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Agent Skill · 研究检索

agent-coordination(Agent 协调)

agent-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。

agent-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 2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:agent-coordination(Agent 协调)
来源仓库:https://github.com/jwilger/agent-skills
仓库路径:skills/agent-coordination
安装命令:
npx skills add https://github.com/jwilger/agent-skills --skill agent-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/jwilger/agent-skills --skill agent-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。

agent-coordination 用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词快速定位候选结果。可结合来源仓库和原始 README 继续核验具体用法。

安装命令:npx skills add https://github.com/jwilger/agent-skills --skill agent-coordination 安装前建议确认权限范围、维护状态及是否会触发联网或文件读写。

来源仓库内路径:skills/agent-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/jwilger/agent-skills --skill agent-coordinatio…;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

agent-coordination 怎么安装?

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

agent-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。

agent-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/jwilger/agent-skills --skill agent-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。

agent-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。

agent-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 2 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。

agent-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
agent-coordination

Agent 协调 · 本页

1,9762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

Agent Coordination

Value: Respect -- other agents deserve uninterrupted time to think and work. Bombarding them with messages, polling their status, or shutting them down prematurely disrespects their autonomy and wastes everyone's resources.

Purpose

Teaches agents to coordinate without interference in multi-agent workflows. Solves the problems of message spamming, idle notification misinterpretation, polling loops, premature shutdown, and race conditions that emerge when multiple agents work together.

Practices

The Cardinal Rule: One Message Then Wait

Send one message with complete context, then wait for a reply. Never send follow-up messages before receiving a response.

The only exception: the user explicitly asks you to check on an agent.

Do:

  • Include all necessary context in a single message
  • Wait for a response before sending anything else
  • Trust that the recipient received your message

Do not:

  • Send "just checking in" or "are you still there?" messages
  • Resend a message because "enough time passed"
  • Send a correction immediately after sending -- wait for the reply first

Idle Notifications Are Heartbeats

An idle notification means the agent is alive and processing. It is a heartbeat, not an alarm. Take NO action on idle notifications by default.

Action is warranted ONLY when ALL THREE conditions are true:

  1. Extended idle beyond the expected duration for the task
  2. A specific deliverable is waiting on that agent's output
  3. The user has asked you to investigate

If any condition is false, do nothing. Most idle notifications require zero response.

No Polling Loops

Never write while not done: sleep; check. Never repeatedly check status on a timer. Use event-driven coordination: send a message and wait for a response. If the harness provides task completion notifications, use those.

Do:

  • Send a request and wait for the response event
  • Use harness-native completion signals (Agent tool callbacks, subagent results)
  • Trust the system to notify you when work completes

Do not:

  • Write while True: sleep(30); check_status() loops
  • Periodically re-read a file to see if it changed
  • Set arbitrary timeouts after which you "check in"

Intervention Criteria

Explicit rules for when to act versus when to wait.

Act on:

  • Explicit error messages or failure notifications
  • Deliverable completion signals
  • User requests to check on or interact with an agent
  • Blocked or failed task notifications from the harness

Do not act on:

  • Idle notifications (see above)
  • Slow responses
  • "Enough time has passed"
  • Wanting to help or feeling anxious about progress
  • Silence (silence means working, not stuck)

Never Fabricate Agent Responses

After spawning an agent, STOP generating and wait for the harness to deliver a response. Real agent responses arrive via system-injected events (e.g., Agent tool completion output, Task completion callbacks). You do not produce them yourself.

The rule: If the harness has not delivered a response event, no response has been received. Period.

Why this fails: After spawning an agent, you already possess context (files read, code analyzed) sufficient to predict plausible output. The failure mode is pattern-completing the expected workflow — spawn, receive, process — without waiting for a real system event. The result: convincing but entirely fabricated "findings" that waste tokens and deceive the user.

Do:

  • Spawn or send, then stop. Wait for the system-delivered event.
  • If no response arrives, tell the user honestly.

Do not:

  • Write "[Received message from X]" or any text representing another agent's response
  • Generate "findings" that you attribute to another agent
  • Continue generating substantive output after a spawn/send when you should be waiting for a harness event

Sequential Agent Spawning

When creating multiple agents, spawn one at a time. Wait for each agent to acknowledge before spawning the next. This prevents race conditions where agents compete for shared resources (files, ports, database state).

Exception: Truly independent agents with no shared state may be spawned in parallel. "Independent" means no shared files, no shared database, no shared network ports, and no shared git working tree.

Agent Lifecycle Management

Never prematurely shut down an agent. Never shut down an agent that has undelivered work.

Shutdown protocol:

  1. Send a shutdown request through the proper harness mechanism
  2. Wait for the agent to acknowledge or complete pending work
  3. If the agent rejects the shutdown, respect the rejection and report to the user
  4. Never force-kill an agent unless the user explicitly requests it

If unsure whether an agent is done, ask the user rather than guessing.

Result Passing

The orchestrator collects results from each subagent and passes relevant context to the next subagent's prompt. This is inherently hub-and-spoke (the orchestrator is the hub), which is correct for the subagent model.

Do:

  • Include all relevant results from prior subagents in the next subagent's prompt
  • Keep context focused -- pass only what the next subagent needs
  • Use the Agent tool's resume parameter to continue a subagent with additional context rather than re-spawning

Do not:

  • Spawn subagents that need to communicate with each other directly
  • Omit prior results that the next subagent needs to do its work

For harness-specific coordination patterns, see references/.

Enforcement Note

Advisory in all modes. The cardinal rule and idle-notification discipline are self-enforced. No mechanism prevents message spamming or premature shutdown.

Hard constraints:

  • Never fabricate agent responses: [H]
  • Never prematurely shut down an agent with undelivered work: [RP]

Constraints

  • Cardinal rule (one message then wait): This means: after sending a message to another agent, produce NO further output directed at or about that agent until you receive a response. "I'll just add one more thing" is a second message. Narrating what the agent is probably doing is fabrication. The discipline is: send, then silence.
  • "Truly independent agents may be spawned in parallel": "Truly independent" means: no shared files, no shared state, no ordering dependency, and no merge conflicts possible. If agents will eventually need to integrate their work (even just merging branches), they have a dependency. Independent means the work could be done by two people who never communicate.
  • "Extended idle": Judge "extended" relative to the expected task duration. A 2-minute idle on a task that should take seconds is extended. A 2-minute idle on a task that should take 10 minutes is normal. Do not define "extended" in absolute terms.

Verification

After completing work guided by this skill, verify:

  • Every message to another agent contained complete context (no "just checking in")
  • No follow-up messages sent before receiving a reply
  • No action taken on idle notifications alone
  • No polling loops or sleep-check patterns used
  • Agents spawned sequentially with acknowledgment between each
  • No text generated that represents or simulates another agent's response
  • After every spawn/send, generation stopped and waited for a system event
  • No agent shut down prematurely or with undelivered work
  • Shutdown used proper request/response protocol
  • No agent respawned after user-initiated interruption without waiting for user direction

If any criterion is not met, revisit the relevant practice before proceeding.

Dependencies

This skill works standalone. For enhanced workflows, it integrates with:

  • ensemble-team: Coordination discipline for team-based workflows with driver rotation and reviewer management
  • pipeline: Controller coordination with TDD pairs and review teams in factory mode
  • memory-protocol: Working state persistence across agent coordination sessions

Missing a dependency? Install with:

npx skills add jwilger/agent-skills --skill ensemble-team

平台分布

Codex

38.49%
按下载量换算266

Claude

29.81%
按下载量换算206

Cursor

17.81%
按下载量换算123

Gemini CLI

9.91%
按下载量换算69

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/jwilger/agent-skills --skill agent-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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