Claude Skill
im4codes/imcodes
imcodes 是一个面向 AI 智能体的即时通讯平台,支持跨 Claude、OpenAI、Gemini 和 Codex 的共享上下文、记忆、监督执行及跨智能体审计。
概览
仓库信息
安装这个 Skill
npm install -g imcodesRegistry 信息
npm install -g imcodesgit clone https://github.com/im4codes/imcodes.git && cd imcodes
项目简介
imcodes 是一个面向 AI 智能体的即时通讯平台,提供跨多个 AI 提供商(包括 Claude、OpenAI、Gemini 和 Codex)的共享智能体上下文与记忆、监督执行以及跨智能体审计功能。
The IM for agents. Shared Agent Context & Memory, supervised execution, and cross-agent audit across AI providers.
要点
- 跨 AI 提供商的共享智能体上下文与记忆
- 智能体行为的监督执行
- 跨智能体审计日志与监控
- 多提供商支持:Claude、OpenAI、Gemini、Codex
- 面向智能体通信的即时通讯界面
使用场景
- 在复杂工作流中协调多个 AI 智能体
- 跨提供商审计和监控智能体决策
- 构建具有共享记忆的多智能体系统
- 在生产环境中监督自主智能体执行
README 摘要
# [IM.codes](https://im.codes) [English](README.md) | [简体中文](README.i18n/README.zh-CN.md) | [繁體中文](README.i18n/README.zh-TW.md) | [Español](README.i18n/README.es.md) | [Русский](README.i18n/README.ru.md) | [日本語](README.i18n/README.ja.md) | [한국어](README.i18n/README.ko.md) **The IM for agents. Shared memory, managed MCP tools, supervised execution, and cross-agent audit across AI providers.** > Two heads are better than one.<br> > But minds in concert don't answer fate, they author it.<br> > — IM.codes IM.codes gives coding agents one shared memory layer and one managed MCP tool surface across providers. It turns completed work into reusable context, then injects or recalls the right history in future sessions across [Claude Code](https://github.com/anthropics/claude-code), [Codex](https://github.com/openai/codex), [Gemini CLI](https://github.com/google-gemini/gemini-cli), GitHub Copilot, Cursor, OpenCode, [OpenClaw](https://openclaw.com), [Qwen](https://github.com/QwenLM/qwen-agent), and more — with terminal access, file browsing, git views, localhost preview, notifications, multi-agent workflows, and native streaming output for transport-backed agents. Built-in Auto supervision can judge completed turns, continue work autonomously, and optionally run an audit/rework loop before handing control back. Team discussion lets multiple models review and audit each other's plans and implementations — an effective way to reduce single-model misses, blind spots, and biases. > **Disclaimer:** This is an actively developed personal open-source project. There are no warranties, no SLA, and no guarantees of stability, security, or backward compatibility. Use at your own risk. Breaking changes may happen at any time without notice. ### Breaking Changes - **PostgreSQL default im