Status: Production Ready
Works with
Last Updated: 2026-01-24
Dependencies: None
Latest Versions: [email protected], [email protected], [email protected], [email protected]
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondjango-cloud-sql-postgresExecute the skills CLI command in your project's root directory to begin installation:
Fetches django-cloud-sql-postgres 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 django-cloud-sql-postgres. Access via /django-cloud-sql-postgres 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
0
total installs
0
this week
697
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
697
stars
Status: Production Ready
Last Updated: 2026-01-24
Dependencies: None
Latest Versions: [email protected], [email protected], [email protected], [email protected]
pip install Django psycopg2-binary gunicorn
For Cloud SQL Python Connector (recommended for local dev):
pip install "cloud-sql-python-connector[pg8000]"
Why this matters:
psycopg2-binary is the PostgreSQL adapter for Djangogunicorn is required for App Engine Standard (Python 3.10+)settings.py (production with Unix socket):
import os
# Detect App Engine environment
IS_APP_ENGINE = os.getenv('GAE_APPLICATION', None)
if IS_APP_ENGINE:
# Production: Connect 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: Connect via Cloud SQL Proxy
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.postgresql',
'NAME': os.environ.get('DB_NAME', 'mydb'),
'USER': os.environ.get('DB_USER', 'postgres'),
'PASSWORD': os.environ.get('DB_PASSWORD', ''),
'HOST': '127.0.0.1',
'PORT': '5432',
}
}
CRITICAL:
/cloudsql/PROJECT:REGION:INSTANCE127.0.0.1:5432runtime: python310
entrypoint: gunicorn -b :$PORT myproject.wsgi:application
env_variables:
DB_NAME: "mydb"
DB_USER: "postgres"
CLOUD_SQL_CONNECTION_NAME: "project-id:region:instance-name"
# Cloud SQL connection
beta_settings:
cloud_sql_instances: "project-id:region:instance-name"
handlers:
- url: /static
static_dir: static/
- url: /.*
script: auto
secure: always
CRITICAL:
beta_settings.cloud_sql_instances enables the Unix socket at /cloudsql/...gcloud app deploy or Secret Manager, not in app.yaml# Create PostgreSQL instance
gcloud sql instances create myinstance \
--database-version=POSTGRES_15 \
--tier=db-f1-micro \
--region=us-central1
# Create database
gcloud sql databases create mydb --instance=myinstance
# Create user
gcloud sql users create postgres \
--instance=myinstance \
--password=YOUR_SECURE_PASSWORD
Key Points:
POSTGRES_15 or later for best compatibilitydb-f1-micro is cheapest for dev ($7-10/month), use db-g1-small or higher for productionPROJECT_ID:REGION:INSTANCE_NAMErequirements.txt:
Django>=5.1,<6.0
psycopg2-binary>=2.9.9
gunicorn>=23.0.0
whitenoise>=6.7.0
settings.py additions:
import os
# Security settings for production
DEBUG = os.environ.get('DEBUG', 'False') == 'True'
ALLOWED_HOSTS = [
'.appspot.com',
'.run.app',
'localhost',
'127.0.0.1',
]
# Static files with WhiteNoise
STATIC_URL = '/static/'
STATIC_ROOT = os.path.join(BASE_DIR, 'static')
MIDDLEWARE.insert(1, 'whitenoise.middleware.WhiteNoiseMiddleware')
STATICFILES_STORAGE = 'whitenoise.storage.CompressedManifestStaticFilesStorage'
# Database connection pooling
DATABASES['default']['CONN_MAX_AGE'] = 60 # Keep connections open for 60 seconds
Why these settings:
CONN_MAX_AGE=60 reduces connection overhead (Cloud SQL has connection limits)ALLOWED_HOSTS must include .appspot.com for App EngineInstall Cloud SQL Auth Proxy:
# macOS
brew install cloud-sql-proxy
# Linux
curl -o cloud-sql-proxy https://storage.googleapis.com/cloud-sql-connectors/cloud-sql-proxy/v2.14.1/cloud-sql-proxy.linux.amd64
chmod +x cloud-sql-proxy
Run the proxy:
# Authenticate first
gcloud auth application-default login
# Start proxy (runs on 127.0.0.1:5432)
./cloud-sql-proxy PROJECT_ID:REGION:INSTANCE_NAME
# Or with specific port
./cloud-sql-proxy PROJECT_ID:REGION:INSTANCE_NAME --port=5432
Set environment variables for local dev:
export DB_NAME=mydb
export DB_USER=postgres
export DB_PASSWORD=your_password
export DEBUG=True
Key Points:
# Local (with proxy running)
python manage.py migrate
# Verify connection
python manage.py dbshell
For production migrations (via Cloud Build or local with proxy):
# Option 1: Run locally with proxy
./cloud-sql-proxy PROJECT:REGION:INSTANCE &
python manage.py migrate
# Option 2: Use Cloud Build (recommended)
# See references/cloud-build-migrations.md
gunicorn.conf.py (optional, for fine-tuning):
import multiprocessing
# Workers
workers = 2 # App Engine Standard limits this
threads = 4
worker_class = 'gthread'
# Timeout (App Engine has 60s limit for standard, 3600s for flexible)
timeout = 55
# Logging
accesslog = '-'
errorlog = '-'
loglevel = 'info'
# Bind (App Engine sets $PORT)
bind = f"0.0.0.0:{os.environ.get('PORT', '8080')}"
app.yaml entrypoint options:
# Simple (recommended for most cases)
entrypoint: gunicorn -b :$PORT myproject.wsgi:application
# With config file
entrypoint: gunicorn -c gunicorn.conf.py myproject.wsgi:application
# With workers and timeout
entrypoint: gunicorn -b :$PORT -w 2 -t 55 myprojectPrerequisites
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.
jezweb/claude-skills
jezweb/claude-skills
jezweb/claude-skills
jezweb/claude-skills
jezweb/claude-skills
jezweb/claude-skills
django-cloud-sql-postgres reduced setup friction for our internal harness; good balance of opinion and flexibility.
django-cloud-sql-postgres is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
django-cloud-sql-postgres fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: django-cloud-sql-postgres is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added django-cloud-sql-postgres from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: django-cloud-sql-postgres is focused, and the summary matches what you get after install.
We added django-cloud-sql-postgres from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend django-cloud-sql-postgres for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
django-cloud-sql-postgres has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: django-cloud-sql-postgres is focused, and the summary matches what you get after install.
showing 1-10 of 51