Skill Introduction

Overview

Plan, create, and configure production-ready Google Kubernetes Engine (GKE) clusters using the golden path Autopilot configuration. Covers Day-0 checklist, Autopilot vs Standard, networking (private clusters, VPC-native, Gateway API), security (Workload Identity, Secret Manager, RBAC hardening), observability, scaling, cost optimization, and AI/ML inference. WHEN: create GKE cluster, provision GKE environment, design GKE networking, secure GKE, optimize GKE cost, GKE autoscaling, GKE inference,

Core value

Turns reusable Development know-how into an installable skill, helping users complete github, community, mcp work faster.

Target users

Developers, testers, and maintainers who handle Development tasks in Focus Code.
Teams that already trust workflows or content from google.
Users who want standardized prompts, steps, or conventions instead of repeating setup work.

Best practices

Read the skill content first to confirm required inputs, expected outputs, and dependencies.
Try it on a small task before relying on it for critical work.
Add project-specific constraints such as coding style, target platform, test expectations, and delivery format.
For external sources, verify the source link, version, and recent maintenance activity.

Best use cases

Tasks related to github, community, mcp that need a reusable execution flow.
Converting a community repo, team convention, or personal workflow into day-to-day assistance.
Starting from a proven skill instead of writing prompts or procedures from scratch.

Limits and boundaries

Results depend on the quality of the original skill content and may need human correction.
It does not replace code review, tests, security review, or professional judgment.
External tools, APIs, account permissions, and local dependencies still need separate setup.

Differentiation

Structured around Development, making it easier to discover and reuse than loose prompt snippets.
Marked as GitHub, which helps users judge trust and maintenance expectations.
Keeps the original source link available for repository, documentation, or discussion follow-up.
Tagged with github, community, mcp, so it can be filtered by concrete task intent.

Install and use

Install

Copy Install Command

focus install gke-basics-ef722c

View source

Detail Preview

SKILL.md

Primary filemarkdown5 KB

name: gke-basics license: Apache-2.0 metadata: author: Google Cloud version: "1.0.0" description: "Plan, create, and configure production-ready Google Kubernetes Engine (GKE) clusters using the golden path Autopilot configuration. Covers Day-0 checklist, Autopilot vs Standard, networking (private clusters, VPC-native, Gateway API), security (Workload Identity, Secret Manager, RBAC hardening), observability, scaling, cost optimization, and AI/ML inference. WHEN: create GKE cluster, provision GKE environment, design GKE networking, secure GKE, optimize GKE cost, GKE autoscaling, GKE inference, GKE upgrade, GKE observability, GKE multi-tenancy, GKE batch, GKE HPC, GKE compute class."

Google Kubernetes Engine (GKE) Basics

GKE is a managed Kubernetes platform on Google Cloud for deploying, scaling, and operating containerized applications. This skill defaults to the golden path Autopilot configuration — see gke-golden-path.md for defaults, rules, and guardrails.

Quick Start

gcloud services enable container.googleapis.com --quiet
gcloud container clusters create-auto my-cluster --region=us-central1 --quiet
gcloud container clusters get-credentials my-cluster --region=us-central1 --quiet
kubectl create deployment hello-server \
  --image=us-docker.pkg.dev/google-samples/containers/gke/hello-app:1.0

Reference Directory

Load the relevant reference based on trigger keywords. Prefer the most specific match; if ambiguous, ask the user to clarify.

Scenario	Trigger Keywords	Reference
Core Concepts	Autopilot vs Standard, architecture, pricing, what is GKE	core-concepts.md
Golden Path & Defaults	golden path, Day-0 checklist, production defaults, cluster defaults	gke-golden-path.md
Cluster Creation	create cluster, new cluster, provision GKE	gke-cluster-creation.md
Networking	private cluster, VPC, subnet, Gateway API, DNS, ingress, egress, datapath	gke-networking.md
Security & IAM	Workload Identity, Secret Manager, RBAC, Binary Auth, hardening, audit, gVisor, IAM roles	gke-security.md
Scaling	HPA, VPA, autoscaler, autoscaling, NAP, scale pods, scale nodes	gke-scaling.md
Compute Classes	ComputeClass, machine family, Spot fallback, GPU node pool, node selection	gke-compute-classes.md
Cost	cost, savings, Spot VMs, rightsizing, CUD, optimize spend, budget	gke-cost.md
AI/ML Inference	inference, model serving, LLM, GPU, TPU, GIQ, vLLM	gke-inference.md
Upgrades	upgrade, maintenance window, release channel, patching, version	gke-upgrades.md
Observability	monitoring, logging, Prometheus, Grafana, metrics, alerts, dashboards	gke-observability.md
Multi-tenancy	multi-tenant, namespace isolation, team access, enterprise, RBAC planning	gke-multitenancy.md
Batch & HPC	batch, HPC, job queue, high performance, MPI, parallel	gke-batch-hpc.md
App Onboarding	containerize, deploy app, Dockerfile, onboard, migrate to GKE	gke-app-onboarding.md
Backup & DR	backup, restore, disaster recovery, CMEK	gke-backup-dr.md
Storage	storage, PVC, persistent volume, StorageClass, Filestore, GCS FUSE	gke-storage.md
Reliability	PDB, health probe, liveness, readiness, topology spread, graceful shutdown	gke-reliability.md
Client Libraries	client library, client-go, kubernetes python, kubernetes java, kubernetes SDK	client-library-usage.md
Infrastructure as Code	Terraform, IaC, HCL, infrastructure as code	iac-usage.md
MCP Server	MCP tools, MCP server, MCP setup	mcp-usage.md
CLI / Tools	gcloud, kubectl, commands, how to	cli-reference.md
Production Audit	production readiness, compliance, golden path check	gke-cluster-creation.md

If you need product information not found in these references, use the Developer Knowledge MCP server search_documents tool.

gke basics