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Task Tracker CLI

A robust command-line task management application built in Python that helps you organize and track your tasks efficiently.

This is an implementation of the Task Tracker Project from roadmap.sh.
View my solution: roadmap.sh/projects/task-tracker/solutions?u=66f3ac06c45e253cb03d158d

📦 Package: TestPyPI | Documentation | GitHub Wiki

📋 Table of Contents

✨ Features

  • Task Management

    • Create tasks with descriptions
    • List all tasks or filter by status
    • Update task descriptions
    • Mark tasks as todo/in-progress/done

  • Data Persistence

    • Automatic JSON storage
    • Timestamps for creation and updates
    • Data integrity checks

🔧 Prerequisites

  • Python 3.10 or higher
  • pip (Python package installer)

🚀 Installation

From TestPyPI

pip install -i https://test.pypi.org/simple/ quantuumhedgehog-task-cli

From Source

  1. Clone the repository:
git clone https://github.com/quantuumhedgehog/roadmaps-python-task-tracker.git
cd roadmaps-python-task-tracker
  1. Install in development mode:
# Install with development dependencies
pip install -e ".[dev]"

# Or install just the application
pip install -e .

Via pip (when published)

pip install task-tracker-cli

Build from Source

# Install build dependencies
pip install build

# Build the package
python -m build

# Install the built package
pip install dist/task_cli-0.1.0-py3-none-any.whl

💻 Usage

The task-cli command provides several subcommands for task management:

Task Creation

# Add a new task
task-cli add "Complete the project documentation"

Task Listing

# List all tasks
task-cli list

# List tasks by status
task-cli list todo
task-cli list in-progress
task-cli list done

Task Updates

# Edit task description
task-cli edit <task-id> "Updated task description"

# Update task status
task-cli mark-todo <task-id>
task-cli mark-progress <task-id>
task-cli mark-done <task-id>

# Remove a task
task-cli rm <task-id>      # or
task-cli remove <task-id>  # alternative command

🛠 Development

Testing

The project includes two testing implementations to demonstrate different Python testing approaches:

Using unittest

The unittest implementation follows Python's built-in testing framework approach:

# Run unittest tests
python -m unittest tests/test_task_cli_unittests.py -v

Key features:

  • Uses Python's standard library testing framework
  • Class-based test organization
  • Built-in assertion methods
  • Test discovery and execution

Using pytest

The pytest implementation offers a more modern and feature-rich testing approach:

# Run basic tests
pytest tests/test_task_cli_pytest.py -v

# Run tests with coverage report
pytest tests/test_task_cli_pytest.py -v --cov=src/task_cli

# Generate HTML coverage report
pytest tests/test_task_cli_pytest.py --cov=src/task_cli --cov-report=html

Key features:

  • Fixtures for better test setup and teardown
  • Powerful assertion introspection
  • Built-in parameterization
  • Rich plugin ecosystem
  • Coverage reporting

Project Structure

task-tracker-cli/                   # Root project directory
├── src/                            # Source code directory
│   └── task_cli/                   # Main package directory
│       ├── __init__.py             # Package initialization
│       ├── __main__.py             # Entry point for CLI
│       └── task_cli.py             # Core implementation
├── tests/                          # Test files directory
│   ├── test_task_cli_pytest.py     # Pytest test suite
│   └── test_task_cli_unittests.py  # Unittest test suite
├── README.md                       # Project documentation
└── pyproject.toml                  # Project metadata and dependencies

🗄️ Data Storage

The application uses a simple JSON file to store tasks, ensuring data persistence between sessions. The storage mechanism includes automatic file creation, data validation, and error handling.

🤝 Contributing

We welcome contributions! Here's how you can help:

Commit Conventions

We follow Conventional Commits specification. Each commit message should be structured as follows:

<type>[optional scope]: <description>

[optional body]

[optional footer(s)]

Types

  • feat: A new feature
  • fix: A bug fix
  • docs: Documentation changes
  • style: Code style changes (formatting, semicolons, etc)
  • refactor: Code changes that neither fix bugs nor add features
  • test: Adding or modifying tests
  • build: Changes affecting build system or dependencies
  • ci: Changes to CI configuration files and scripts
  • chore: Other changes that don't modify src or test files

Examples

feat: add task priority feature
fix(parser): handle empty task descriptions
docs: update installation instructions
build(deps): add sphinx for documentation
test: add unit tests for task deletion

Breaking Changes

For breaking changes, add a ! after the type/scope or add BREAKING CHANGE in the footer:

feat!: change task storage format
feat(api)!: remove deprecated endpoints

feat: allow provided config object

BREAKING CHANGE: `config` key in API response has been renamed to `settings`

Getting Started

  1. Fork the repository
  2. Clone your fork: git clone https://github.com/your-username/roadmaps-python-task-tracker.git
  3. Create a feature branch: git checkout -b feature-name
  4. Make your changes
  5. Push to your fork: git push origin feature-name
  6. Open a Pull Request

What to Work On

  • Check our Issues page for open tasks
  • See FUTURELOG.md for planned features
  • Look for issues tagged with good first issue if you're new
  • Feel free to suggest new features!

Guidelines

  • Follow the existing code style
  • Add tests for new features
  • Update documentation as needed
  • Keep commits focused and write clear commit messages
  • Reference issues in your pull request

Development Setup

Follow the Development section above to set up your environment.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

This project is prepared within the Python Developer path on Roadmap.sh.

Documentation and unit tests improvements were generated with assistance from Codeium AI.

📝 Version History

We maintain two logs to track project evolution:

Changelog

The CHANGELOG.md file tracks all notable changes to the project:

  • Breaking changes
  • New features
  • Bug fixes
  • Documentation updates
  • Dependency updates

View the full changelog.

Future Planning

The FUTURELOG.md file outlines future development plans:

  • Planned features and enhancements
  • Architectural improvements
  • Performance optimizations
  • Integration possibilities

This helps us maintain a clear vision for future development.