GC

gcp-cloud-run

Patterns and best practices for building production-ready serverless apps on Google Cloud.

Install

mkdir -p .claude/skills/gcp-cloud-run-tjsndhu && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/15494" && unzip -o skill.zip -d .claude/skills/gcp-cloud-run-tjsndhu && rm skill.zip

Installs to .claude/skills/gcp-cloud-run-tjsndhu

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Specialized skill for building production-ready serverless
58 charsno explicit “when” trigger
Intermediate

Key capabilities

  • Build Cloud Run services for containerized web applications.
  • Develop Cloud Run Functions for event-driven handlers.
  • Optimize for cold starts with CPU boost and min instances.
  • Set concurrency based on workload.
  • Handle signals gracefully for clean shutdown.

How it works

The skill provides patterns and best practices for deploying applications on GCP Cloud Run, including Dockerfile examples for containerized services and code examples for event-driven functions. It also covers cold start optimization and graceful shutdown.

Inputs & outputs

You give it
Application code (e.g., Node.js web app, event handler)
You get back
Production-ready serverless application deployed on GCP Cloud Run or Cloud Run Functions

When to use gcp-cloud-run

  • Optimizing cloud run cold starts
  • Configuring event-driven architecture
  • Containerizing node apps for GCP
  • Managing cloud run instances

About this skill

GCP Cloud Run

Specialized skill for building production-ready serverless applications on GCP. Covers Cloud Run services (containerized), Cloud Run Functions (event-driven), cold start optimization, and event-driven architecture with Pub/Sub.

Principles

  • Cloud Run for containers, Functions for simple event handlers
  • Optimize for cold starts with startup CPU boost and min instances
  • Set concurrency based on workload (start with 8, adjust)
  • Memory includes /tmp filesystem - plan accordingly
  • Use VPC Connector only when needed (adds latency)
  • Containers should start fast and be stateless
  • Handle signals gracefully for clean shutdown

Patterns

Cloud Run Service Pattern

Containerized web service on Cloud Run

When to use: Web applications and APIs,Need any runtime or library,Complex services with multiple endpoints,Stateless containerized workloads

# Dockerfile - Multi-stage build for smaller image
FROM node:20-slim AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production

FROM node:20-slim
WORKDIR /app

# Copy only production dependencies
COPY --from=builder /app/node_modules ./node_modules
COPY src ./src
COPY package.json ./

# Cloud Run uses PORT env variable
ENV PORT=8080
EXPOSE 8080

# Run as non-root user
USER node

CMD ["node", "src/index.js"]
// src/index.js
const express = require('express');
const app = express();

app.use(express.json());

// Health check endpoint
app.get('/health', (req, res) => {
  res.status(200).send('OK');
});

// API routes
app.get('/api/items/:id', async (req, res) => {
  try {
    const item = await getItem(req.params.id);
    res.json(item);
  } catch (error) {
    console.error('Error:', error);
    res.status(500).json({ error: 'Internal server error' });
  }
});

// Graceful shutdown
process.on('SIGTERM', () => {
  console.log('SIGTERM received, shutting down gracefully');
  server.close(() => {
    console.log('Server closed');
    process.exit(0);
  });
});

const PORT = process.env.PORT || 8080;
const server = app.listen(PORT, () => {
  console.log(`Server listening on port ${PORT}`);
});
# cloudbuild.yaml
steps:
  # Build the container image
  - name: 'gcr.io/cloud-builders/docker'
    args: ['build', '-t', 'gcr.io/$PROJECT_ID/my-service:$COMMIT_SHA', '.']

  # Push the container image
  - name: 'gcr.io/cloud-builders/docker'
    args: ['push', 'gcr.io/$PROJECT_ID/my-service:$COMMIT_SHA']

  # Deploy to Cloud Run
  - name: 'gcr.io/google.com/cloudsdktool/cloud-sdk'
    entrypoint: gcloud
    args:
      - 'run'
      - 'deploy'
      - 'my-service'
      - '--image=gcr.io/$PROJECT_ID/my-service:$COMMIT_SHA'
      - '--region=us-central1'
      - '--platform=managed'
      - '--allow-unauthenticated'
      - '--memory=512Mi'
      - '--cpu=1'
      - '--min-instances=1'
      - '--max-instances=100'
      - '--concurrency=80'
      - '--cpu-boost'

images:
  - 'gcr.io/$PROJECT_ID/my-service:$COMMIT_SHA'

Structure

project/ ├── Dockerfile ├── .dockerignore ├── src/ │ ├── index.js │ └── routes/ ├── package.json └── cloudbuild.yaml

Gcloud_deploy

Direct gcloud deployment

gcloud run deploy my-service
--source .
--region us-central1
--allow-unauthenticated
--memory 512Mi
--cpu 1
--min-instances 1
--max-instances 100
--concurrency 80
--cpu-boost

Cloud Run Functions Pattern

Event-driven functions (formerly Cloud Functions)

When to use: Simple event handlers,Pub/Sub message processing,Cloud Storage triggers,HTTP webhooks

// HTTP Function
// index.js
const functions = require('@google-cloud/functions-framework');

functions.http('helloHttp', (req, res) => {
  const name = req.query.name || req.body.name || 'World';
  res.send(`Hello, ${name}!`);
});
// Pub/Sub Function
const functions = require('@google-cloud/functions-framework');

functions.cloudEvent('processPubSub', (cloudEvent) => {
  // Decode Pub/Sub message
  const message = cloudEvent.data.message;
  const data = message.data
    ? JSON.parse(Buffer.from(message.data, 'base64').toString())
    : {};

  console.log('Received message:', data);

  // Process message
  processMessage(data);
});
// Cloud Storage Function
const functions = require('@google-cloud/functions-framework');

functions.cloudEvent('processStorageEvent', async (cloudEvent) => {
  const file = cloudEvent.data;

  console.log(`Event: ${cloudEvent.type}`);
  console.log(`Bucket: ${file.bucket}`);
  console.log(`File: ${file.name}`);

  if (cloudEvent.type === 'google.cloud.storage.object.v1.finalized') {
    await processUploadedFile(file.bucket, file.name);
  }
});
# Deploy HTTP function
gcloud functions deploy hello-http \
  --gen2 \
  --runtime nodejs20 \
  --trigger-http \
  --allow-unauthenticated \
  --region us-central1

# Deploy Pub/Sub function
gcloud functions deploy process-messages \
  --gen2 \
  --runtime nodejs20 \
  --trigger-topic my-topic \
  --region us-central1

# Deploy Cloud Storage function
gcloud functions deploy process-uploads \
  --gen2 \
  --runtime nodejs20 \
  --trigger-event-filters="type=google.cloud.storage.object.v1.finalized" \
  --trigger-event-filters="bucket=my-bucket" \
  --region us-central1

Cold Start Optimization Pattern

Minimize cold start latency for Cloud Run

When to use: Latency-sensitive applications,User-facing APIs,High-traffic services

1. Enable Startup CPU Boost

gcloud run deploy my-service \
  --cpu-boost \
  --region us-central1

2. Set Minimum Instances

gcloud run deploy my-service \
  --min-instances 1 \
  --region us-central1

3. Optimize Container Image

# Use distroless for minimal image
FROM node:20-slim AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production

FROM gcr.io/distroless/nodejs20-debian12
WORKDIR /app
COPY --from=builder /app/node_modules ./node_modules
COPY src ./src
CMD ["src/index.js"]

4. Lazy Initialize Heavy Dependencies

// Lazy load heavy libraries
let bigQueryClient = null;

function getBigQueryClient() {
  if (!bigQueryClient) {
    const { BigQuery } = require('@google-cloud/bigquery');
    bigQueryClient = new BigQuery();
  }
  return bigQueryClient;
}

// Only initialize when needed
app.get('/api/analytics', async (req, res) => {
  const client = getBigQueryClient();
  const results = await client.query({...});
  res.json(results);
});

5. Increase Memory (More CPU)

# Higher memory = more CPU during startup
gcloud run deploy my-service \
  --memory 1Gi \
  --cpu 2 \
  --region us-central1

Optimization_impact

  • Startup_cpu_boost: 50% faster cold starts
  • Min_instances: Eliminates cold starts for traffic spikes
  • Distroless_image: Smaller attack surface, faster pull
  • Lazy_init: Defers heavy loading to first request

Concurrency Configuration Pattern

Proper concurrency settings for Cloud Run

When to use: Need to optimize instance utilization,Handle traffic spikes efficiently,Reduce cold starts

Understanding Concurrency

# Default concurrency is 80
# Adjust based on your workload

# For I/O-bound workloads (most web apps)
gcloud run deploy my-service \
  --concurrency 80 \
  --cpu 1

# For CPU-bound workloads
gcloud run deploy my-service \
  --concurrency 1 \
  --cpu 1

# For memory-intensive workloads
gcloud run deploy my-service \
  --concurrency 10 \
  --memory 2Gi

Node.js Concurrency

// Node.js is single-threaded but handles I/O concurrently
// Use async/await for all I/O operations

// GOOD - async I/O
app.get('/api/data', async (req, res) => {
  const [users, products] = await Promise.all([
    fetchUsers(),
    fetchProducts()
  ]);
  res.json({ users, products });
});

// BAD - blocking operation
app.get('/api/compute', (req, res) => {
  const result = heavyCpuOperation(); // Blocks other requests!
  res.json(result);
});

Python Concurrency with Gunicorn

FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .

# 4 workers for concurrency
CMD exec gunicorn --bind :$PORT --workers 4 --threads 2 main:app
# main.py
from flask import Flask
app = Flask(__name__)

@app.route('/api/data')
def get_data():
    return {'status': 'ok'}

Concurrency_guidelines

  • Concurrency=1: Only for CPU-bound or unsafe code
  • Concurrency=8 20: Memory-intensive workloads
  • Concurrency=80: Default, good for I/O-bound
  • Concurrency=250: Maximum, for very lightweight handlers

Pub/Sub Integration Pattern

Event-driven processing with Cloud Pub/Sub

When to use: Asynchronous message processing,Decoupled microservices,Event-driven architecture

Push Subscription to Cloud Run

# Create topic
gcloud pubsub topics create orders

# Create push subscription to Cloud Run
gcloud pubsub subscriptions create orders-push \
  --topic orders \
  --push-endpoint https://my-service-xxx.run.app/pubsub \
  --ack-deadline 600
// Handle Pub/Sub push messages
const express = require('express');
const app = express();
app.use(express.json());

app.post('/pubsub', async (req, res) => {
  // Verify the request is from Pub/Sub
  if (!req.body.message) {
    return res.status(400).send('Invalid Pub/Sub message');
  }

  try {
    // Decode message data
    const message = req.body.message;
    const data = message.data
      ? JSON.parse(Buffer.from(message.data, 'base64').toString())
      : {};

    console.log('Processing order:', data);

    await processOrder(data);

    // Return 200 to acknowledge
    res.status(200).send('OK');
  } catch (error) {
    console.error('Processing failed:', error);
    // Return 500 to trigger retry
    res.status(500).send('Processing failed');
  }
});

Publishing Messages

const { PubSub } = require('@google-cloud/pubsub');
con

---

*Content truncated.*

When not to use it

  • When the user needs AWS serverless solutions.
  • When the user needs Azure containers.
  • When the user needs database design.

Limitations

  • Focuses on GCP Cloud Run and Cloud Run Functions.
  • Does not cover AWS or Azure serverless platforms.
  • Requires manual implementation of code and configuration.

How it compares

This skill offers specialized guidance and code patterns for optimizing serverless applications on GCP Cloud Run, focusing on container performance and event-driven design, which is more specific than general cloud deployment.

Compared to similar skills

gcp-cloud-run side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
gcp-cloud-run (this skill)03moReviewIntermediate
exa-deploy-integration01moReviewAdvanced
adk-deploy-guide03moReviewAdvanced
firecrawl-deploy-integration11moReviewIntermediate

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Example prompts that trigger this skill in your AI assistant.

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