Customization

Adapting the Template

This section covers how to customize the Cursor Python Template for your specific project needs.

Project-Specific Customizations

1. Rename the Package

Replace the placeholder package name with your actual package name:

Navigate to the src/ directory
Rename the project_name_placeholder/ directory to your package name
Update imports in any files that reference the old package name

2. Update Project Metadata

Edit pyproject.toml to include your project’s metadata:

[project]
name = "your-package-name"
version = "0.1.0"
description = "Your project description"
authors = [
   {name = "Your Name", email = "your.email@example.com"},
]
readme = "README.md"
requires-python = ">=3.8"
# ... other metadata

3. Update Documentation

Update the documentation templates with your project-specific information:

Edit docs/conf.py to set your project name, author, and version
Modify docs/index.md and other documentation files to describe your project
Create additional documentation pages as needed

4. Customize README

Update README.md with:

Your project name and description
Project-specific installation instructions
Usage examples
Project status
Contribution guidelines

5. Update License

Make sure to update the LICENSE file with:

Current year
Your name or organization

Creating Custom Cursor Rules

The most powerful way to customize the template is by creating your own Cursor rules:

1. Create a New Rule File

Create a new .mdc file in the .cursor/rules/ directory:

# Example: Creating a rule file for your specific project architecture
touch .cursor/rules/myproject-architecture-rules.mdc

2. Define Rule Metadata

Start your rule file with YAML frontmatter:

---
description: Project-specific architecture guidelines for MyProject
globs:
  - "src/**/*.py"
  - "tests/**/*.py"
alwaysApply: false
---

The metadata includes:

description: Brief explanation of the rule’s purpose
globs: File patterns where the rule applies
alwaysApply: Whether the rule applies without explicit invocation

3. Write Rule Content

After the frontmatter, add Markdown content with your rules:

# MyProject Architecture Guidelines

## Core Components

- **DataManager**: Responsible for all data access operations
- **ProcessingEngine**: Handles data transformation
- **APIHandler**: Manages external API interactions

## Component Interaction Rules

1. DataManager must never directly call ProcessingEngine
2. All API calls must go through APIHandler
3. ...

## Naming Conventions

- Use `Data` prefix for all data access classes
- Use `Process` prefix for all processing classes
- ...

4. Test Your Rules

Test your new rules by:

Opening a relevant file in Cursor
Asking the AI to follow your new rules
Verifying the AI’s responses align with your expectations

Extending the Template

You can extend the template with additional components:

1. Add CI/CD Workflows

Create GitHub Actions workflows in .github/workflows/:

# .github/workflows/tests.yml
name: Tests
on: [push, pull_request]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.10'
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -e ".[dev]"
      - name: Test with pytest
        run: pytest

2. Add Code Quality Tools

Configure code quality tools like Black, isort, mypy, or flake8 in pyproject.toml:

[tool.black]
line-length = 88
target-version = ['py38']

[tool.isort]
profile = "black"
line_length = 88

[tool.mypy]
python_version = "3.8"
warn_return_any = true
warn_unused_configs = true

3. Customize Testing

Extend your testing setup:

Add test fixtures in tests/conftest.py
Create subdirectories for different test types (unit, integration, etc.)
Configure test coverage reporting