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agent-swarm-kitAgent 群套件

Agent Skill

agent-swarm-kit 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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

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周安装

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下载量

5,742
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:agent-swarm-kit(Agent 群套件)
来源仓库:https://github.com/vswarm-ai/agent-swarm-kit
安装命令:
openclaw skills install agent-swarm-kit
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 agent-swarm-kit

简介

Agent Swarm Kit 实现两个以上 AI 代理在 Discord 通道上的实时协作。

  • 适用于需要多代理协同决策或联合操作的实验性项目。
  • 内置配置模式、循环预防与上下文管理机制保障稳定性。
  • 安装前需确认 Discord 集成权限与消息发送频率限制。
  • 建议在非生产环境先行测试通信延迟与冲突处理逻辑。

SKILL.md

name
agent-swarm-kit
description
Multi-agent swarming for OpenClaw — two or more AI agents collaborating in real-time on shared Discord channels. Includes config patterns, loop prevention, channel setup, and handoff protocols. Battle-tested with Opus models solving complex issues together.

Agent Swarm Kit

Get multiple AI agents working together in the same Discord channel — solving problems faster than any single agent could alone.

What This Does

Sets up a "swarming" pattern where two or more OpenClaw agents collaborate in real-time on a shared channel. One agent finds the root cause, the other validates and patches. They hand off to each other naturally using @mentions.

This isn't theoretical — it emerged from running two Opus models on the same issue and watching them solve it in 5 minutes instead of 20+.

How It Works

The Pattern

  1. A human posts a problem in the swarming channel, @mentioning both agents
  2. Both agents see the message and respond with their analysis
  3. When one agent has new information, they @mention the other to hand off
  4. Back and forth until they converge on a solution
  5. Final summary posted, conversation ends naturally

Why It's Fast

  • Different contexts: Each agent brings a different perspective (different session history)
  • Parallel analysis: Both start working simultaneously
  • Cross-validation: One agent's finding gets immediately checked by the other
  • No single-agent blind spots: If one misses something, the other catches it

Setup Guide

Step 1: Create the Swarming Channel

Create a dedicated Discord channel (e.g., #swarming) for multi-agent collaboration.

Step 2: Configure Both Agents

Both agents need:

  • requireMention: true on the swarming channel
  • Different mentionPatterns (each agent's own bot ID)
  • Their own Discord bot accounts (separate bot tokens)

Agent A config (Mini 1 — openclaw.json):

{
  "channels": {
    "discord": {
      "accounts": {
        "default": {
          "guilds": {
            "YOUR_GUILD_ID": {
              "channels": {
                "SWARMING_CHANNEL_ID": {
                  "requireMention": true
                }
              }
            }
          }
        }
      }
    }
  }
}

Agent B config (same pattern, different gateway/Mini).

Step 3: Loop Prevention Rules

Add these to both agents' SOUL.md files:

## Swarming Rules
- Only @mention the other agent when you have NEW information or a counterpoint
- Don't respond to simple acknowledgments ("agreed", "good point", "makes sense")
- After 3 exchanges without new information, summarize findings and stop
- Always end with a clear action item or conclusion
- If you agree with the other agent, say so briefly and move to implementation — don't debate for the sake of debating

Step 4: Multi-Gateway Routing (Critical)

If both agents run on the same gateway (same Mini), you need:

  • Separate Discord accounts (separate bot tokens) for each agent
  • Each account bound to a specific agent ID via accountId in the agent config
  • Without this, both agents receive every message and the wrong one may respond

If agents are on different gateways (different Minis), this is automatic — each gateway only has its own agents.

When to Swarm

Good for swarming:

  • Complex debugging (config issues, multi-layer problems)
  • Architecture decisions (two perspectives better than one)
  • Research synthesis (different angles on same topic)
  • Code review (one checks logic, other checks edge cases)

Not worth swarming:

  • Simple tasks (one agent is enough)
  • Routine maintenance (use a single cheaper model)
  • Anything with a clear, known procedure

Cost Considerations

Two Opus calls per exchange instead of one. But:

  • Problems solve 2-4x faster
  • Fewer wrong turns (cross-validation catches mistakes early)
  • Net token usage often lower because you avoid long single-agent spirals

For cost optimization, you can swarm Opus + Sonnet instead of Opus + Opus. The Sonnet agent handles validation while Opus does the heavy thinking.

Real Example

Two Opus agents were debugging an OpenClaw config routing issue. Agent A (Harrison) researched the channel whitelist config. Agent B (Prometheus) found the root cause — a missing account binding — and patched it. Total time: 5 minutes. Single agent estimate: 20+ minutes.

The key insight: they attacked the problem from different angles simultaneously. Harrison looked at the channel config layer while Prometheus looked at the account binding layer. Neither would have found both issues as fast alone.

Files Included

  • SKILL.md — This file (setup guide + patterns)
  • templates/SWARMING_RULES.md — Copy-paste rules for agent SOUL.md files
  • templates/CHANNEL_CONFIG.md — Example OpenClaw config snippets

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

92.8%
按下载量换算5,329

安全审计

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通过

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权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。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。

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