google-app-engine▌
jezweb/claude-skills · updated Apr 8, 2026
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Status: Production Ready
- ›Last Updated: 2026-01-24
- ›Dependencies: Google Cloud SDK (gcloud CLI)
- ›Skill Version: 1.0.0
Google App Engine
Status: Production Ready Last Updated: 2026-01-24 Dependencies: Google Cloud SDK (gcloud CLI) Skill Version: 1.0.0
Quick Start (10 Minutes)
1. Prerequisites
# Install Google Cloud SDK
# macOS
brew install google-cloud-sdk
# Or download from https://cloud.google.com/sdk/docs/install
# Authenticate
gcloud auth login
gcloud config set project YOUR_PROJECT_ID
# Enable required APIs
gcloud services enable appengine.googleapis.com
gcloud services enable sqladmin.googleapis.com
gcloud services enable secretmanager.googleapis.com
2. Create app.yaml
# app.yaml - Standard Environment (Python 3.12)
runtime: python312
instance_class: F2
env_variables:
DJANGO_SETTINGS_MODULE: "myproject.settings.production"
handlers:
# Static files (served directly by App Engine)
- url: /static
static_dir: staticfiles/
secure: always
# All other requests go to the app
- url: /.*
script: auto
secure: always
automatic_scaling:
min_instances: 0
max_instances: 10
target_cpu_utilization: 0.65
3. Deploy
# Deploy to App Engine
gcloud app deploy
# Deploy with specific version
gcloud app deploy --version=v1 --no-promote
# View logs
gcloud app logs tail -s default
Standard vs Flexible Environment
Standard Environment (Recommended for Most Apps)
Use when: Building typical web apps, APIs, or services that fit within runtime constraints.
| Aspect | Standard |
|---|---|
| Startup | Fast (milliseconds) |
| Scaling | Scale to zero |
| Pricing | Pay per request |
| Runtimes | Python 3.8-3.12 |
| Instance Classes | F1, F2, F4, F4_1G |
| Max Request | 60 seconds |
| Background | Cloud Tasks only |
# app.yaml - Standard
runtime: python312
instance_class: F2
Flexible Environment
Use when: Need custom runtimes, Docker, longer request timeouts, or background threads.
| Aspect | Flexible |
|---|---|
| Startup | Slower (minutes) |
| Scaling | Min 1 instance |
| Pricing | Per-hour VM |
| Runtimes | Any (Docker) |
| Max Request | 60 minutes |
| Background | Native threads |
# app.yaml - Flexible
runtime: python
env: flex
runtime_config:
runtime_version: "3.12"
resources:
cpu: 1
memory_gb: 0.5
disk_size_gb: 10
automatic_scaling:
min_num_instances: 1
max_num_instances: 5
Cost Warning: Flexible always runs at least 1 instance (~$30-40/month minimum).
Cloud SQL Connection
Standard Environment (Unix Socket)
App Engine Standard connects to Cloud SQL via Unix sockets, not TCP/IP.
# settings.py
import os
if os.getenv('GAE_APPLICATION'):
# Production: Cloud SQL via Unix socket
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'NAME': os.environ['DB_NAME'],
'USER': os.environ['DB_USER'],
'PASSWORD': os.environ['DB_PASSWORD'],
'HOST': f"/cloudsql/{os.environ['CLOUD_SQL_CONNECTION_NAME']}",
'PORT': '', # Empty for Unix socket
}
}
else:
# Local development: Cloud SQL Proxy or local DB
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'NAME': os.environ.get('DB_NAME', 'localdb'),
'USER': os.environ.get('DB_USER', 'postgres'),
'PASSWORD': os.environ.get('DB_PASSWORD', ''),
'HOST': '127.0.0.1',
'PORT': '5432',
}
}
# app.yaml
env_variables:
DB_NAME: "mydb"
DB_USER: "myuser"
CLOUD_SQL_CONNECTION_NAME: "project:region:instance"
# CRITICAL: Beta settings for Cloud SQL socket
beta_settings:
cloud_sql_instances: "project:region:instance"
CRITICAL: The beta_settings.cloud_sql_instances enables the Unix socket. Without it, connection fails.
Local Development with Cloud SQL Proxy
# Download and run Cloud SQL Proxy
cloud-sql-proxy PROJECT:REGION:INSTANCE --port=5432
# Or use Docker
docker run -p 5432:5432 \
gcr.io/cloud-sql-connectors/cloud-sql-proxy:2.8.0 \
PROJECT:REGION:INSTANCE
Static Files with Cloud Storage
Option 1: App Engine Static Handlers (Simple)
# app.yaml
handlers:
- url: /static
static_dir: staticfiles/
secure: always
expiration: "1d"
# Collect static files before deploy
python manage.py collectstatic --noinput
gcloud app deploy
Limitation: Files bundled with deploy, limited to 32MB per file.
Option 2: Cloud Storage (Recommended for Production)
# settings.py
from google.cloud import storage
GS_BUCKET_NAME = os.environ.get('GS_BUCKET_NAME')
STATICFILES_STORAGE = 'storages.backends.gcloud.GoogleCloudStorage'
DEFAULT_FILE_STORAGE = 'storages.backends.gcloud.GoogleCloudStorage'
# Or with django-storages
STORAGES = {
"default": {
"BACKEND": "storages.backends.gcloud.GoogleCloudStorage",
"OPTIONS": {
"bucket_name": GS_BUCKET_NAME,
"location": "media",
},
},
"staticfiles": {
"BACKEND": "storages.backends.gcloud.GoogleCloudStorage",
"OPTIONS": {
"bucket_name": GS_BUCKET_NAME,
"location": "static",
},
},
}
# Install django-storages
pip install django-storages[google]
# Create bucket with public access for static files
gsutil mb -l us-central1 gs://YOUR_BUCKET_NAME
gsutil iam ch allUsers:objectViewer gs://YOUR_BUCKET_NAME
Environment Variables and Secrets
Simple: app.yaml env_variables
# app.yaml - NOT for secrets!
env_variables:
DJANGO_SETTINGS_MODULE: "myproject.settings.production"
How to use google-app-engine on Cursor
AI-first code editor with Composer
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 google-app-engine
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches google-app-engine from GitHub repository jezweb/claude-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate google-app-engine. Access the skill through slash commands (e.g., /google-app-engine) 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
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.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 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▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★73 reviews- ★★★★★Shikha Mishra· Dec 28, 2024
google-app-engine fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Meera Bhatia· Dec 28, 2024
I recommend google-app-engine for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Maya Verma· Dec 24, 2024
We added google-app-engine from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Aanya Sharma· Dec 20, 2024
Keeps context tight: google-app-engine is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Maya Johnson· Dec 16, 2024
google-app-engine is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Daniel Robinson· Dec 12, 2024
Useful defaults in google-app-engine — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Noor Martinez· Dec 8, 2024
Solid pick for teams standardizing on skills: google-app-engine is focused, and the summary matches what you get after install.
- ★★★★★Daniel Choi· Nov 27, 2024
google-app-engine is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Noor Rao· Nov 23, 2024
I recommend google-app-engine for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Rahul Santra· Nov 19, 2024
Registry listing for google-app-engine matched our evaluation — installs cleanly and behaves as described in the markdown.
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