Claude Skill
23blocks-OS/ai-maestro
AI Maestro 通过记忆检索、代码图谱查询和代理消息传递增强 Claude Code 代理。通过单一仪表盘管理 Claude、Aider 和 Cursor,并支持多机协作。
概览
仓库信息
安装这个 Skill
git clone https://github.com/23blocks-OS/ai-maestro.gitRegistry 信息
git clone https://github.com/23blocks-OS/ai-maestro.gitgit clone https://github.com/23blocks-OS/lolabot.git
项目简介
AI Maestro 是一个配备技能系统的 AI 代理编排器,旨在增强 Claude Code 代理。它提供记忆检索、代码图谱查询和代理间消息传递等功能,并通过统一仪表盘管理 Claude、Aider 和 Cursor,支持多机协作。
AI Agent Orchestrator with Skills System - Give AI Agents superpowers: memory search, code graph queries, agent-to-agent messaging. Manage Claude, Codex or any AI Agent from one dashboard. Move Agents between computers and locations
要点
- 配备技能系统的 AI 代理编排器
- 通过记忆检索和代码图谱查询增强 Claude Code 代理
- 支持代理间消息传递
- 用于管理 Claude、Aider 和 Cursor 的统一仪表盘
- 支持分布式工作流的多机协作
使用场景
- 在统一工作流中编排多个 AI 编码助手
- 通过持久化记忆和代码上下文增强 Claude Code 代理
- 促进不同 AI 代理之间的通信
- 通过中央界面管理 Aider 和 Cursor 等开发工具
- 在多台机器上扩展 AI 辅助编码
README 摘要
<div align="center"> <img src="./docs/logo-constellation.svg" alt="AI Maestro Logo" width="120"/> # AI Maestro *I was running 35 AI agents across multiple terminals and became the human mailman between them. So I built AI Maestro.* **The OS for AI-first organizations — orchestrate any AI agent with persistent memory, agent-to-agent messaging, and multi-machine support.** [](https://github.com/23blocks-OS/ai-maestro/releases) [-lightgrey)](https://github.com/23blocks-OS/ai-maestro) [](./LICENSE) [](https://github.com/23blocks-OS/ai-maestro)  [Quick Start](#-quick-start) · [Features](#-features) · [Documentation](#-documentation) · [Contributing](./CONTRIBUTING.md) </div> --- ## The Story I gave an AI agent a real task — not autocomplete, a real engineering problem. It checked the code, read the logs, queried the database, and came back with the answer. That was the moment. *This thing can actually work.* Within a week I was running 35 agents across terminals. They were productive, but they couldn't talk to each other. I became the human message bus — copying context from one terminal, pasting into another. I was the bottleneck in my own AI team. **So I built AI Maestro** — one dashboard to see every agent, on every machine, with persistent memory and direct agent-to-agent communication. Today I run 80+ agents across multiple computers, building real companies with them every day. **What makes this different:**