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Gke Basics

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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 inferenc...

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  • Added September 10, 2026
devopspythongojavabashnoderailsdockerkubernetesterraformapi

Works with

  • cli
  • api
  • mcp

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Scanned September 10, 2026

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SKILL.md
---
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](./references/gke-golden-path.md) for defaults, rules, and guardrails.

## Quick Start

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

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

Files in this skill

  • SKILL.md4.6 KB
  • assets/default-deny-netpol.yaml154 B
  • assets/golden-path-autopilot.yaml3.6 KB
  • assets/hpa-example.yaml495 B
  • assets/vpa-example.yaml469 B
  • assets/workload-identity-pod.yaml793 B
  • references/cli-reference.md10.6 KB
  • references/client-library-usage.md2.3 KB
  • references/core-concepts.md2.8 KB
  • references/gke-app-onboarding.md4.3 KB
  • references/gke-backup-dr.md2.4 KB
  • references/gke-batch-hpc.md4 KB
  • references/gke-cluster-creation.md5 KB
  • references/gke-compute-classes.md4.5 KB
  • references/gke-cost.md4.9 KB
  • references/gke-golden-path.md4.4 KB
  • references/gke-inference.md5.3 KB
  • references/gke-multitenancy.md3.9 KB
  • references/gke-networking.md5.3 KB
  • references/gke-observability.md6.4 KB

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