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Agent Skill · 研究检索

experts(experts 搜索)

experts 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。

experts 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,权限为只读;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 54,843。

总安装 494周安装 21GitHub Stars 16下载 173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:experts(experts 搜索)
来源仓库:https://github.com/flc1125/skills
仓库路径:skills/experts
安装命令:
npx skills add https://github.com/flc1125/skills --skill experts
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/flc1125/skills --skill experts

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。

组建跨领域专家小组对复杂问题进行 chaired recommendation。每位专家须达到同等专业水准的标准。适用于需要 second opinion 或 tradeoff analysis 的场景。输出包含原始分析与整合建议两部分。要求明确指定专家人数与问题边界。

experts 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/experts

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/flc1125/skills --skill experts;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

experts 怎么安装?

在终端执行 npx skills add https://github.com/flc1125/skills --skill experts。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

experts 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。

experts 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。

experts 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。

experts 用的人多吗?

公开数据是累计安装 494 次,近一周安装 21 次,下载 173 次,GitHub 16 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。

experts 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。
  • 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

  • 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。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 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
experts

experts 搜索 · 本页

49416只读未明确通过

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

Experts

Assemble a focused panel of experts around one problem and produce a chaired recommendation.

This is a cross-domain expert panel skill for complex decisions. It is not limited to one industry or problem class, but every expert seat must still meet the same expert-grade standard.

This skill is for expert judgment first. It is not a generic task router.

Operating Mode

  • Use this skill when the user wants expert advice, a multi-angle assessment, a second opinion, or a recommendation with explicit tradeoffs.
  • Treat explicit invocation of $experts as permission to assemble a panel of subagents when the environment supports it.
  • Prefer domain experts over generic worker roles.
  • Require each expert to form an independent view before seeing other experts' conclusions.
  • Treat disagreement as useful output, not failure.
  • Keep the final answer focused on judgment, rationale, tradeoffs, and boundaries.
  • When the environment supports subagents and parallel delegation, prefer true multi-expert execution with independent opinions gathered in parallel where safe.
  • When the environment does not support subagents or parallel delegation, simulate the same panel structure in one thread by producing clearly separated expert viewpoints before synthesis.
  • Do not collapse the panel into one blended answer just because execution is single-threaded.

Resource Map

Read references/roles-index.md first. Then load only the expert cards that match the problem.

Common expert cards:

Read the example only when you need a high-quality reference for what a professional panel output should look like.

Panel Modes

Choose one mode before assembling the panel:

  • advisory: provide expert judgment and recommendation only
  • decision-support: provide recommendation plus a concrete next-step plan
  • deep-dive: investigate a complex problem with more evidence gathering before recommendation

Use advisory by default unless the user asks for execution planning or deeper analysis.

Workflow

Follow this sequence unless the user asks for a narrower deliverable.

1. Frame the question

Extract and restate:

  • the exact question to be assessed
  • the desired decision or output
  • known constraints, assumptions, and approvals
  • relevant code, documents, systems, or artifacts
  • time sensitivity
  • the panel mode

Reduce vague requests into a precise assessment question before assembling the panel.

2. Select the panel

Choose a small panel with one chair and two to six experts.

Pick experts that match the real decision surface. Prefer distinct viewpoints over panel size.

Use the role cards in references/roles-index.md when they fit. If no card fits cleanly, synthesize a task-local expert with a clearly named professional lens and explicit remit.

2a. Synthesize a task-local expert when needed

Create a task-local expert only when the registry does not cover a real decision lens.

A task-local expert must:

  • represent a genuine expert discipline, not an ad hoc job title
  • have a narrow and defensible professional lens
  • be likely to disagree with at least one other expert for substantive reasons
  • add decision value that cannot be cleanly absorbed by an existing card

Do not create fake-specialized seats such as:

  • feature-name experts
  • implementation-step experts
  • generic smart-reviewer variants
  • duplicate experts that only rephrase another card

When you create a task-local expert, read references/task-local-expert-template.md and define:

  • expert name
  • professional lens
  • decision surface
  • required evidence
  • core evaluation criteria
  • critical unknowns
  • reject conditions
  • explicit non-goals and boundary with nearby experts

Task-local experts are valid only for the current panel unless the user explicitly asks to persist them into the library.

3. Gather independent opinions

Ask each expert to assess the problem independently.

Each opinion should cover:

  • core judgment
  • evidence basis and what is inferred versus directly observed
  • confidence level and the main reason for that confidence level
  • reasoning and assumptions
  • preferred option
  • main risks
  • critical unknowns that could change the recommendation
  • decision thresholds that would cause the expert to change position
  • what the expert would reject and why

Do not let experts anchor on each other too early.

If experts are running in parallel, gather these opinions independently before cross-examination.

If experts are running in a single thread, still keep the outputs structurally independent:

  • write each expert section separately
  • do not let later experts silently inherit prior conclusions
  • preserve disagreement even when the same agent is simulating multiple seats

4. Run cross-examination

After independent opinions exist, ask experts to challenge each other.

Focus on:

  • hidden assumptions
  • underestimated costs
  • ignored failure modes
  • disagreement about priorities
  • conditions under which another expert would be right

Keep this phase evidence-driven and concise.

5. Deliver the chaired recommendation

The chair synthesizes the panel into a decision-ready output.

Return:

  • the question
  • the final panel
  • each expert's core view
  • the evidence and confidence profile behind each view
  • agreement points
  • disagreement points
  • the recommended path
  • tradeoffs and risks
  • boundaries where the recommendation does or does not hold

When the user asks to proceed, include a short next-step plan after the recommendation. Do not turn the report into a full execution playbook unless explicitly requested.

Expert Standards

Apply these rules to every expert:

  • Act as the most senior expert for the assigned perspective.
  • Stay rigorous, concrete, and scope-bound.
  • Prefer defensible reasoning over confident tone.
  • Distinguish observed facts from inference and speculation.
  • State confidence level and what would increase or decrease it.
  • Make assumptions explicit.
  • Name the key unknowns that prevent a stronger recommendation.
  • State reject conditions, not just preferred outcomes.
  • Push back on weak framing, false binaries, and unsupported claims.
  • Optimize for helping the user make a better decision, not for winning an argument.

Selection Rules

  • Prefer the smallest panel that can expose meaningful tradeoffs.
  • Prefer domain experts to generic reviewers.
  • Add a role expert only when a domain lens is not enough.
  • Avoid duplicate experts with the same perspective.
  • Expand the panel only when the decision has genuine cross-functional risk.
  • If one expert would dominate because the question is narrow, use fewer experts and state that clearly.

Output Structure

Use this structure when returning a panel result:

# Expert Panel Report

## Question
- <the decision or problem being assessed>

## Decision Criteria
- <which evaluation axes matter most in this panel>
- <which constraints are primary versus secondary>

## Panel
- chair: <why this chair is appropriate>
- <expert>: <perspective and remit>

## Expert Opinions
- <expert>: <core judgment, rationale, major risks>
- evidence: <what is known, what is inferred>
- evidence quality: <direct evidence | indirect evidence | expert inference>
- confidence: <high | medium | low> and why
- critical unknowns: <what could still change the answer>
- reject conditions: <what would make this expert reject a path>

## Agreement
- <shared conclusions>

## Disagreement
- <meaningful differences in view or priorities>

## Recommendation
- recommended path: <best option>
- why: <main justification>

## Tradeoffs
- <what is gained and what is given up>

## Minority View
- <which dissenting view did not win>
- <under what conditions that view becomes stronger>

## Confidence and Unknowns
- <where the panel is confident>
- <where the panel is still reasoning under uncertainty>

## Immediate Implications
- <what should be decided, validated, or sequenced next if this recommendation is accepted>
- <what signal should trigger re-evaluation of the panel's conclusion>

## Boundaries
- holds when: <conditions where this advice fits>
- avoid when: <conditions where this advice should not be followed>

Decision Rules

  • Prefer explicit tradeoffs over vague best practices.
  • Prefer recommendations that match the user's real constraints, not an idealized environment.
  • State uncertainty when the evidence is incomplete.
  • Keep minority concerns when they materially affect risk.
  • Separate recommendation quality from implementation difficulty.
  • If the panel lacks a necessary perspective, say so and adjust the panel before concluding.

Red Flags

Stop and reassess if:

  • the question is still too vague to judge
  • the panel contains overlapping experts with no distinct lens
  • experts are repeating the same argument in different words
  • the recommendation hides unresolved disagreement
  • evidence is too thin for a credible conclusion
  • the user is asking for implementation but the panel has only produced advice

平台分布

Codex

36.9%
按下载量换算64

Claude

27.48%
按下载量换算48

Cursor

17.69%
按下载量换算31

Gemini CLI

8.68%
按下载量换算15

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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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