django-ai-boost

🥈Silver

Django AI Boost is an MCP server for Django applications that exposes project information to AI tools. It enables developers to use AI assistance for Django projects, similar to Laravel Boost. The server connects to Django workflows and integrates with AI agents like Claude.

8010Updated 2mo ago

Intermediate30min to implementautomation

Saves ~120 min per use

Quick InstallView Source

git clone https://github.com/vintasoftware/django-ai-boost.git

Works with:

Claude

Overview

About This Skill

How to Use

[{"step":"Install and configure django-ai-boost in your Django project by following the MCP server setup instructions. Ensure it's running alongside your development server.","tip":"Use 'python manage.py runmcp' if using the MCP server implementation, or configure the server in your IDE's AI assistant settings."},{"step":"Trigger the analysis by asking your AI assistant to 'Use django-ai-boost to analyze query performance in [MODEL_NAME]'. Specify the exact model you want to optimize.","tip":"Be specific about the model name and the view/function causing performance issues. The more context you provide, the better the analysis."},{"step":"Review the AI's suggested optimizations and implement the recommended changes in your codebase. Focus on the exact file locations and line numbers provided.","tip":"Test each optimization change individually using Django's test suite to ensure no regressions occur. Use 'python manage.py test' after each modification."},{"step":"Measure the performance impact using Django Debug Toolbar or similar tools to verify the improvements. Compare query counts and execution times before/after changes.","tip":"Take screenshots of the before/after metrics to document the performance gains for your team or stakeholders."},{"step":"Commit the optimized code to your repository with clear documentation of the changes made and their impact on performance.","tip":"Include the query count reduction and performance metrics in your commit message for future reference."}]

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/vintasoftware/django-ai-boost

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Use django-ai-boost to analyze the Django project [PROJECT_NAME] and identify performance bottlenecks in the [MODEL_NAME] model's query patterns. Focus on N+1 query issues and suggest optimizations like select_related, prefetch_related, or raw SQL where applicable. Provide the exact lines of code that need modification with before/after examples.

Example Output

Analysis of the Django project 'EcommercePlatform' revealed critical performance bottlenecks in the Product model's query patterns. The current implementation executes 1,247 individual queries when rendering the product listing page, causing a 4.8-second load time. The primary issue is in the ProductCardView where the related Category and Inventory models are fetched in separate queries for each product.

**Problematic Code (views.py):**
```python
products = Product.objects.all()
for product in products:
    category = product.category  # 1247 queries
    stock = product.inventory.stock  # 1247 queries
```

**Optimized Solution:**
```python
products = Product.objects.select_related('category').prefetch_related('inventory').all()
for product in products:
    category = product.category  # Single query
    stock = product.inventory.first().stock  # Single query
```

Additional recommendations:
1. Add database indexes to frequently queried fields (price, category_id)
2. Implement caching for product listings using Redis (TTL: 5 minutes)
3. Consider denormalizing frequently accessed fields like 'current_price' to avoid joins

The optimized version reduced query count from 1,247 to 3 and improved page load time to 850ms. The changes require modifications to views.py and models.py as shown above.