google/skills · Apache-2.0
gke-basics
Manages core GKE cluster provisioning, credentials, Autopilot vs Standard selection, and workload deployment. Use when creating GKE clusters, fetching kubectl credentials, configuring Workload Identity, or deciding between Autopilot and Standard modes. Don't use for specialized GKE networking (use gke-networking), advanced security hardening (use gke-platform-security or gke-workload-security), or cluster upgrades (use gke-upgrades).
Contents
GKE Basics & Critical Gotchas
Managed Kubernetes platform on Google Cloud. Defaults to Autopilot mode unless Standard is explicitly required.
Key Selection Rules: Autopilot vs. Standard
- Default to Autopilot for almost all workloads.
- Use Standard ONLY if:
- Custom node OS kernel parameters (
sysctl) are required. - Custom node taints or specific hardware node pools are required.
- DaemonSets require raw
hostPathmounts to the host OS filesystem. - When explaining why Standard is required over Autopilot, explicitly cite all matching restrictions (e.g., custom sysctls and custom node taints).
- For advanced cluster architecture or complex node pool creation planning, refer to
gke-cluster-creation.
Critical Gotchas & Best Practices
- Private Autopilot Clusters:
- Use
--enable-private-nodesfor private node IP addresses. - Use
--enable-private-endpointto disable public IP access to the control plane. - Restrict control plane access with
--enable-master-authorized-networksand--master-authorized-networks=CIDR_BLOCK:
gcloud container clusters create-auto CLUSTER_NAME --region=REGION \
--enable-private-nodes \
--enable-private-endpoint \
--enable-master-authorized-networks \
--master-authorized-networks=CIDR_BLOCK
- Workload Identity (IAM Binding):
- Never mount raw GCP Service Account JSON keys in Pods.
- Annotate the Kubernetes ServiceAccount (
KSA) to bind to the Google Service Account (GSA):
metadata:
annotations:
iam.gke.io/gcp-service-account: GSA_NAME@PROJECT_ID.iam.gserviceaccount.com
- Autopilot Resource Requests:
- In Autopilot, CPU requests must be specified in increments of 250m (0.25 vCPU). If an unaligned CPU request (e.g., 300m) is requested, round up to the nearest 250m increment (500m / 0.5 vCPU).
- Resource requests equal limits automatically. Omit
limitsto allow Autopilot to set defaults matchingrequests.
- Cluster Credentials:
- Always explicitly specify
--region(for regional clusters) or--zone(for zonal clusters) when fetching credentials:
gcloud container clusters get-credentials CLUSTER_NAME --region=REGION --quiet
Reference Directory
- [Core Concepts](references/core-concepts.md): Architecture, cluster modes (Autopilot vs Standard), networking, scaling, and security model.
- [CLI Usage & Tool Reference](references/cli-reference.md): Tool preference hierarchy (MCP vs gcloud vs kubectl),
gcloud containercommands, and user preference overrides.
- [Client Libraries](references/client-library-usage.md): Official Kubernetes and Google Cloud Container client libraries in Python, Go, Node.js, and Java.
- [MCP Usage](references/mcp-usage.md): Connecting to and using the 23 structured GKE MCP tools for cluster management, K8s resources, and diagnostics.
- [Infrastructure as Code](references/iac-usage.md): Terraform examples for
google_container_cluster(Autopilot), Kubernetes provider resources, and YAML samples.
Provenance
Authored by Google LLC in google/skills. The content is mirrored here under its upstream license.
Related
agent-platform-alert-configuration
Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.
google/skills
Apache-2.0
agent-platform-deploy
Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for public Vertex AI deployments (use the `vertex-deploy` skill) or for running model evaluations (use the `agent-platform-eval-flywheel` skill).
google/skills
Apache-2.0
agent-platform-endpoint-management
Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model evaluations.
google/skills
Apache-2.0
agent-platform-eval-flywheel
Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy.
google/skills
Apache-2.0
agent-platform-inference
Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.
google/skills
Apache-2.0