django-expert

vintasoftware/django-ai-plugins · updated Apr 19, 2026

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$npx skills add https://github.com/vintasoftware/django-ai-plugins --skill django-expert
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summary

Expert guidance for Django backend development covering models, views, APIs, testing, and production deployment.

  • Covers model design, ORM optimization, query performance tuning, and migration strategies with focus on preventing N+1 problems
  • Supports Django REST Framework development including serializers, viewsets, pagination, filtering, and authentication patterns
  • Includes security best practices for CSRF/XSS prevention, permissions, and production-ready configuration
  • Provides te
skill.md

Django Expert

Overview

This skill provides expert guidance for Django backend development with comprehensive coverage of models, views, Django REST Framework, forms, authentication, testing, and performance optimization. It follows official Django best practices and modern Python conventions to help you build robust, maintainable applications.

Key Capabilities:

  • Model design with optimal ORM patterns
  • View implementation (FBV, CBV, DRF viewsets)
  • Django REST Framework API development
  • Query optimization and performance tuning
  • Authentication and permissions
  • Testing strategies and patterns
  • Security best practices

When to Use

Invoke this skill when you encounter these triggers:

Model & Database Work:

  • "Create a Django model for..."
  • "Optimize this queryset/database query"
  • "Generate migrations for..."
  • "Design database schema for..."
  • "Fix N+1 query problem"

View & API Development:

  • "Create an API endpoint for..."
  • "Build a Django view that..."
  • "Implement DRF serializer/viewset"
  • "Add filtering/pagination to API"

Authentication & Security:

  • "Implement authentication/permissions"
  • "Create custom user model"
  • "Secure this endpoint/view"

Testing & Quality:

  • "Write tests for this Django app"
  • "Debug this Django error/issue"
  • "Review Django code for issues"

Performance & Optimization:

  • "This Django view is slow"
  • "Optimize database queries"
  • "Add caching to..."

Production Deployment:

  • "Deploy Django to production"
  • "Configure Django for production"
  • "Set up HTTPS/SSL for Django"
  • "Production settings checklist"
  • "Configure production database/cache"

Instructions

Follow this workflow when handling Django development requests:

1. Analyze the Request and Gather Context

Identify the task type:

  • Model design (database schema, relationships, migrations)
  • View/API development (FBV, CBV, DRF viewsets, serializers)
  • Query optimization (N+1 problems, database performance)
  • Authentication/permissions (user models, access control)
  • Testing (unit tests, integration tests, fixtures)
  • Security review (CSRF, XSS, SQL injection, permissions)
  • Production deployment (settings, HTTPS, database, caching, monitoring)
  • Template rendering (Django templates, context processors)

Leverage available context:

  • If django-ai-boost MCP server is available, use it to understand project structure and existing patterns
  • Read relevant existing code to understand conventions
  • Check Django version for compatibility considerations

2. Load Relevant Reference Documentation

Based on the task type, reference the appropriate bundled documentation:

  • Models/ORM work -> references/models-and-orm.md

    • Model design patterns and field choices
    • Relationship configurations (ForeignKey, ManyToMany)
    • Custom managers and QuerySet methods
    • Migration strategies
  • View/API development -> references/views-and-urls.md + references/drf-guidelines.md

    • FBV vs CBV decision criteria
    • DRF serializers, viewsets, and routers
    • URL configuration patterns
    • Middleware and request/response handling
  • Performance issues -> references/performance-optimization.md

    • Query optimization techniques (select_related, prefetch_related)
    • Caching strategies (Redis, Memcached, database caching)
    • Database indexing and query profiling
    • Connection pooling and async patterns
  • Production deployment -> references/production-deployment.md

    • Critical settings (DEBUG, SECRET_KEY, ALLOWED_HOSTS)
    • HTTPS and SSL/TLS configuration
    • Database and cache configuration
    • Static/media file serving
    • Error monitoring and logging
    • Deployment process and health checks
  • Security concerns -> references/security-checklist.md

    • CSRF/XSS/SQL injection prevention
    • Authentication and authorization patterns
    • Secure configuration practices
    • Input validation and sanitization
  • Testing tasks -> references/testing-strategies.md

    • Test structure and organization
    • Fixtures and factories
    • Mocking external dependencies
    • Coverage and CI/CD integration

3. Implement Following Django Best Practices

Code quality standards:

  • Follow PEP 8 and Django coding style
  • Use Django built-ins over third-party packages when possible
  • Keep views thin, use services/managers for business logic
  • Write descriptive variable names and add docstrings for complex logic
  • Handle errors gracefully with appropriate exceptions

Django-specific patterns:

  • Use select_related() for FK/OneToOne, prefetch_related() for reverse FK/M2M
  • Leverage class-based views and mixins for code reuse
  • Use Django forms/serializers for validation
  • Follow Django's migration workflow (never edit applied migrations)
  • Use Django's built-in security features (CSRF tokens, auth decorators)

API development (DRF):

  • Use ModelSerializer for standard CRUD operations
  • Implement proper pagination and filtering
  • Use appropriate permission classes
  • Follow RESTful conventions for endpoints
  • Version APIs when making breaking changes

4. Validate and Test

Before presenting the solution:

Code review:

  • Check for N+1 query problems (use Django Debug Toolbar mentally)
  • Verify proper error handling and edge cases
  • Ensure security best practices are followed
  • Confirm migrations are clean and reversible

Testing considerations:

  • Suggest or write appropriate tests for new functionality
  • Verify test coverage for critical paths
  • Check that fixtures/factories are maintainable

Performance check:

  • Review database queries for efficiency
  • Consider caching opportunities
  • Verify proper use of database indexes

Bundled Resources

references/ - Comprehensive Django documentation loaded into context as needed

These reference files provide detailed guidance beyond this SKILL.md overview:

  • references/models-and-orm.md (~11k words)

    • Model field types and best practices
    • Relationship configurations (ForeignKey, OneToOne, ManyToMany)
    • Custom managers and QuerySet methods
    • Migration patterns and common pitfalls
    • Database-level constraints and indexes
  • references/views-and-urls.md (~17k words)

    • Function-based vs class-based view trade-offs
    • CBV mixins and inheritance patterns
    • URL routing and reverse resolution
    • Middleware implementation
    • Request/response lifecycle
  • references/drf-guidelines.md (~18k words)

    • Serializer patterns (ModelSerializer, nested serializers)
    • ViewSet and router configurations
    • Pagination, filtering, and search
    • Authentication and permission classes
    • API versioning strategies
    • Performance optimization for APIs
  • references/testing-strategies.md (~18k words)

    • Test organization and structure
    • Factory patterns vs fixtures
    • Testing views, models, and serializers
    • Mocking external services
    • Test database optimization
    • CI/CD integration
  • references/security-checklist.md (~12k words)

    • CSRF protection implementation
    • XSS prevention techniques
    • SQL injection defense
    • Authentication best practices
    • Permission and authorization patterns
    • Secure settings configuration
  • references/performance-optimization.md (~14k words)

    • Query optimization (select_related, prefetch_related, only, defer)
    • Database indexing strategies
    • Caching layers (Redis, Memcached, database cache)
    • Database connection pooling
    • Profiling and monitoring tools
    • Async views and background tasks
  • references/production-deployment.md (~20k words)

    • Critical settings (DEBUG, SECRET_KEY, ALLOWED_HOSTS)
    • Database configuration and connection pooling
    • HTTPS/SSL configuration and security headers
    • Static and media file serving
    • Caching with Redis/Memcached
    • Email configuration for production
    • Error monitoring with Sentry
    • Logging and health checks
    • Zero-downtime deployment strategies
  • references/examples.md - Practical implementation examples

    • Model design with custom managers
    • N+1 query optimization
    • DRF API endpoint implementation
    • Writing Django tests

Additional Notes

Django Version Compatibility:

  • Consider LTS releases (4.2, 5.2) for production
  • Check deprecation warnings when upgrading
  • Use django-upgrade tool for automated migration

Common Pitfalls to Avoid:

  • Circular imports (use lazy references)
  • Missing related_name on relationships
  • Forgetting database indexes on frequently queried fields
  • Using get() without exception handling
  • N+1 queries in templates and serializers
how to use django-expert

How to use django-expert 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-expert
2

Execute installation command

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

$npx skills add https://github.com/vintasoftware/django-ai-plugins --skill django-expert

The skills CLI fetches django-expert from GitHub repository vintasoftware/django-ai-plugins 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-expert

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

List & Monetize Your Skill

Submit your Claude Code skill and start earning

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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)
  • No comments yet — start the thread.
general reviews

Ratings

4.455 reviews
  • Kofi Desai· Dec 28, 2024

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

  • Olivia Agarwal· Dec 4, 2024

    Registry listing for django-expert matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Zaid Sethi· Dec 4, 2024

    Keeps context tight: django-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Chinedu Sanchez· Nov 23, 2024

    I recommend django-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Amina Nasser· Nov 19, 2024

    Useful defaults in django-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Harper Mensah· Oct 14, 2024

    Useful defaults in django-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Amina Chen· Oct 10, 2024

    I recommend django-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Oshnikdeep· Sep 25, 2024

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

  • Isabella Garcia· Sep 21, 2024

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

  • Zaid Reddy· Sep 9, 2024

    Solid pick for teams standardizing on skills: django-expert is focused, and the summary matches what you get after install.

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