Deploy and manage Google Kubernetes Engine clusters. Configure node pools, networking, and workload identity. Use when running Kubernetes on GCP.
npx skills add https://github.com/BagelHole/DevOps-Security-Agent-Skills --skill gcp-gke
Deploy, operate, and scale managed Kubernetes clusters on Google Cloud Platform.
gcloud) and kubectl installedroles/container.admin for cluster managementgcloud services enable container.googleapis.com compute.googleapis.com
gcloud components install kubectl
| Feature | Standard | Autopilot |
|---------|----------|-----------|
| Node management | You manage node pools | Google manages nodes |
| Pricing | Pay per node (VM) | Pay per pod resource request |
| GPU/TPU | Full support | Supported (with limits) |
| DaemonSets | Allowed | Restricted |
| Best for | Full control, specialized HW | Hands-off, cost-optimized |
gcloud container clusters create prod-cluster \
--region=us-central1 --num-nodes=2 \
--machine-type=e2-standard-4 --disk-size=100 \
--enable-autoscaling --min-nodes=1 --max-nodes=5 \
--enable-autorepair --enable-autoupgrade \
--release-channel=regular \
--workload-pool=${PROJECT_ID}.svc.id.goog \
--enable-ip-alias --enable-network-policy \
--enable-shielded-nodes \
--logging=SYSTEM,WORKLOAD --monitoring=SYSTEM,WORKLOAD \
--labels=env=production,team=platform
gcloud container clusters get-credentials prod-cluster --region=us-central1
gcloud container clusters create-auto autopilot-prod \
--region=us-central1 --release-channel=regular \
--workload-pool=${PROJECT_ID}.svc.id.goog \
--network=my-vpc --subnetwork=gke-subnet
# High-memory pool with taint
gcloud container node-pools create highmem-pool \
--cluster=prod-cluster --region=us-central1 \
--machine-type=n2-highmem-8 --disk-size=200 --disk-type=pd-ssd \
--num-nodes=1 --enable-autoscaling --min-nodes=0 --max-nodes=4 \
--node-labels=workload=memory-intensive \
--node-taints=dedicated=highmem:NoSchedule
# GPU pool
gcloud container node-pools create gpu-pool \
--cluster=prod-cluster --region=us-central1 \
--machine-type=n1-standard-8 \
--accelerator=type=nvidia-tesla-t4,count=1 \
--num-nodes=0 --enable-autoscaling --min-nodes=0 --max-nodes=4 \
--node-taints=nvidia.com/gpu=present:NoSchedule
# Spot pool for batch workloads
gcloud container node-pools create spot-pool \
--cluster=prod-cluster --region=us-central1 \
--machine-type=e2-standard-4 --spot \
--num-nodes=0 --enable-autoscaling --min-nodes=0 --max-nodes=20 \
--node-taints=cloud.google.com/gke-spot=true:NoSchedule
# Create GSA and grant permissions
gcloud iam service-accounts create app-gsa
gcloud projects add-iam-policy-binding ${PROJECT_ID} \
--member="serviceAccount:app-gsa@${PROJECT_ID}.iam.gserviceaccount.com" \
--role="roles/storage.objectViewer"
# Create KSA and bind to GSA
kubectl create namespace myapp
kubectl create serviceaccount app-ksa --namespace=myapp
gcloud iam service-accounts add-iam-policy-binding \
app-gsa@${PROJECT_ID}.iam.gserviceaccount.com \
--role=roles/iam.workloadIdentityUser \
--member="serviceAccount:${PROJECT_ID}.svc.id.goog[myapp/app-ksa]"
kubectl annotate serviceaccount app-ksa --namespace=myapp \
iam.gke.io/gcp-service-account=app-gsa@${PROJECT_ID}.iam.gserviceaccount.com
# deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: web-app
namespace: myapp
spec:
replicas: 3
selector:
matchLabels: { app: web-app }
template:
metadata:
labels: { app: web-app }
spec:
serviceAccountName: app-ksa
containers:
- name: web
image: us-central1-docker.pkg.dev/PROJECT_ID/repo/web-app:v1.2.0
ports: [{ containerPort: 8080 }]
resources:
requests: { cpu: 250m, memory: 512Mi }
limits: { cpu: 500m, memory: 1Gi }
readinessProbe:
httpGet: { path: /healthz, port: 8080 }
initialDelaySeconds: 5
livenessProbe:
httpGet: { path: /healthz, port: 8080 }
initialDelaySeconds: 15
topologySpreadConstraints:
- maxSkew: 1
topologyKey: topology.kubernetes.io/zone
whenUnsatisfiable: DoNotSchedule
labelSelector:
matchLabels: { app: web-app }
---
apiVersion: v1
kind: Service
metadata: { name: web-app, namespace: myapp }
spec:
selector: { app: web-app }
ports: [{ port: 80, targetPort: 8080 }]
type: ClusterIP
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
name: web-ingress
namespace: myapp
annotations:
kubernetes.io/ingress.class: "gce"
networking.gke.io/managed-certificates: "web-cert"
kubernetes.io/ingress.global-static-ip-name: "web-static-ip"
spec:
rules:
- host: app.example.com
http:
paths:
- path: /
pathType: Prefix
backend:
service: { name: web-app, port: { number: 80 } }
---
apiVersion: networking.gke.io/v1
kind: ManagedCertificate
metadata: { name: web-cert, namespace: myapp }
spec:
domains: [app.example.com]
gcloud compute addresses create web-static-ip --global
resource "google_container_cluster" "primary" {
name = "prod-cluster"
location = "us-central1"
release_channel { channel = "REGULAR" }
workload_identity_config { workload_pool = "${var.project_id}.svc.id.goog" }
network = google_compute_network.vpc.name
subnetwork = google_compute_subnetwork.gke.name
ip_allocation_policy {
cluster_secondary_range_name = "pods"
services_secondary_range_name = "services"
}
private_cluster_config {
enable_private_nodes = true
master_ipv4_cidr_block = "172.16.0.0/28"
}
network_policy { enabled = true }
logging_config { enable_components = ["SYSTEM_COMPONENTS", "WORKLOADS"] }
monitoring_config {
enable_components = ["SYSTEM_COMPONENTS", "WORKLOADS"]
managed_prometheus { enabled = true }
}
remove_default_node_pool = true
initial_node_count = 1
}
resource "google_container_node_pool" "primary" {
name = "primary-pool"
cluster = google_container_cluster.primary.name
location = "us-central1"
initial_node_count = 2
autoscaling { min_node_count = 1; max_node_count = 5 }
management { auto_repair = true; auto_upgrade = true }
node_config {
machine_type = "e2-standard-4"
disk_size_gb = 100
disk_type = "pd-balanced"
oauth_scopes = ["https://www.googleapis.com/auth/cloud-platform"]
shielded_instance_config {
enable_secure_boot = true
enable_integrity_monitoring = true
}
metadata = { disable-legacy-endpoints = "true" }
}
}
resource "google_compute_subnetwork" "gke" {
name = "gke-subnet"
ip_cidr_range = "10.0.0.0/20"
region = "us-central1"
network = google_compute_network.vpc.id
secondary_ip_range { range_name = "pods"; ip_cidr_range = "10.4.0.0/14" }
secondary_ip_range { range_name = "services"; ip_cidr_range = "10.8.0.0/20" }
}
gcloud container clusters list
gcloud container clusters upgrade prod-cluster --region=us-central1 --master
kubectl top nodes && kubectl top pods --namespace=myapp
kubectl scale deployment web-app --replicas=5 --namespace=myapp
kubectl autoscale deployment web-app --namespace=myapp --min=3 --max=20 --cpu-percent=70
kubectl logs -f deployment/web-app --namespace=myapp --all-containers
| Symptom | Cause | Fix |
|---------|-------|-----|
| Pods stuck in Pending | No nodes with enough resources | Check autoscaler; add larger node pool; verify resource requests |
| ImagePullBackOff | Wrong image path or missing AR access | Verify image URL; grant roles/artifactregistry.reader to node SA |
| Workload Identity wrong account | KSA annotation missing | Re-annotate KSA; restart pods to pick up new token |
| Nodes NotReady | Disk/memory pressure or network issue | Run kubectl describe node; check taints and conditions |
| Ingress returns 502 | Backend pods failing health check | Verify readiness probe; check NEG health in Console |
| Cluster create quota error | Insufficient regional CPU/IP quota | Request quota increase in IAM & Admin > Quotas |
| Network policy not working | Not enabled on cluster | Recreate with --enable-network-policy or use Dataplane V2 |
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take bagelhole/gcp-gke from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.