gcp-cloud-run

sickn33/antigravity-awesome-skills · updated Apr 8, 2026

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$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill gcp-cloud-run
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summary

Production-ready serverless applications on GCP Cloud Run with containerized services and event-driven functions.

  • Covers Cloud Run Services (containerized web apps and APIs) and Cloud Run Functions (HTTP, Pub/Sub, and Cloud Storage event handlers)
  • Includes cold start optimization techniques: startup CPU boost, minimum instances, distroless images, lazy dependency loading, and memory tuning
  • Provides multi-stage Docker builds, graceful shutdown patterns, and Cloud Build deployment pipe
skill.md

GCP Cloud Run

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
# 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'
     

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']

```javascript
// 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);
});
how to use gcp-cloud-run

How to use gcp-cloud-run on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add gcp-cloud-run
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill gcp-cloud-run

The skills CLI fetches gcp-cloud-run from GitHub repository sickn33/antigravity-awesome-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/gcp-cloud-run

Reload or restart Cursor to activate gcp-cloud-run. Access the skill through slash commands (e.g., /gcp-cloud-run) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.646 reviews
  • Ama Johnson· Dec 28, 2024

    gcp-cloud-run reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Dhruvi Jain· Dec 12, 2024

    gcp-cloud-run is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Noor Harris· Dec 4, 2024

    Keeps context tight: gcp-cloud-run is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Kaira Gonzalez· Dec 4, 2024

    Registry listing for gcp-cloud-run matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Emma Liu· Dec 4, 2024

    gcp-cloud-run is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Noor Diallo· Nov 23, 2024

    gcp-cloud-run is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Yusuf Ghosh· Nov 23, 2024

    Solid pick for teams standardizing on skills: gcp-cloud-run is focused, and the summary matches what you get after install.

  • Noor Ramirez· Nov 23, 2024

    Keeps context tight: gcp-cloud-run is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Oshnikdeep· Nov 3, 2024

    Keeps context tight: gcp-cloud-run is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Ganesh Mohane· Oct 22, 2024

    gcp-cloud-run has been reliable in day-to-day use. Documentation quality is above average for community skills.

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