gke-app-onboarding

Original：🇺🇸 English

Translated

1 scriptsChecked / no sensitive code detected

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).

6installs

Sourcegoogle/skills

Added on2026-07-04

NPX Install

npx skill4agent add google/skills gke-app-onboarding

SKILL.md Content

View Translation Comparison →

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):

dockerfile

# 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:

bash

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

3. Image Management

Build and store the container image:

bash

# 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.

bash

# 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:

yaml

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:

bash

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

gke-app-onboarding

NPX Install

Tags

SKILL.md Content

GKE App Onboarding

Workflow

1. App Assessment

2. Containerization

3. Image Management

4. Manifest Generation

5. Deploy

Next Steps