django-cloud-sql-postgres
Status: Production Ready
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What it does
Last Updated: 2026-01-24
Dependencies: None
Latest Versions: [email protected], [email protected], [email protected], [email protected]
Installation Guide
How to use django-cloud-sql-postgres 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 machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
django-cloud-sql-postgres
Run the install command
Execute 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.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
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.
Security 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 environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
Django on Google Cloud SQL PostgreSQL
Status: Production Ready
Last Updated: 2026-01-24
Dependencies: None
Latest Versions: [email protected], [email protected], [email protected], [email protected]
Quick Start (10 Minutes)
1. Install Dependencies
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-binaryis the PostgreSQL adapter for Djangogunicornis required for App Engine Standard (Python 3.10+)- Cloud SQL Python Connector provides secure connections without SSH tunneling
2. Configure Django Settings
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:
- App Engine connects via Unix socket at
/cloudsql/PROJECT:REGION:INSTANCE - Local development requires Cloud SQL Auth Proxy on
127.0.0.1:5432 - Never hardcode connection strings - use environment variables
3. Create app.yaml
runtime: 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_instancesenables the Unix socket at/cloudsql/...- DB_PASSWORD should be set via
gcloud app deployor Secret Manager, not in app.yaml
The 6-Step Setup Process
Step 1: Create Cloud SQL Instance
# 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:
- Use
POSTGRES_15or later for best compatibility db-f1-microis cheapest for dev ($7-10/month), usedb-g1-smallor higher for production- Note the connection name:
PROJECT_ID:REGION:INSTANCE_NAME
Step 2: Configure Django Project
requirements.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=60reduces connection overhead (Cloud SQL has connection limits)- WhiteNoise serves static files without Cloud Storage
ALLOWED_HOSTSmust include.appspot.comfor App Engine
Step 3: Set Up Local Development with Cloud SQL Proxy
Install 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:
- Proxy creates a secure tunnel to Cloud SQL
- No need to whitelist your IP address
- Works with both password and IAM authentication
Step 4: Run Migrations
# 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
Step 5: Configure Gunicorn
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 myprojectList & 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
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate 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
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Reviews
- PPratham Ware★★★★★Dec 24, 2024
django-cloud-sql-postgres reduced setup friction for our internal harness; good balance of opinion and flexibility.
- OOlivia Desai★★★★★Dec 24, 2024
django-cloud-sql-postgres is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- AAnika Agarwal★★★★★Dec 20, 2024
django-cloud-sql-postgres fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ZZara Smith★★★★★Dec 16, 2024
Keeps context tight: django-cloud-sql-postgres is the kind of skill you can hand to a new teammate without a long onboarding doc.
- AAva Rao★★★★★Dec 16, 2024
We added django-cloud-sql-postgres from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- AAnaya Shah★★★★★Dec 8, 2024
Solid pick for teams standardizing on skills: django-cloud-sql-postgres is focused, and the summary matches what you get after install.
- NNikhil Singh★★★★★Nov 27, 2024
We added django-cloud-sql-postgres from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- SSakshi Patil★★★★★Nov 15, 2024
I recommend django-cloud-sql-postgres for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ZZara Taylor★★★★★Nov 11, 2024
django-cloud-sql-postgres has been reliable in day-to-day use. Documentation quality is above average for community skills.
- KKaira Abbas★★★★★Nov 7, 2024
Solid pick for teams standardizing on skills: django-cloud-sql-postgres is focused, and the summary matches what you get after install.
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