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Task-Specific AI Agent Platform for Cursor AI

Project description

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.

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