Status: Production Ready
Works with
Last Updated: 2026-01-24
Dependencies: Google Cloud SDK (gcloud CLI)
Skill Version: 1.0.0
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiongoogle-app-engineExecute the skills CLI command in your project's root directory to begin installation:
Fetches google-app-engine from jezweb/claude-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate google-app-engine. Access via /google-app-engine in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Status: Production Ready Last Updated: 2026-01-24 Dependencies: Google Cloud SDK (gcloud CLI) Skill Version: 1.0.0
# 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
# 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
# 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
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
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).
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.
# 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
# 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.
# 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
# app.yaml - NOT for secrets!
env_variables:
DJANGO_SETTINGS_MODULE: "myproject.settings.production"
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
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google-app-engine fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend google-app-engine for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added google-app-engine from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: google-app-engine is the kind of skill you can hand to a new teammate without a long onboarding doc.
google-app-engine is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in google-app-engine — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: google-app-engine is focused, and the summary matches what you get after install.
google-app-engine is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend google-app-engine for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for google-app-engine matched our evaluation — installs cleanly and behaves as described in the markdown.
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