ML-MLOps-Portfolio: Skill for Claude Code

Deploy to GKE

Authorization Protocol

This skill enforces the Agent Behavior Protocol (AGENTS.md). Actions per environment:

Env	Mode	What the agent does
dev	AUTO	Execute all steps without asking
staging	CONSULT	Show the full plan (image tag, diff, namespace) and wait for a human "proceed" before kubectl apply
prod	STOP	Do NOT apply. Instruct the user to merge an approved PR and let GitHub Actions with environment: production (required_reviewers) perform the deploy

If you are in prod mode and the human insists, output:

[AGENT MODE: STOP]
Operation: Direct kubectl apply to production cluster
Reason: Prod deploys require the governed path (see ADR-002)
Waiting for: Merge to main + GitHub Environment approval

Then halt.

Pre-Flight Checklist

• Verify context: kubectl config current-context must be GKE cluster
• Docker image built and pushed to Artifact Registry
• Kustomize overlay patched with correct image tag
• Terraform applied for any new infrastructure
• Model artifact uploaded to GCS
• All tests passing in CI

Step 1: Verify Cluster Context

kubectl config current-context
# Expected: gke_{PROJECT_ID}_{REGION}_{CLUSTER_NAME}

NEVER proceed if context is wrong. Switch with:

gcloud container clusters get-credentials {CLUSTER} --region {REGION} --project {PROJECT}

Step 2: Build and Push Image

# Tag with version and SHA
export VERSION=v{X.Y.Z}
export SHA=$(git rev-parse --short HEAD)
export REGISTRY={REGION}-docker.pkg.dev/{PROJECT_ID}/{REPO}

docker build -t ${REGISTRY}/{service}:${VERSION} -t ${REGISTRY}/{service}:sha-${SHA} .
docker push ${REGISTRY}/{service}:${VERSION}
docker push ${REGISTRY}/{service}:sha-${SHA}

Step 3: Update Kustomize Overlay

# k8s/overlays/gcp-{env}/kustomization.yaml  (env = dev | staging | production)
images:
  - name: {service}-predictor
    newName: {REGION}-docker.pkg.dev/{PROJECT_ID}/{REPO}/{service}
    newTag: {VERSION}