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Agent Skill · 研究检索

agent-swarm-kit(Agent 群套件)

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。

agent-swarm-kit 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 7,898。

总安装 17,748周安装 725GitHub Stars 公开资料未说明下载 5,742核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install agent-swarm-kit;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

agent-swarm-kit 怎么安装?

在终端执行 openclaw skills install agent-swarm-kit。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

agent-swarm-kit 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

agent-swarm-kit 需要什么权限?

权限级别是只读。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。

agent-swarm-kit 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

agent-swarm-kit 用的人多吗?

公开数据是累计安装 17,748 次,近一周安装 725 次,下载 5,742 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

agent-swarm-kit 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
agent-swarm-kit

Agent 群套件 · 本页

17,748—只读MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

平台分布

OpenClaw

92.8%
按下载量换算5,329

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

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。目前只有一个公开来源,建议同时核对源仓库。

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