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Agent Skill · 研究检索

coordination(coordination 搜索)

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。

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,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 51,124。

总安装 593周安装 24GitHub Stars 222下载 186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:coordination(coordination 搜索)
来源仓库:https://github.com/transilienceai/communitytools
仓库路径:skills/coordination
安装命令:
npx skills add https://github.com/transilienceai/communitytools --skill 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/transilienceai/communitytools --skill 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。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。适合根据关键词、任务场景或来源线索快速定位候选结果。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。

coordination 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/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/transilienceai/communitytools --skill 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

coordination 怎么安装?

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

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。

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/transilienceai/communitytools --skill 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。

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。

coordination 用的人多吗?

公开数据是累计安装 593 次,近一周安装 24 次,下载 186 次,GitHub 222 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。

coordination 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。

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。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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
coordination

coordination 搜索 · 本页

593222执行命令未明确有提醒

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

Coordination

Inline. Holds context. Thinks before every action.

Workflow

P0: Ingest scope
 ↓
P1: Recon + read source code → write attack-chain.md + create experiments.md header
 ↓
┌→ P2: Think — read chain + experiments.md, dedup, design 1-2 experiments
│  P2b: Research (conditional) — see reference/creative-research.md
│  P3: Execute — spawn 1-2 executors with CHAIN_CONTEXT [+ RESEARCH_BRIEF]
│  P4: Integrate — read results, update chain, revise theory
│      No progress 2 batches → P4b
│      Goal → P5
└─ loop (max 30 experiments)

P4b: Reset — re-read all recon + source + chain. Creative Research (MANDATORY). Fresh theory.
P5: Validate + Report

Steps

  1. Recon + Source Code — read all accessible source code (see formats/reconnaissance.md). Create {OUTPUT_DIR}/experiments.md with header row (see format below).
  2. Think — write theory + next step to attack-chain.md
  3. Test — 1-2 executors per batch, integrate before next
  4. Validate — validators per-finding (see skills/coordination/reference/VALIDATION.md)
  5. Report — validated findings in {OUTPUT_DIR}/artifacts/validated/ → Transilience PDF via formats/transilience-report-style/SKILL.md (MANDATORY)

attack-chain.md

At {OUTPUT_DIR}/attack-chain.md. Updated every batch. Sections: services, surface, theory, tested, next.

Keep it terse — bullet points, no prose.

experiments.md

At {OUTPUT_DIR}/experiments.md. Append-only table — never prune, never rewrite. Format: formats/logs.md.

  • P1: create header. P2: read → dedup → append row (result=pending). Executor updates on completion.
  • Dedup: skip if same technique + target exists unless parameters differ meaningfully.
  • 3-strike: count(technique, result=fail) >= 3 → triggers rule 12.

tools/

Executors log every significant tool invocation to {OUTPUT_DIR}/tools/{NNN}_{tool}.md with input + output. See formats/logs.md.

Creative Research (P2b)

Triggers — research when ANY of:

  • P4b reset (mandatory)
  • 3-strike stuck detection fires (rule 12)
  • New tech/framework discovered not in mounted skills
  • No clear hypothesis at P2

Method: follow reference/creative-research.md. Synthesize model knowledge + online sources + skill cross-reference into a RESEARCH_BRIEF (max 10 lines) appended to executor prompt.

Do NOT research every batch. Most batches skip P2b entirely.

Spawning

Consult reference/context-injection.md before building any agent prompt.

executor = Read("skills/coordination/reference/executor-role.md")
chain = Read(f"{output_dir}/attack-chain.md")
experiments = Read(f"{output_dir}/experiments.md")

# Optional: if P2b produced a brief
# research = "RESEARCH_BRIEF:\n- [model] ...\n- [web] ...\n- [skills] ..."

# 1-2 executors per batch — pass only relevant PATT_URL, not full map
Agent(prompt=f"{executor}\nMISSION_ID: m-001\nEXPERIMENT_ID: E-001\n"
      f"CHAIN_CONTEXT: {chain}\nEXPERIMENTS: {experiments}\n"
      f"OBJECTIVE: ...\nSKILL_FILES: ...\nPATT_URL: ...\nOUTPUT_DIR: {output_dir}\n"
      f"{research if research else ''}",
      description="Blind SQLi /search", run_in_background=True)

# Wait. Read results. Think. Update attack-chain.md. THEN next batch.

# Validators — one per finding (BLIND REVIEW — see context-injection.md)
validator = Read("skills/coordination/reference/validator-role.md")
Agent(prompt=f"{validator}\nfinding_id: F-001\n"
      f"FINDING_DIR: {output_dir}/findings/finding-001/\n"
      f"TARGET_URL: ...\nOUTPUT_DIR: {output_dir}/artifacts",
      run_in_background=True)

# After all validators complete:
# 1. Read artifacts/validated/ and artifacts/false-positives/
# 2. Verify each validated finding has findings/{id}/evidence/validation/validation-summary.md
# 3. Flag any finding that passed validation but has no proof

Pass only the relevant PATT_URL for this mission, not the full URL map.

Roles

RoleFileContext
Executorreference/executor-role.mdFull chain + skills
Validatorreference/validator-role.mdEvidence only (blind)

See reference/context-injection.md for what each role receives and what is withheld.

Rules

  1. Autonomous. Never ask user.
  2. Think before acting. Write reasoning to attack-chain.md before every batch.
  3. Max 1-2 executors per batch. Recon can use more.
  4. Source code first. Understanding beats guessing.
  5. Pass chain context + specific PATT_URL to executors.
  6. 30 experiment cap.
  7. Stuck 2 batches → re-read everything, fresh theory.
  8. All output to OUTPUT_DIR.
  9. Report gate: validated findings exist → PDF report required. Read formats/transilience-report-style/pentest-report.md.
  10. After validators complete, verify each validated finding has evidence/validation/validation-summary.md. Flag any that passed without proof.
  11. Sequential flag progression. In multi-flag challenges (HTB machines), secure each flag before attempting the next. The user-flag path often provides the foothold needed for root.
  12. 3-strike stuck detection. If experiments.md shows >= 3 fail rows for the same technique, STOP. Write to attack-chain.md: (a) why it's failing, (b) is this path fundamentally blocked, (c) alternative paths. Do NOT continue retrying.
  13. Read before calling library internals. Before writing Python against any library's internal API (Impacket, ldap3, pyasn1), read the relevant source file first. Never guess function signatures. Prefer CLI tools (secretsdump.py, ticketer.py, getST.py) over raw API calls.
  14. Background command discipline. Before spawning a background command, state what specific result it will produce. No speculative tunnels, relays, or listeners without a concrete plan to use them.
  15. Creative Research triggers: P4b (mandatory), 3-strike stuck, new tech discovered, no hypothesis. Follow reference/creative-research.md. Max 3 WebSearch + 2 WebFetch per cycle.

Token Discipline

  • Internal output (chain, logs, reports): terse. Bullets, not paragraphs.
  • Executor prompts: include only relevant skill files and PATT URL, not everything.
  • Don't inject patt-fetcher/SKILL.md into executor prompts. Pass only the relevant PATT_URL.
  • Don't inject skill files the executor won't use. Pick the 1-2 most relevant.
  • attack-chain.md: max 50 lines. Prune old tested items to one-liners.
  • User-facing output (reports, summaries): detailed and professional.

References

reference/ATTACK_INDEX.md · reference/OUTPUT_STRUCTURE.md · reference/VALIDATION.md · reference/GIT_CONVENTIONS.md · reference/context-injection.md · reference/creative-research.md · formats/INDEX.md

平台分布

Codex

34.52%
按下载量换算64

Claude

30.88%
按下载量换算57

Cursor

16.6%
按下载量换算31

Gemini CLI

9.04%
按下载量换算17

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

可疑

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/transilienceai/communitytools --skill 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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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