systemprompt-code-orchestrator

🥈Silver

MCP server for orchestrating AI coding agents (Claude Code CLI & Gemini CLI). Features task management, process execution, Git integration, and dynamic resource discovery. Full TypeScript implementation with Docker support and Cloudflare Tunnel integration.

140260Updated Today

Intermediate30min to implementautomation

Saves ~180 min per use

Quick InstallView Source

git clone https://github.com/systempromptio/systemprompt-code-orchestrator.git

Works with:

Claude

Overview

About This Skill

The systemprompt-code-orchestrator is an innovative MCP server designed to streamline the orchestration of AI coding agents, specifically Claude Code CLI and Gemini CLI. This skill enables developers to manage tasks, execute processes, and integrate with Git seamlessly. With full TypeScript implementation, Docker support, and Cloudflare Tunnel integration, it provides a robust environment for enhancing AI automation workflows. This skill is particularly valuable for those looking to improve their coding efficiency and reduce manual overhead in project management. One of the key benefits of using the systemprompt-code-orchestrator is its ability to simplify complex workflows. By automating task management and process execution, developers can save significant time that would otherwise be spent on repetitive coding tasks. Although the exact time savings are not quantified, the skill's intermediate complexity suggests that users will find it a practical tool for reducing development cycles and increasing productivity. This skill is especially beneficial for product managers and AI practitioners who need to oversee multiple coding agents and ensure that projects progress smoothly. This skill is ideal for developers, product managers, and AI practitioners who are looking to enhance their workflow automation capabilities. By integrating Git and providing dynamic resource discovery, the systemprompt-code-orchestrator allows teams to collaborate more effectively and maintain version control with ease. Practical use cases include automating the deployment of code updates, managing continuous integration/continuous deployment (CI/CD) processes, and orchestrating complex AI-driven projects that require multiple coding agents to work in unison. With a moderate implementation difficulty of approximately 30 minutes, the systemprompt-code-orchestrator is accessible to those with an intermediate skill level. It fits seamlessly into AI-first workflows by enabling teams to leverage AI agents for coding tasks, thus allowing human developers to focus on higher-level problem-solving and innovation. By adopting this skill, organizations can better position themselves in the competitive landscape of AI automation, driving efficiency and enhancing their development processes.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/systempromptio/systemprompt-code-orchestrator

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

I need to orchestrate multiple AI coding agents to work on a project for [COMPANY] in the [INDUSTRY] sector. The project involves [DATA] and requires the following tasks: [TASK_LIST]. Please use the systemprompt-code-orchestrator to manage these tasks, execute processes, and integrate with Git. Ensure dynamic resource discovery is enabled and provide Docker support with Cloudflare Tunnel integration.

Example Output

## Task Orchestration Plan for [COMPANY] Project

### Overview
The project for [COMPANY] in the [INDUSTRY] sector involves [DATA]. The following tasks have been identified for AI coding agents to execute:

- **Task 1**: Data preprocessing and cleaning
- **Task 2**: Model training and evaluation
- **Task 3**: API development and deployment
- **Task 4**: Documentation and testing

### Task Management
- **Task 1**: Assigned to AI Agent 1
- Status: In Progress
- Estimated Completion: 2 hours
- **Task 2**: Assigned to AI Agent 2
- Status: Pending
- Estimated Completion: 3 hours
- **Task 3**: Assigned to AI Agent 3
- Status: Pending
- Estimated Completion: 4 hours
- **Task 4**: Assigned to AI Agent 4
- Status: Pending
- Estimated Completion: 2 hours

### Process Execution
- **Data Preprocessing**: AI Agent 1 is currently executing data preprocessing scripts.
- **Model Training**: AI Agent 2 will start model training once Task 1 is completed.
- **API Development**: AI Agent 3 will begin API development after Task 2 is completed.
- **Documentation**: AI Agent 4 will start documentation once Task 3 is completed.

### Git Integration
- All changes will be committed to the Git repository with appropriate tags and messages.
- Regular updates will be pushed to the remote repository.

### Dynamic Resource Discovery
- Additional resources will be discovered and allocated as needed.
- Cloudflare Tunnel integration is enabled for secure access.

### Docker Support
- Docker containers will be used for isolated execution environments.
- Regular updates will be pushed to the Docker Hub repository.

### Summary
The orchestration plan is designed to efficiently manage and execute the tasks required for the [COMPANY] project. Regular updates will be provided to ensure smooth progress and completion.