gke-basics

name	gke-basics
description	Plans, creates, and configures production-ready Google Kubernetes Engine (GKE) clusters using the golden path Autopilot configuration. Covers networking, security, observability, scaling, cost optimization, and AI/ML inference on GKE. Use when this capability is needed.
metadata	{"author":"davila7"}

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.

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Source: davila7/claude-code-templates — distributed by TomeVault.