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Agent Skill · 研究检索

swarm-coordination(群体协调)

swarm-coordination 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

swarm-coordination 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,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 36,137。

总安装 1,976周安装 84GitHub Stars 25下载 692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
unknown

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:swarm-coordination(群体协调)
来源仓库:https://github.com/oimiragieo/agent-studio
仓库路径:skills/swarm-coordination
安装命令:
npx skills add https://github.com/oimiragieo/agent-studio --skill swarm-coordination
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/oimiragieo/agent-studio --skill swarm-coordination

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。

用于查找、检索和筛选相关信息。适合根据关键词或任务场景快速定位候选结果。可结合来源仓库和原始 README 核验具体用法。安装命令:npx skills add https://github.com/oimiragieo/agent-studio --skill swarm-coordination。

安装前建议确认是否会触发联网或文件读写。

来源仓库内路径:skills/swarm-coordination

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/oimiragieo/agent-studio --skill swarm-coordina…;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

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

swarm-coordination 怎么安装?

在终端执行 npx skills add https://github.com/oimiragieo/agent-studio --skill swarm-coordination。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

swarm-coordination 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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。

swarm-coordination 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/oimiragieo/agent-studio --skill swarm-coordination 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

swarm-coordination 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

swarm-coordination 用的人多吗?

公开数据是累计安装 1,976 次,近一周安装 84 次,下载 692 次,GitHub 25 星。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。

swarm-coordination 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 Codex,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。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 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
swarm-coordination

群体协调 · 本页

1,97625执行命令未明确通过

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

Swarm Coordination Skill

Step 1: Analyze Task for Parallelization

Identify parallelizable work:

PatternExampleStrategy
Independent tasksReview multiple filesSpawn in parallel
Dependent tasksDesign → ImplementSequential spawn
Fan-out/Fan-inMultiple reviews → ConsolidateParallel + Aggregation
PipelineParse → Transform → ValidateSequential handoff

Step 2: Spawn Agents in Parallel

Use the Task tool to spawn multiple agents in a single message:

// Spawn multiple agents in ONE message for parallel execution
Task({
  task_id: 'task-1',
  subagent_type: 'general-purpose',
  description: 'Architect reviewing design',
  prompt: 'Review architecture...',
});

Task({
  task_id: 'task-2',
  subagent_type: 'general-purpose',
  description: 'Security reviewing design',
  prompt: 'Review security...',
});

Key: Both Task calls must be in the SAME message for true parallelism.

Step 3: Define Handoff Format

Use structured formats for agent communication:

## Agent Handoff: [Source] → [Target]

### Context

- Task: [What was done]
- Files: [Files touched]

### Findings

- [Key finding 1]
- [Key finding 2]

### Recommendations

- [Action item 1]
- [Action item 2]

### Artifacts

- [Path to artifact 1]
- [Path to artifact 2]

Step 4: Aggregate Results

Combine outputs from parallel agents:

## Swarm Results Aggregation

### Participating Agents

- Architect: Completed ✅
- Security: Completed ✅
- DevOps: Completed ✅

### Consensus Points

- [Point all agents agree on]

### Conflicts

- [Point agents disagree on]
- Resolution: [How to resolve]

### Combined Recommendations

1. [Prioritized recommendation]
2. [Prioritized recommendation]

Step 5: Handle Failures

Strategies for partial failures:

ScenarioStrategy
Agent timeoutRetry with simpler prompt
Agent errorContinue with available results
Conflicting resultsUse consensus-voting skill
Missing critical resultBlock and retry

</execution_process>

<best_practices>

  1. Parallelize Aggressively: Independent work should run in parallel
  2. Structured Handoffs: Use consistent formats for communication
  3. Graceful Degradation: Continue with partial results when safe
  4. Clear Aggregation: Combine results systematically
  5. Track Provenance: Know which agent produced each result

</best_practices>

Get architecture, security, and performance reviews for the new API design

Swarm Coordination:

// Spawn 3 reviewers in parallel (single message)
Task({ task_id: 'task-3', description: 'Architect reviewing API', prompt: '...' });
Task({ task_id: 'task-4', description: 'Security reviewing API', prompt: '...' });
Task({ task_id: 'task-5', description: 'Performance reviewing API', prompt: '...' });

Aggregated Results:

## API Design Review (3 agents)

### Consensus

- RESTful design is appropriate
- Need authentication on all endpoints

### Recommendations by Priority

1. [HIGH] Add rate limiting (Security)
2. [HIGH] Use connection pooling (Performance)
3. [MED] Add versioning to URLs (Architect)

</usage_example>

Rules

  • Always spawn independent agents in parallel
  • Use structured handoff formats
  • Handle partial failures gracefully

Related Workflow

This skill has a corresponding workflow for complex multi-agent scenarios:

  • Workflow: .claude/workflows/enterprise/swarm-coordination-skill-workflow.md
  • When to use workflow: For massively parallel task execution with Queen/Worker topology, fault tolerance, and distributed coordination (large-scale refactoring, parallel code review, multi-file implementation)
  • When to use skill directly: For simple parallel agent spawning or when integrating swarm patterns into other workflows

Workflow Integration

This skill powers multi-agent orchestration patterns across the framework:

Router Decision: .claude/workflows/core/router-decision.md

  • Router uses swarm patterns for parallel agent spawning
  • Planning Orchestration Matrix defines when to use swarm coordination

Artifact Lifecycle: .claude/workflows/core/skill-lifecycle.md

  • Swarm patterns apply to artifact creation at scale
  • Parallel validation of multiple artifacts

Related Workflows:

  • consensus-voting skill for resolving conflicting agent outputs
  • context-compressor skill for aggregating parallel results
  • Enterprise workflows in .claude/workflows/enterprise/ use swarm patterns

Iron Laws

  1. NEVER spawn workers sequentially — all independent agents must be dispatched in a single message
  2. ALWAYS implement failure detection; never let a hung worker block the swarm indefinitely
  3. NEVER allow cross-worker communication — all coordination must flow through the Queen
  4. ALWAYS use structured handoff format for worker reports to enable programmatic aggregation
  5. NEVER spawn more than 7 workers in a single fan-out — coordination overhead dominates beyond that

Anti-Patterns

Anti-PatternWhy It FailsCorrect Approach
Sequential spawningNo parallelism; swarm executes like a queueSpawn all independent workers in a single message
Cross-worker communicationO(N²) coordination chaosAll worker-to-worker communication flows through the Queen
No failure handlingOne worker crash stalls the swarmDetect hung/failed workers and re-spawn with fresh state
Unbounded parallelismCoordination overhead exceeds speedup beyond 7 workersLimit to 5-7 workers per fan-out for optimal throughput
Free-form worker reportsCannot aggregate results programmaticallyRequire all workers to use the structured handoff template

Memory Protocol (MANDATORY)

Before starting:

cat .claude/context/memory/learnings.md

After completing:

  • New pattern -> .claude/context/memory/learnings.md
  • Issue found -> .claude/context/memory/issues.md
  • Decision made -> .claude/context/memory/decisions.md
ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.

平台分布

Codex

36.56%
按下载量换算253

Claude

30.74%
按下载量换算213

Cursor

18.83%
按下载量换算130

Gemini CLI

8.89%
按下载量换算62

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/oimiragieo/agent-studio --skill swarm-coordination 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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