Skills gcp-cloud-run
install
source · Clone the upstream repo
git clone https://github.com/TerminalSkills/skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/TerminalSkills/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/gcp-cloud-run" ~/.claude/skills/terminalskills-skills-gcp-cloud-run && rm -rf "$T"
manifest:
skills/gcp-cloud-run/SKILL.mdsafety · automated scan (low risk)
This is a pattern-based risk scan, not a security review. Our crawler flagged:
- references API keys
Always read a skill's source content before installing. Patterns alone don't mean the skill is malicious — but they warrant attention.
source content
GCP Cloud Run
Google Cloud Run is a fully managed serverless platform for running containers. It automatically scales from zero to thousands of instances, charges only for actual usage, and supports any language or binary that can run in a container.
Core Concepts
- Service — a long-running container that auto-scales based on traffic
- Revision — an immutable snapshot of a service's configuration and code
- Traffic splitting — route percentages of traffic to different revisions
- Job — run a container to completion (batch, cron, one-off tasks)
- Concurrency — max simultaneous requests per container instance
Deploying a Service
# Deploy from source code (Cloud Build + Cloud Run) gcloud run deploy web-app \ --source . \ --region us-central1 \ --allow-unauthenticated \ --port 8080 \ --memory 512Mi \ --cpu 1 \ --min-instances 0 \ --max-instances 100 \ --concurrency 80 \ --set-env-vars "NODE_ENV=production,LOG_LEVEL=info"
# Deploy from a pre-built container image gcloud run deploy web-app \ --image us-central1-docker.pkg.dev/my-project/repo/web-app:v1.2.0 \ --region us-central1 \ --allow-unauthenticated \ --port 8080 \ --memory 1Gi \ --cpu 2 \ --timeout 300 \ --set-secrets "DATABASE_URL=db-url:latest,API_KEY=api-key:latest"
Building and Pushing Images
# Build with Cloud Build and push to Artifact Registry gcloud builds submit \ --tag us-central1-docker.pkg.dev/my-project/repo/web-app:v1.2.0
# Dockerfile — optimized multi-stage build for Cloud Run FROM node:20-slim AS builder WORKDIR /app COPY package*.json ./ RUN npm ci --production=false COPY . . RUN npm run build FROM node:20-slim WORKDIR /app COPY --from=builder /app/dist ./dist COPY --from=builder /app/node_modules ./node_modules COPY package*.json ./ ENV PORT=8080 EXPOSE 8080 CMD ["node", "dist/server.js"]
Traffic Splitting
# Deploy a new revision without sending traffic gcloud run deploy web-app \ --image us-central1-docker.pkg.dev/my-project/repo/web-app:v1.3.0 \ --region us-central1 \ --no-traffic
# Split traffic: 90% to current, 10% to new revision (canary) gcloud run services update-traffic web-app \ --region us-central1 \ --to-revisions web-app-00002=10,web-app-00001=90
# Promote canary to 100% gcloud run services update-traffic web-app \ --region us-central1 \ --to-latest
# Rollback by routing all traffic to a previous revision gcloud run services update-traffic web-app \ --region us-central1 \ --to-revisions web-app-00001=100
Cloud Run Jobs
# Create a batch job gcloud run jobs create data-export \ --image us-central1-docker.pkg.dev/my-project/repo/data-export:latest \ --region us-central1 \ --memory 2Gi \ --cpu 2 \ --task-timeout 3600 \ --max-retries 3 \ --set-env-vars "EXPORT_FORMAT=csv"
# Execute the job gcloud run jobs execute data-export --region us-central1
# Schedule a job with Cloud Scheduler gcloud scheduler jobs create http data-export-daily \ --location us-central1 \ --schedule "0 2 * * *" \ --uri "https://us-central1-run.googleapis.com/apis/run.googleapis.com/v1/namespaces/my-project/jobs/data-export:run" \ --http-method POST \ --oauth-service-account-email my-sa@my-project.iam.gserviceaccount.com
Custom Domains
# Map a custom domain gcloud run domain-mappings create \ --service web-app \ --domain app.example.com \ --region us-central1
# Get DNS records to configure gcloud run domain-mappings describe \ --domain app.example.com \ --region us-central1
Service Configuration
# Update environment variables and secrets gcloud run services update web-app \ --region us-central1 \ --update-env-vars "FEATURE_FLAG=true" \ --set-secrets "DB_PASS=db-password:latest" \ --min-instances 1 \ --cpu-boost
# Set IAM policy (authenticated access only) gcloud run services add-iam-policy-binding web-app \ --region us-central1 \ --member="serviceAccount:frontend@my-project.iam.gserviceaccount.com" \ --role="roles/run.invoker"
Monitoring
# View service details and URL gcloud run services describe web-app --region us-central1 --format='value(status.url)'
# List revisions gcloud run revisions list --service web-app --region us-central1
# Stream logs gcloud run services logs tail web-app --region us-central1
Best Practices
- Set min-instances=1 for latency-sensitive services to avoid cold starts
- Use concurrency settings matching your app's thread model (default 80)
- Store secrets in Secret Manager, not environment variables
- Use multi-stage Docker builds for smaller images and faster deploys
- Enable CPU boost for faster cold start initialization
- Use traffic splitting for safe canary deployments
- Set appropriate request timeouts (default 300s, max 3600s)
- Use Cloud Run Jobs for batch work instead of long-running services