Gke App Onboarding Agent Skill

>- Manages GKE application onboarding, covering containerization, deployment manifests, and migration. Use when onboarding or deploying an application to GKE for the first time, or containerizing an app for GKE. Don't use for general GKE cluster administration or upgrades (use gke-basics or gke-upgrades instead).

2k tokens

context cost

the whole folder, loaded on every use

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

15506

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/google/skills --skill gke-app-onboarding

What comes with it

2 557 bytes besides the instruction

assets/Dockerfile

assets/deployment.yaml

assets/index.js

assets/package.json

The instruction itself

8 sections, as written by the author

GKE App Onboarding

This reference provides workflows for containerizing and deploying applications

to GKE for the first time.

> MCP Tools: apply_k8s_manifest, get_k8s_resource,

> get_k8s_rollout_status, get_k8s_logs, describe_k8s_resource

Workflow

1. App Assessment

Before containerizing, assess the application:

Language & Framework: Identify the tech stack
Dependencies: List required libraries and external services
Configuration: How is the app configured? (env vars, config files,

secrets)

Statefulness: Does it need persistent storage? (databases, file storage)
Networking: Port mapping and protocol (HTTP, gRPC, TCP)
Health endpoints: Does the app expose health check endpoints?

2. Containerization

Create a container image:

Dockerfile (recommended for most apps):

# Multi-stage build for smaller, more secure images
FROM golang:1.22 AS builder
WORKDIR /app
COPY . .
RUN CGO_ENABLED=0 go build -o server .

FROM gcr.io/distroless/static:nonroot
COPY --from=builder /app/server /server
USER nonroot:nonroot
EXPOSE 8080
ENTRYPOINT ["/server"]

Best practices:

Use multi-stage builds to keep production images small
Use distroless or minimal base images to reduce attack surface
Run as non-root user
Log to stdout and stderr for Cloud Logging collection

For applications where writing a Dockerfile is not preferred, you can use

Cloud Native Buildpacks to automatically detect

the language and build a container image:

pack build <image> --builder gcr.io/buildpacks/builder:latest

3. Image Management

Build and store the container image:

# Configure Docker for Artifact Registry
gcloud auth configure-docker <REGION>-docker.pkg.dev --quiet

# Build and push
docker build -t <REGION>-docker.pkg.dev/<PROJECT>/<REPO>/<IMAGE>:<TAG> .
docker push <REGION>-docker.pkg.dev/<PROJECT>/<REPO>/<IMAGE>:<TAG>

Vulnerability scanning: Enable automatic scanning in Artifact Registry to

detect issues in base images and dependencies.

# Check scan results
gcloud artifacts docker images describe \
  <REGION>-docker.pkg.dev/<PROJECT>/<REPO>/<IMAGE>:<TAG> \
  --show-package-vulnerability \
  --quiet

4. Manifest Generation

Generate Kubernetes manifests for the application:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
  namespace: default
spec:
  replicas: 2
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
      - name: my-app
        image: <REGION>-docker.pkg.dev/<PROJECT>/<REPO>/<IMAGE>:<TAG>
        ports:
        - containerPort: 8080
        resources:
          requests:
            cpu: "250m"
            memory: "256Mi"
          limits:
            cpu: "500m"
            memory: "512Mi"
        livenessProbe:
          httpGet:
            path: /healthz
            port: 8080
          initialDelaySeconds: 10
        readinessProbe:
          httpGet:
            path: /readyz
            port: 8080
          initialDelaySeconds: 5
---
apiVersion: v1
kind: Service
metadata:
  name: my-app
spec:
  selector:
    app: my-app
  ports:
  - port: 80
    targetPort: 8080
  type: ClusterIP

Checklist for manifests:

Resource requests and limits set
Liveness and readiness probes configured
At least 2 replicas for production
Service type appropriate (ClusterIP for internal, use Gateway API for

external)

5. Deploy

# MCP (preferred)
apply_k8s_manifest(parent="projects/<PROJECT>/locations/<REGION>/clusters/<CLUSTER>", yamlManifest="<manifest>")

# Verify
get_k8s_rollout_status(parent="...", resourceType="deployment", name="my-app")
get_k8s_resource(parent="...", resourceType="pod", labelSelector="app=my-app")

kubectl fallback:

kubectl apply -f manifests/
kubectl rollout status deployment/my-app
kubectl get pods -l app=my-app

Next Steps

Once the application is running on GKE:

Configure autoscaling — see the gke-workload-scaling skill
Set up observability — see the gke-observability skill
Harden security — see the gke-workload-security skill
Configure reliability (PDBs, topology spread) — see the gke-reliability

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17k tokens

How to use it

Copy the folder

Take google/gke-app-onboarding from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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.