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Found 12 Skills
Plans and executes GKE cluster creation, provisioning, and production readiness audits. Use when creating GKE clusters, provisioning GKE environments, or auditing GKE clusters. Don't use for application onboarding or deployment configuration (use gke-app-onboarding instead).
Sets up and configures Google Kubernetes Engine (GKE) clusters for production use. Use when creating new GKE clusters, choosing between Autopilot vs Standard modes, configuring networking (VPC-native, private clusters), setting up node pools, or planning cluster architecture for Spring Boot microservices. Includes regional vs zonal decisions, security hardening, and resource provisioning guidance.
Trigger on mention of GKE cluster autoscaler, node autoscaling, node pool auto-creation / node auto-provisioning. Provides guidance on enabling and optimizing cluster autoscaler, best practices, and troubleshooting issues such as nodes not scaling up or down, zonal stockouts, or capacity buffers. Do not use for ComputeClass-specific YAML generation or priority configuration (defer to gke-compute-classes skill).
Generates and updates secure, production-ready Kubernetes YAML manifests optimized for GKE Autopilot and GKE Standard clusters. Use when creating or modifying GKE deployment manifests, configuring container security contexts, setting CPU/memory resource limits, defining readiness/liveness/startup probes, mounting secrets and volumes, configuring GKE Gateway API routes, targeting Spot VMs, or deploying AI model inference workloads (vLLM, TGI, Gemma). Don't use for live cluster operations, pod troubleshooting (use gke-workload-troubleshooting), or cluster infrastructure provisioning (use gke-cluster-creation).
Monitors, troubleshoots, and manages GKE TPU Dynamic Slices custom resources. Use when checking TPU slice lifecycle states, troubleshooting slice provisioning failures, validating single-slice or multi-slice (JobSet) workload manifests, or safely patching stuck finalizers and disabling the slice controller. Don't use for generic GKE cluster node pool creation or standard non-TPU workload management (use gke-basics or gke-cluster-creation instead).
Monitors, troubleshoots, and manages GKE TPU Dynamic Slices custom resources. Use when checking TPU slice lifecycle states, troubleshooting slice provisioning failures, validating single-slice or multi-slice (JobSet) workload manifests, or safely patching stuck finalizers and disabling the slice controller. Don't use for generic GKE cluster node pool creation or standard non-TPU workload management (use gke-basics or gke-cluster-creation instead).
Discovers golden base images for creating GKE custom node images based on technical specifications or context clues. Use when finding the golden base image for custom GKE node creation, mapping cluster configuration parameters (GKE version, OS, architecture, accelerators, gVisor, cgroups) to image names, or querying GKE base image maps. Don't use for general GKE cluster creation (use gke-cluster-creation) or standard node pool management (use gke-basics).
Diagnoses GKE nodes reporting NotReady or Unknown status by inspecting node conditions, events, kubelet/containerd logs, and node metrics, then proposing safe remediations. Use when nodes show NotReady, when the kubelet stops posting node status, or when workloads are evicted or stuck Pending due to node health. Don't use for pod-level application failures (use gke-workload-troubleshooting), autoscaler scale-up/scale-down decisions (use gke-cluster-autoscaler), or non-GKE compute.
Google Cloud Platform expert for gcloud CLI, GKE, Cloud Run, and managed services
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 Cloud Platform services including GKE, Cloud Run, Cloud Storage, BigQuery, and Pub/Sub. Activate for GCP infrastructure, Google Cloud deployment, and GCP integration.
Deploy and manage Google Kubernetes Engine clusters. Configure node pools, networking, and workload identity. Use when running Kubernetes on GCP.