back to gcloud CLI only if MCP is unavailable.
  7. Track: Use get_operation to monitor creation progress
  8. Verify: Use get_cluster with readMask="*" 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_id if 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, the cluster.name should be the short name (e.g., my-cluster), not the full resource path
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Overall Score

76/100

Grade

B

Good

Safety

82

Quality

72

Clarity

82

Completeness

68

Summary

This skill guides AI agents through creating Google Kubernetes Engine (GKE) clusters, with emphasis on the "golden path Autopilot" production-ready configuration. It provides templates for different deployment modes (Autopilot vs. Standard), environment types (production, dev/test), and specialized workloads (GPU/AI), along with structured workflows for discovery, configuration review, creation, and verification using GCP MCP tools.

Detected Capabilities

GCP MCP tool invocation (list_clusters, create_cluster, get_cluster, list_operations, get_operation)gcloud CLI command executionkubectl apply (for ComputeClass and GPU manifests)GCP project discovery and region/cluster context gatheringConfiguration templating and JSON API payload construction

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

create gke clusterprovision gke autopilotdeploy gpu clusteraudit gke configurationsetup kubernetes cluster

Risk Signals

INFO

gcloud and MCP tool invocation for cluster creation

Workflow section, templates section
WARNING

References to external assets (gke-golden-path/assets/golden-path-autopilot.yaml, gke-compute-classes.md, gke-inference.md)

Template 1, Template 4
INFO

kubectl apply used for GPU ComputeClass and inference manifests

Template 4

Referenced Domains

External domains referenced in skill content, detected by static analysis.

www.apache.org

Use Cases

  • Create production-ready GKE Autopilot clusters with security hardening and monitoring enabled
  • Provision dev/test GKE clusters with relaxed settings for cost savings
  • Set up Standard mode GKE clusters for workloads requiring kernel tuning or custom OS
  • Deploy GPU-accelerated Autopilot clusters with ComputeClass configurations
  • Audit and verify GKE cluster configurations against golden path standards

Quality Notes

  • Strength: Clear mode selection matrix explicitly compares Autopilot vs. Standard with decision criteria
  • Strength: Well-structured workflow with 8 distinct phases (discover, gather, select, configure, review, create, track, verify)
  • Strength: Multiple templates provided for common scenarios (production, dev/test, standard, GPU)
  • Strength: Explicit guardrails documented ('ALWAYS ask for project_id/region/cluster_name', 'DEFAULT to Autopilot', 'WARN about Day-0 decisions')
  • Strength: MCP tool usage prioritized over CLI with documented fallback strategy
  • Weakness: External asset references (gke-golden-path/assets/golden-path-autopilot.yaml, gke-compute-classes.md, gke-inference.md) are mentioned but files not provided in manifest; agent cannot verify golden path compliance without these
  • Weakness: No documented error handling for creation failures (e.g., quota exceeded, invalid region, insufficient permissions)
  • Weakness: No edge cases documented (e.g., what if cluster name already exists, how to handle partial failures during multi-region setup)
  • Weakness: No guidance on networking decisions beyond mentioning 'auto-create subnet (default) or bring-your-own'; private nodes are enabled by default but implications not explained
  • Weakness: Cost estimation absent; GPU/multi-region warning is mentioned but no guidance on budgeting or cost optimization
Model: claude-haiku-4-5-20251001Analyzed: Jun 28, 2026

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Version History

  1. v1.1

    Content updated

    ✦ AINo detectable behavioral changes in SKILL.md, frontmatter, or supporting files.

    2026-06-28

    Latest
  2. v1.0

    2026-06-24

    View This VersionInitial version

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