writing-skills
writing-skills
Use when creating new skills, editing existing skills, or verifying skills work before deployment
gke-basicsManages core GKE cluster provisioning, credentials, Autopilot vs Standard selection, and workload deployment. Use when creating GKE clusters, fetching kubectl credentials, or deciding between Autopilot and Standard modes. Don't use for Workload Identity (use gke-workload-identity), GKE networking (use gke-networking), security hardening (use gke-platform-se…
Independent GitHub snapshot · Automated classification · Original documentation governs
Routing Note: For Workload Identity KSA/GSA setup, open
gke-workload-identity/SKILL.md. For cluster security flags (--database-encryption-key,--security-posture, RBAC, Shielded Nodes, Binary Authorization), opengke-platform-security/SKILL.md. For cluster upgrades or maintenance windows, opengke-upgrades/SKILL.md. For generating Kubernetes YAML manifests (Deployment,StatefulSet,Service,HTTPRoute,PodDisruptionBudget), opengke-manifest-generation/SKILL.md.
Managed Kubernetes platform on Google Cloud. Defaults to Autopilot mode unless Standard is explicitly required.
sysctl) are required.hostPath mounts to the host OS filesystem.gke-cluster-creation.Private Autopilot Clusters:
--enable-private-nodes for private node IP addresses.--enable-private-endpoint to disable public IP access to the control plane.--enable-master-authorized-networks and --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):
gke-workload-identity/SKILL.md for KSA/GSA setup.Autopilot Resource Requests:
limits to allow Autopilot to set defaults matching requests.Cluster Credentials:
--region (for regional clusters) or --zone (for zonal clusters) when fetching credentials:
gcloud container clusters get-credentials CLUSTER_NAME --region=REGION --quiet
Core Concepts: Architecture, cluster modes (Autopilot vs Standard), networking, scaling, and security model.
CLI Usage & Tool Reference: Tool preference hierarchy (MCP vs gcloud vs kubectl), gcloud container commands, and user preference overrides.
Client Libraries: Official Kubernetes and Google Cloud Container client libraries in Python, Go, Node.js, and Java.
MCP Usage: Connecting to and using the 23 structured GKE MCP tools for cluster management, K8s resources, and diagnostics.
Infrastructure as Code: Terraform examples for google_container_cluster (Autopilot), Kubernetes provider resources, and YAML samples.