django-cloud-sql-postgres

jezweb/claude-skills · updated Apr 8, 2026

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$npx skills add https://github.com/jezweb/claude-skills --skill django-cloud-sql-postgres
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Status: Production Ready

skill.md

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-binary is the PostgreSQL adapter for Django
  • gunicorn is 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_instances enables the Unix socket at /cloudsql/...
  • DB_PASSWORD should be set via gcloud app deploy or 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_15 or later for best compatibility
  • db-f1-micro is cheapest for dev ($7-10/month), use db-g1-small or 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=60 reduces connection overhead (Cloud SQL has connection limits)
  • WhiteNoise serves static files without Cloud Storage
  • ALLOWED_HOSTS must include .appspot.com for 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 myproject
how to use django-cloud-sql-postgres

How to use django-cloud-sql-postgres on Cursor

AI-first code editor with Composer

1

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 django-cloud-sql-postgres
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/jezweb/claude-skills --skill django-cloud-sql-postgres

The skills CLI fetches django-cloud-sql-postgres from GitHub repository jezweb/claude-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/django-cloud-sql-postgres

Reload or restart Cursor to activate django-cloud-sql-postgres. Access the skill through slash commands (e.g., /django-cloud-sql-postgres) 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.

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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. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 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

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.651 reviews
  • Pratham Ware· Dec 24, 2024

    django-cloud-sql-postgres reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Olivia Desai· Dec 24, 2024

    django-cloud-sql-postgres is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Anika Agarwal· Dec 20, 2024

    django-cloud-sql-postgres fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Zara 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.

  • Ava 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.

  • Anaya 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.

  • Nikhil 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.

  • Sakshi 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.

  • Zara Taylor· Nov 11, 2024

    django-cloud-sql-postgres has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Kaira 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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