Cloud

gcp-cloud-run

aj-geddes/useful-ai-prompts · updated Apr 8, 2026

$npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill gcp-cloud-run
summary

Google Cloud Run enables deployment of containerized applications at scale without managing infrastructure. Run stateless HTTP containers with automatic scaling from zero to thousands of instances, paying only for compute time consumed.

skill.md

GCP Cloud Run

Table of Contents

Overview

Google Cloud Run enables deployment of containerized applications at scale without managing infrastructure. Run stateless HTTP containers with automatic scaling from zero to thousands of instances, paying only for compute time consumed.

When to Use

  • Microservices and APIs
  • Web applications and backends
  • Batch processing jobs
  • Long-running background workers
  • CI/CD pipeline integration
  • Data processing pipelines
  • WebSocket applications
  • Multi-language services

Quick Start

Minimal working example:

# Build container image
gcloud builds submit --tag gcr.io/MY_PROJECT_ID/my-app:latest

# Deploy to Cloud Run
gcloud run deploy my-app \
  --image gcr.io/MY_PROJECT_ID/my-app:latest \
  --platform managed \
  --region us-central1 \
  --memory 512Mi \
  --cpu 1 \
  --timeout 3600 \
  --max-instances 100 \
  --min-instances 1 \
  --no-allow-unauthenticated \
  --set-env-vars NODE_ENV=production,DATABASE_URL=postgresql://...

# Allow public access
gcloud run services add-iam-policy-binding my-app \
  --platform managed \
  --region us-central1 \
  --member=allUsers \
  --role=roles/run.invoker

# Get service URL
gcloud run services describe my-app \
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Guide Contents
Cloud Run Deployment with gcloud CLI Cloud Run Deployment with gcloud CLI
Containerized Application (Node.js) Containerized Application (Node.js)
Terraform Cloud Run Configuration Terraform Cloud Run Configuration
Docker Build and Push Docker Build and Push

Best Practices

✅ DO

  • Use container health checks
  • Set appropriate CPU and memory
  • Implement graceful shutdown
  • Use service accounts with least privilege
  • Monitor with Cloud Logging
  • Enable Cloud Armor for protection
  • Use revision management for blue-green deployments
  • Implement startup and liveness probes

❌ DON'T

  • Store secrets in code
  • Use default service account
  • Create stateful applications
  • Ignore health checks
  • Deploy without testing
  • Use excessive resource limits
  • Store files in container filesystem
general reviews

Ratings

4.510 reviews
  • Shikha Mishra· Oct 10, 2024

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

  • Piyush G· Sep 9, 2024

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

  • Chaitanya Patil· Aug 8, 2024

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

  • Sakshi Patil· Jul 7, 2024

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

  • Ganesh Mohane· Jun 6, 2024

    I recommend gcp-cloud-run for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Oshnikdeep· May 5, 2024

    Useful defaults in gcp-cloud-run — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Dhruvi Jain· Apr 4, 2024

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

  • Rahul Santra· Mar 3, 2024

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

  • Pratham Ware· Feb 2, 2024

    We added gcp-cloud-run from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Yash Thakker· Jan 1, 2024

    gcp-cloud-run fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.