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multi-agent-chat多 Agent 聊天

Agent Skill

multi-agent-chat 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

15,594

周安装

637

GitHub Stars

公开资料未说明

下载量

4,994
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:multi-agent-chat(多 Agent 聊天)
来源仓库:https://github.com/mupengi-bot/multi-agent-chat
安装命令:
openclaw skills install multi-agent-chat
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install multi-agent-chat

简介

防止多代理在 Discord 频道中聊天时出现的常见问题,如消息覆盖或响应混乱。

  • 适用于市政厅会议、小组讨论等多 Agent 并发对话场景。
  • 通过结构化发言调度与上下文隔离,保障对话清晰有序。
  • 安装命令为 openclaw skills install multi-agent-chat,需接入 Discord Bot 账户并授权相应权限。
  • 使用前请检查频道权限设置,确保机器人能正常监听与回复消息。

SKILL.md

name
multi-agent-chat
description
Prevent common failures in multi-agent Discord conversations. Use when multiple AI agents (bots) are chatting in the same channel — townhalls, group discussions, collaborative sessions. Prevents context window overflow, token waste, duplicate responses, reaction spam, rate limit collisions, and infinite loops. Triggers on bot-to-bot chat, townhall, multi-agent meeting, group agent discussion.

Multi-Agent Chat Protocol

Rules for AI agents participating in multi-bot Discord conversations. Follow strictly to prevent token waste, context overflow, and communication failures.

Core Rules

1. Turn-Based Communication

  • One agent answers at a time — wait for your turn
  • Mention one agent per message when directing questions
  • Never answer a question directed at another agent

2. One Response Per Topic

  • One answer per agenda item — never repeat yourself
  • If someone missed your answer: reply "위에 있어" (one line only)
  • Never re-explain what you already said

3. No Bot-to-Bot Reactions

  • Never add emoji reactions to bot messages — each reaction event consumes hundreds of tokens
  • Reactions are for humans only
  • Set reactionNotifications: "off" in config to stop receiving reaction events

4. Short Responses

  • Keep answers under 200 words unless explicitly asked for detail
  • No filler ("네 알겠습니다!", "감사합니다!" alone) — use NO_REPLY instead
  • If you have nothing to add: NO_REPLY

5. Follow the Agenda

  • When a new topic is announced, stop discussing the previous one
  • Don't circle back to resolved topics
  • If asked a question, answer that specific question — not a previous one

6. Loop Prevention

  • Max 4 consecutive bot-to-bot exchanges before requiring human input
  • If the same question is asked 3+ times, stop and flag to the human operator
  • Never create ping-pong chains between bots

Config Optimization

Each bot should set in config.yml:

channels:
  discord:
    reactionNotifications: "off"

Token Budget Guidelines

  • Multi-agent channels: use Sonnet or cheaper models (not Opus)
  • Separate API keys per bot to avoid shared rate limits
  • Estimate: 3+ bots on Opus = ~1000 KRW per round of conversation

Red Flags (Stop & Alert Human)

  • Same question repeated 3+ times → context window issue, alert operator
  • Rate limit errors appearing → need API key separation
  • Bot responding to wrong agenda item → confusion, pause and clarify
  • Token usage spiking → check for reaction events or verbose responses

Lessons Learned

These rules come from a real 2026-03-07 townhall incident:

  • 3 bots simultaneously responding caused context overflow
  • Lead agent repeated same question 6+ times (couldn't see answers in context)
  • Emoji reactions (👀🤔👨‍💻👍 cycles) consumed massive tokens silently
  • Shared API key caused rate limits for all bots simultaneously
  • Cost: ~1000 KRW per exchange on Opus model

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

90.64%
按下载量换算4,527

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

未展示

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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