gcp-gke
gcp-gke is an engineering AI skill with a core value of Deploy and manage Google Kubernetes Engine clusters. It
helps developers solve real-world problems in the engineering domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
Deploy and manage Google Kubernetes Engine clusters. Configure node pools, networking, and workload identity. Use when running Kubernetes on GCP.
Quick Facts
mkdir -p ./skills/gcp-gke && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/gcp-gke/SKILL.md -o ./skills/gcp-gke/SKILL.mdRun in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Google Kubernetes Engine (GKE)
Deploy, operate, and scale managed Kubernetes clusters on Google Cloud Platform.
When to Use
- Running containerized microservices at scale with automatic scaling and healing
- Workloads requiring fine-grained orchestration, service mesh, or custom scheduling
- Teams already invested in Kubernetes tooling (Helm, Argo CD, Flux)
- When Cloud Run's request-based model does not fit (long-running, stateful workloads)
Prerequisites
- Google Cloud SDK (`gcloud`) and `kubectl` installed
- APIs enabled: Kubernetes Engine, Compute Engine
- IAM role `roles/container.admin` for cluster management
gcloud services enable container.googleapis.com compute.googleapis.com
gcloud components install kubectlStandard vs Autopilot
| 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 |
Create a Standard Cluster
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=${PROJECT_ID}.svc.id.goog \
--enable-ip-alias --enable-network-policy \
--enable-shielded-nodes \
--logging=SYSTEM,WORKLOAD --monitoring=SYSTEM,WORKLOAD \
--labels=env=production,team=platform
gcloud container clusters get-credentials prod-cluster --region=us-central1Create an Autopilot Cluster
gcloud container clusters create-auto autopilot-prod \
--region=us-central1 --release-channel=regular \
--workload-pool=${PROJECT_ID}.svc.id.goog \
--network=my-vpc --subnetwork=gke-subnetNode Pools
# 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:NoScheduleWorkload Identity
# 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.comDeploying Workloads
# 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:
serviceAccoun🎯 Best For
- Claude users
- AI users
💡 Use Cases
- Using gcp-gke in daily workflow
- Automating repetitive engineering tasks
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply gcp-gke to Your Work
Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.
- 4
Review and Refine
Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.
❓ Frequently Asked Questions
How do I install gcp-gke?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/gcp-gke/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Not reading the full skill
Skills contain important context and edge cases beyond the quick start.