task-manager

Task Tracker is a terminal-based application for managing tasks and a project plan with a three-pane interface. It provides a reusable API for programmatic access, a command-line interface for scripting common operations, and a terminal UI for interactive task and plan management. The UI presents a three-pane layout: a top-left task list, a top-right details pane for the selected task, and a bottom full-width project plan. You can create, edit, delete, and prioritize tasks, and you can define high-level project steps, track their completion, and reorder them. The included CLI and API allow you to automate typical workflows like adding tasks, toggling plan steps, and exporting data. To run the UI, start the server and launch the terminal interface; to use the CLI or API, call the provided command patterns as shown in the examples.

Prerequisites:

• Python 3.8+ installed on your system
• Git installed -pip available

Installation steps:

1. Clone the repository: git clone https://github.com/yourusername/terminal-task-tracker.git
2. Navigate into the project directory: cd terminal-task-tracker
3. Install the package in editable mode (develop mode) so changes reflect immediately: pip install -e .
4. Ensure data storage directory exists or will be created by the application (default: ~/.tasktracker).

Usage notes:

• The application exposes a Python-based API, a CLI, and a terminal UI. Use the UI for interactive management, the CLI for scripting, and the API for integration with other tools.

Tips and common considerations:

• Data is stored by default under ~/.tasktracker as JSON files (tasks.json, plan.json, notes.json). You can override this path by setting the DATA_DIR environment variable (see mcp_config env).
• The CLI supports subcommands for tasks and plan operations, including listing, adding, editing, and toggling steps. Use 'export' to generate a JSON snapshot of your data.
• If you encounter encoding or path issues, check that your environment has permission to read/write in the data directory and that the Python version matches the project requirements.
• The UI and CLI share the same underlying API, so changes made via one interface will be reflected in the other after persistence.