/gke-cluster-creation
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
$ npx -y skills add google/skills --skill gke-cluster-creation --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/gke-cluster-creation
Context preview
The summary Claude sees to decide when to auto-load this skill.
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
SKILL.md
gke-cluster-creation.SKILL.mdname: 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:
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
| 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
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:**
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":Read more
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: 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
| 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
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:**
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":This repository contains Agent Skills for Google products and technologies, including Google Cloud. This repository is under active development.
Repo: google/skills
Other skills on google-skills.
- /data-manager-api-audience-ingestion
Guides developers through managing (adding, removing, and clearing) audience members for Google products using the Data Manager API and its associated client libraries. Use this skill when the user wants to upload audience members, remove specific users, or clear/replace an
Open skill - /data-manager-api-event-ingestion
Guides developers through implementing event and conversion ingestion to Google products using the Data Manager API /v1/events/ingest endpoint and its associated client libraries. Use this skill when the user wants to upload offline conversions, enhanced conversions for leads,
Open skill - /data-manager-api-setup
Guides developers through client library installation and authentication setup steps for the Data Manager API. Use this skill when a user is getting started with the Data Manager API and needs to setup their local environment, install the client library, or setup access to the
Open skill - /google-ads-api-account-diagnostics
Diagnoses Google Ads account performance issues such as conversion loss (value or volume), low lead flow/volume, and lost impression share (opportunities) due to ad rank, bids, or budgets. Use when troubleshooting sudden performance drops, analyzing campaign impression share
Open skill - /google-ads-api-mcp-setup
Guides developers through downloading, configuring, and installing the official open-source Google Ads MCP Server. Use this skill when a user wants to connect their AI assistant (such as Gemini, Claude Code, or Cursor) to their Google Ads account to query campaigns or retrieve
Open skill - /google-ads-api-quickstart
Guides developers through Google Ads API quickstart: credential setup, choosing from 6 client libraries/REST, configuring environments, and running a "retrieve campaigns" script. Troubleshoots common setup errors: USER_PERMISSION_DENIED, login_customer_id issues, and
Open skill

