django-expert — community django-expert, TrackWatch, community, ide skills

v1.0.0

About this Skill

Ideal for Python-based AI Agents requiring expert-level Django backend development capabilities Expert Django backend development guidance. Use when creating Django models, views, serializers, or APIs; debugging ORM queries or migrations; optimizing database performance; implementing authenticat

emlopezr emlopezr
[0]
[0]
Updated: 3/12/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reviewed Landing Page Review Score: 9/11

Killer-Skills keeps this page indexable because it adds recommendation, limitations, and review signals beyond the upstream repository text.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review Locale and body language aligned
Review Score
9/11
Quality Score
50
Canonical Locale
en
Detected Body Locale
en

Ideal for Python-based AI Agents requiring expert-level Django backend development capabilities Expert Django backend development guidance. Use when creating Django models, views, serializers, or APIs; debugging ORM queries or migrations; optimizing database performance; implementing authenticat

Core Value

Empowers agents to design optimal Django models using ORM patterns, implement scalable views with FBV, CBV, and DRF viewsets, and develop high-performance REST APIs with Django REST Framework, following official Django best practices and modern Python conventions

Ideal Agent Persona

Ideal for Python-based AI Agents requiring expert-level Django backend development capabilities

Capabilities Granted for django-expert

Building maintainable Django applications with robust model design
Implementing scalable and secure Django REST Framework APIs
Optimizing Django application performance using official best practices

! Prerequisites & Limits

  • Requires Python and Django installation
  • Limited to Django backend development
  • Follows official Django best practices, which may not be compatible with older Django versions

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

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FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is django-expert?

Ideal for Python-based AI Agents requiring expert-level Django backend development capabilities Expert Django backend development guidance. Use when creating Django models, views, serializers, or APIs; debugging ORM queries or migrations; optimizing database performance; implementing authenticat

How do I install django-expert?

Run the command: npx killer-skills add emlopezr/TrackWatch/django-expert. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for django-expert?

Key use cases include: Building maintainable Django applications with robust model design, Implementing scalable and secure Django REST Framework APIs, Optimizing Django application performance using official best practices.

Which IDEs are compatible with django-expert?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for django-expert?

Requires Python and Django installation. Limited to Django backend development. Follows official Django best practices, which may not be compatible with older Django versions.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add emlopezr/TrackWatch/django-expert. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use django-expert immediately in the current project.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

django-expert

Install django-expert, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
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Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

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

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