agent-task

Agent Task

A powerful task management platform for AI agents in Cursor AI (v0.1.0).

Overview

Agent Task is a platform that helps manage, organize, and share task-specific configurations for AI agents in Cursor. It provides a structured way to define rules, guidelines, and model context protocols (MCP) for different AI tasks.

Features

• Task Management: Create, load, archive, and import task configurations
• Structured Organization: Maintains a consistent directory structure for each task
• Rule Management: Store and manage task-specific guidelines and rules
• MCP Support: Handle Model Context Protocol servers for advanced AI configurations
• Cross-platform: Works across different operating systems using standard paths

Installation

pip install agent-task

Directory Structure

Each task follows this standard structure:

task_name/
├── README.md           # Task documentation
├── rules/             # Task-specific guidelines
│   └── rule.mdc       # Main rules file
└── mcp/               # Model Context Protocol servers

Usage

Initialize a New Task

agent-task init task_name

List Available Tasks

agent-task list

Archive a Task

agent-task archive task_name

Import a Task

agent-task import task_name

Clone a Task from Marketplace

agent-task clone [URL]

Show Task Archive Directory

agent-task path

Configuration

The platform uses standard application directories for storing tasks and configurations:

• App Data: User-specific application data
• Config: User-specific configuration files
• Cache: User-specific cache data
• Logs: User-specific log files

Development

Project Structure

• agent_task/
  • __init__.py - Package initialization and version
  • paths.py - Path management functionality
  • task_manager.py - Core task management logic
  • cli.py - Command-line interface implementation

Requirements

• Python 3.6+
• appdirs for cross-platform path management
• pyyaml for configuration handling
• Additional dependencies as specified in requirements.txt

License

[Add your license information here]

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.