google/skills

gke-golden-path

Provides GKE golden path configuration defaults, production readiness checklists, and cluster default patterns.

View source

How to use

  1. Copy the installation command and review the source package before installing it in your own agent environment.
  2. Check the source document for required tools, credentials and supported hosts.
  3. Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/google/skills --skill gke-golden-path

Before you start

  • Installation does not include a model subscription, API allowance or permission to access your files.
  • Repository stars and installation counts describe activity, not an independent quality or security test.
Original skill document

Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.

GKE Golden Path Configuration

The golden path is the recommended Autopilot configuration for production clusters. It defines sensible defaults — when the user requests different settings, apply them and note relevant trade-offs. For setting up autoscaling specifically, use gke-cluster-autoscaler for node autoscaling or gke-workload-scaling for workload autoscaling (HPA/VPA).

MCP Tools: get_cluster, create_cluster, update_cluster

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read Usage Attribution. For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix gcloud with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:gke-golden-path)" and append agent-skills/1.0 (skill:gke-golden-path) to User-Agent headers.

Rules

  1. Default to the golden path. Use golden path values unless the user

requests otherwise. When deviating, note trade-offs but respect the user's choice.

  1. Day-0 vs Day-1. Flag Day-0 decisions (networking, private nodes,

subnets, IP allocation) prominently — they are hard/impossible to change after creation.

  1. Tool preference: MCP > gcloud > kubectl. MCP is preferred as it directly

interfaces with GKE APIs with structured data, reducing shell syntax errors and parsing ambiguities. See the gke-basics skill's CLI reference for full coverage matrix and override options. If the user says "use gcloud" or "use kubectl", respect that for the session.

  1. Document decisions and rationale, especially for Day-0 choices and

golden path deviations.

Required Inputs

If the user is unsure, use golden path defaults.

  • Project ID (required)
  • Region (required, e.g., us-central1)
  • Cluster name (required)
  • Environment type: dev/test or production (defaults to production)
  • Networking: bring-your-own VPC/subnet or auto-create (default:

auto-create)

  • Scale expectations: expected node/pod count, workload types
  • Cost constraints: Spot VM tolerance, budget considerations

Always-Apply Defaults

Recommended best practices applied by default. If the user requests a different setting, apply it and briefly note the security or operational trade-off.

SettingGolden Path Value
autopilot.enabledtrue
privateClusterConfig.enablePrivateNodestrue
masterAuthorizedNetworksConfig.privateEndpointEnforcementEnabledtrue
secretManagerConfig.enabled + rotationInterval: 120strue
rbacBindingConfig.enableInsecureBinding*false (both)
workloadIdentityConfig.workloadPoolenabled
networkConfig.datapathProviderADVANCED_DATAPATH
networkConfig.dnsConfig.clusterDnsCLOUD_DNS
autoscaling.autoscalingProfileOPTIMIZE_UTILIZATION
verticalPodAutoscaling.enabledtrue
monitoringConfig componentsSYSTEMCOMPONENTS, STORAGE, POD, DEPLOYMENT, STATEFULSET, DAEMONSET, HPA, JOBSET, CADVISOR, KUBELET, DCGM, APISERVER, SCHEDULER, CONTROLLERMANAGER
loggingConfig componentsSYSTEM_COMPONENTS, WORKLOADS (enabled by default)
advancedDatapathObservabilityConfig.enableMetricstrue
nodeConfig.shieldedInstanceConfig.enableSecureBoottrue
nodeConfig.workloadMetadataConfig.modeGKE_METADATA
nodeConfig.gcfsConfig.enabled / gvnic.enabledtrue / true
addonsConfig.statefulHaConfig.enabledtrue
Storage CSI drivers (Filestore, GCS FUSE, Parallelstore)enabled
Pod Security Standardsrestricted on production namespaces

Customer-Configurable Settings

These have golden path defaults but customers may deviate with valid justification. Ask before changing.

SettingDefaultWhy Deviate
dnsEndpointConfig.allowExternalTraffictrueRestrict if cluster only accessed from within VPC
autoIpamConfig / createSubnetworktrue / trueCustomer has pre-existing VPC/subnets
maxPodsPerNode48 (this golden path's choice)Halves per-node IP consumption (/25 instead of /24). Not a GKE default (Standard defaults to 110, Autopilot to 32); raise for high pod-density at the cost of more CIDR space
subnetworkauto-createdCustomer brings existing subnets
Release channel + maintenance windowsREGULAR channel with a recurring maintenance windowAdd targeted maintenance exclusions (keep under ~6 months) only for critical freezes — see the gke-upgrades skill
nodeConfig.bootDisk.diskTypepd-balancedpd-ssd for I/O-intensive, pd-standard for cost
Note: Autopilot selects node machine types automatically (e.g., ek-standard-8 may appear in describe output); the machine type is not customer-configurable in Autopilot. Steer workload placement via ComputeClasses instead.

Guardrails

  • Do not request or output secrets (tokens, keys, service account JSON).
  • Resolve project/cluster context from the conversation, MCP tools, or

gcloud config get-value project; ask the user only if it cannot be resolved.

  • For Day-0 decisions, always ask clarifying questions before proceeding.
  • For Day-1 features, propose golden path defaults with trade-offs and let the

customer confirm.

  • Do not promise zero downtime — see Upgrade Disruption below for what to

advise instead.

  • When auditing existing clusters, compare against golden path and report

deviations with severity and remediation.

Upgrade Disruption

Never promise zero downtime for node upgrades, on any configuration. Node upgrades cordon and drain nodes, which evicts Pods. Draining honors PodDisruptionBudgets and terminationGracePeriodSeconds for up to one hour, after which GKE forcefully evicts the remaining Pods so the upgrade can proceed. A PDB narrows the window; it cannot veto the upgrade. Say so plainly rather than implying the disruption can be eliminated.

What to recommend, all four — not a subset:

  • PodDisruptionBudgets with minAvailable set so eviction cannot take the

last healthy replica. A PDB that can never be satisfied stalls the drain for an hour and then loses anyway.

  • At least 2 replicas, spread across zones with topology spread

constraints. A single-replica Deployment has downtime by definition.

  • Readiness probes that reflect real serving health, so traffic drains

before the Pod dies.

  • Surge upgrade settings on the node pool. Surge is the default strategy;

the default is maxSurge=1, maxUnavailable=0 — one extra node is created and made ready before an old one is drained.

SettingControlsDefault
maxSurgeAdditional nodes added per zone during the upgrade1
maxUnavailableNodes simultaneously unavailable per zone0

Nodes upgraded at once is the sum of the two, capped at 20 (Autopilot) and 100 (Standard). Multi-zone node pools upgrade one zone at a time. Raising maxUnavailable trades availability for speed; raising maxSurge trades cost for availability.

Caveat: externalTrafficPolicy: Local does not work with parallel node drains, so it constrains aggressive surge configurations.

For rollback procedures and maintenance windows, see the gke-upgrades skill.

Golden Path Config

See golden-path-autopilot.yaml for the full cluster-level policy settings.

from this repository

More skills

All skills
google
Community

cloud-logging-query-generation

Generates Logging Query Language (LQL) queries for Cloud Logging on Google Cloud from natural language. Use this skill when you need to query log data or when you are debugging issues. You can filter log data by Google Cloud service. Don't use this skill to query other databases, such as SQL or Cloud Spanner.

installs
1
GitHub stars
21.6K
Updated
Oct 25
google
Community

google-cloud-solution-architecture

Interactively discovers requirements and designs holistic, multi-product system architectures, solution blueprints, and deployment recommendations for complex workloads on Google Cloud. Use when designing end-to-end cloud solutions, selecting and integrating Google Cloud services, generating architecture diagrams, or conducting requirements discovery for new cloud workloads or migrations. Don't use for single-product tasks (use product-specific skills), initial onboarding or authentication (use

installs
1
GitHub stars
21.6K
Updated
Oct 25
google
Community

workload-manager-basics

Use this skill to manage Google Cloud Workload Manager evaluations, rules, scanned resources, and validation results by using public client libraries and the REST API. Use when you need to inspect workload best-practice rules, create and run evaluations for Google Cloud general best practices, SAP, SQL Server, or custom organizational rules, review violations, export results to BigQuery, or automate Workload Manager through client libraries because no service-specific public CLI or MCP server is

installs
1
GitHub stars
21.6K
Updated
Oct 25
google
Community

cloud-run-alert-configuration

Configures best-practice, high-signal alerting policies for Cloud Run resources on Google Cloud (services, jobs, and worker pools) based on seasoned SRE practices. Use when analyzing, recommending, writing, or deploying Terraform PromQL alerting policies to monitor Cloud Run error rates (4xx/5xx), request latency, container instance saturation (warning/critical), container CPU/memory utilization and allocation, billable instance time, job execution status, and worker pool queue backlog. Don't us

installs
3
GitHub stars
21.6K
Updated
Oct 25