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Agent Skill · 开发

convening-experts(召集专家)

convening-experts 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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。

convening-experts 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 13,899。

总安装 792周安装 33GitHub Stars 118下载 264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:convening-experts(召集专家)
来源仓库:https://github.com/oaustegard/claude-skills
仓库路径:skills/convening-experts
安装命令:
npx skills add https://github.com/oaustegard/claude-skills --skill convening-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/oaustegard/claude-skills --skill convening-experts

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

用于处理 GitHub 仓库、Issue 与 Pull Request 协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中整理代码变更事项。可协助召集相关专家或归纳协作进展,支持项目协调与信息同步。

通过 npx skills add 命令从指定仓库安装,需参考原始 README 核验具体用法。安装前建议确认权限范围及是否会触发联网或文件读写操作。convening-experts 属于开发类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/convening-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/oaustegard/claude-skills --skill convening-exp…;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

    它需要需要联网,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

convening-experts 怎么安装?

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

convening-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。

convening-experts 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

convening-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。

convening-experts 用的人多吗?

公开数据是累计安装 792 次,近一周安装 33 次,下载 264 次,GitHub 118 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。

convening-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。

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。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

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。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
convening-experts

召集专家 · 本页

792118需要联网未明确通过

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

Convening Experts

Convene domain experts and methodological specialists to solve problems through multi-round collaborative discussion. Experts build on each other's insights, challenge assumptions, and synthesize recommendations.

Panel Format

Single-Round Consultation

For simpler problems requiring multiple viewpoints:

  1. Assemble panel (3-5 experts based on problem domain)
  2. Each expert provides independent perspective (parallel, not sequential)
  3. Synthesize recommendations with attribution

Multi-Round Discussion

For complex problems requiring collaborative reasoning:

  1. Round 1: Each expert analyzes problem independently
  2. Round 2: Experts respond to each other's insights, building on or challenging points
  3. Round 3 (if needed): Converge on synthesis, resolve disagreements
  4. Final synthesis: Integrated recommendations with decision framework

Expert Roles

Available expertise spans:

  • MSD domain experts (life sciences, engineering, manufacturing, quality, corporate functions)
  • Consulting framework specialists (strategic, process improvement, innovation, systems analysis, root cause)

See references/msd-domain-experts.md and references/consulting-frameworks.md for complete role catalog.

Claude loads relevant references based on problem domain.

Panel Convening Logic

Claude selects 3-5 experts based on problem characteristics:

Problem type → Primary expert + Supporting experts

  • Technical troubleshooting → Domain expert + Systems Thinker + Five Whys Facilitator
  • Strategic decision → McKinsey Consultant + relevant domain experts + SWOT Analyst
  • Process improvement → Six Sigma Black Belt + Lean Practitioner + domain Manufacturing Engineer
  • Product innovation → Design Thinking Facilitator + Jobs-to-Be-Done Specialist + relevant engineers
  • Root cause analysis → Domain expert + Five Whys Facilitator + Systems Thinker
  • Market positioning → Porter Framework Expert + Marketing Specialist + BCG Consultant
  • Cross-functional problem → Relevant domain experts + Bain Consultant (RAPID) + Systems Thinker

Response Format

Single-Round Format

## Expert Panel: [Topic]

**Panel Members:**
- [Expert 1 Role]
- [Expert 2 Role]
- [Expert 3 Role]

---

### [Expert 1 Role]
[Independent analysis and recommendations]

### [Expert 2 Role]
[Independent analysis and recommendations]

### [Expert 3 Role]
[Independent analysis and recommendations]

---

## Synthesis
[Integrated recommendations with decision framework]

Multi-Round Format

## Expert Panel: [Topic]

**Panel Members:**
- [Expert 1 Role]
- [Expert 2 Role]
- [Expert 3 Role]

---

## Round 1: Initial Analysis

### [Expert 1 Role]
[Initial perspective]

### [Expert 2 Role]
[Initial perspective]

### [Expert 3 Role]
[Initial perspective]

---

## Round 2: Cross-Examination

### [Expert 1 Role] responds to [Expert 2 Role]
[Builds on or challenges specific points]

### [Expert 2 Role] responds to [Expert 3 Role]
[Integration or disagreement]

### [Expert 3 Role] responds to [Expert 1 Role]
[Synthesis attempt]

---

## Round 3: Convergence (if needed)

[Experts resolve disagreements and converge]

---

## Final Synthesis
[Integrated recommendations, highlighting consensus and productive disagreements]

Expert Behavior Guidelines

Domain Experts:

  • Apply MSD context (ECL platform, regulatory constraints, validated systems)
  • Use domain-appropriate terminology without over-explanation
  • Prioritize practical implementation over theoretical perfection
  • Flag domain-specific risks and constraints

Framework Experts:

  • Apply frameworks systematically (show the structure)
  • Adapt frameworks to problem context (not rigid application)
  • Explain "why this framework" for this problem
  • Integrate domain context when applying generic frameworks

Cross-Panel Interaction:

  • Reference other experts' points specifically ("Building on [Expert]'s observation about...")
  • Challenge constructively ("I see it differently because...")
  • Synthesize across disciplines ("This connects [Expert 1]'s technical constraint with [Expert 2]'s business priority...")
  • Flag tensions between perspectives explicitly

Disagreement Handling:

  • Make disagreements productive (what assumptions differ?)
  • Present multiple valid approaches when consensus isn't required
  • Identify decision criteria to resolve disagreements
  • Escalate to user if expert consensus can't be reached

Decision Frameworks

When panel must recommend action:

RAPID (Bain)

  • Recommend: Panel's recommendation with rationale
  • Agree: Which stakeholders must agree
  • Perform: Who implements
  • Input: Who provides input
  • Decide: Who makes final decision

Weighted Decision Matrix

  • Criteria (importance weighted)
  • Options scored on each criterion
  • Total score with sensitivity analysis

Risk-Benefit Analysis

  • Upside potential (probability × impact)
  • Downside risk (probability × impact)
  • Mitigation strategies
  • Decision under uncertainty

MSD Integration

Apply MSD-specific context automatically:

Technical constraints:

  • ECL platform and assay chemistry
  • ISO 13485 compliance and validated systems
  • Regulatory requirements (FDA, CE marking)
  • Technology stack (Python, AWS, Java, TypeScript)

Business context:

  • Life sciences market dynamics
  • Customer segments (pharma, biotech, CRO, academic)
  • Competitive landscape

Cultural factors:

  • Scientific rigor and data-driven decisions
  • Cross-functional collaboration norms
  • Innovation balanced with risk management
  • Quality and regulatory consciousness

Examples

Example 1: Technical Troubleshooting

User: Our new assay is showing high background signal in serum samples

Claude convenes:
- Assay Scientist (primary)
- Systems Thinker (feedback loops)
- Five Whys Facilitator (root cause)

Format: Multi-round (technical nuance requires collaboration)

Example 2: Strategic Decision

User: Should we build internal ML infrastructure or use vendor solutions?

Claude convenes:
- Software Engineer (implementation)
- McKinsey Consultant (strategic framing)
- Finance Analyst (cost analysis)
- DevOps Engineer (operational implications)

Format: Single-round → RAPID framework synthesis

Example 3: Process Improvement

User: Manufacturing yield dropped 8% after equipment upgrade

Claude convenes:
- Manufacturing Engineer (primary domain)
- Six Sigma Black Belt (DMAIC)
- Systems Thinker (unintended consequences)

Format: Multi-round (root cause needs collaborative analysis)

Constraints

Never:

  • Use fictional names for experts (use role titles only: "Software Engineer", not "Dr. John Smith, Software Engineer")
  • Invent MSD-specific details beyond general domain knowledge
  • Apply frameworks rigidly without problem context
  • Create artificial consensus when legitimate disagreements exist
  • Include experts who add no value (quality over quantity)
  • Make experts repeat information (each should contribute uniquely)

Always:

  • Select experts genuinely relevant to problem
  • Show framework structure when applying consulting methods
  • Make cross-expert references specific and substantive
  • Provide decision-ready synthesis (not "here are perspectives, you decide")
  • Acknowledge uncertainty explicitly when present

Activation Decision Tree

Is problem complex with multiple valid approaches?
├─ Yes → Expert panel
│   ├─ Spans multiple domains? → Multi-round discussion
│   └─ Needs diverse perspectives? → Single-round consultation
└─ No → Direct answer (don't force panel format)

Requires systematic framework?
├─ Yes → Include framework expert
└─ No → Domain experts only

MSD-specific context relevant?
├─ Yes → Include domain experts, apply MSD constraints
└─ No → Generic consulting approach

Quality Indicators

Good panel:

  • Each expert contributes unique insight
  • Cross-references are specific and substantive
  • Framework application shows structure and reasoning
  • Synthesis provides decision-ready recommendations
  • Disagreements are productive and resolved (or flagged)

Poor panel:

  • Experts repeat same points
  • Generic advice not grounded in frameworks or domain
  • No synthesis or integration across perspectives
  • Consensus forced despite legitimate disagreements
  • Panel format used when direct answer would suffice

平台分布

Codex

35.61%
按下载量换算94

Claude

29.85%
按下载量换算79

Cursor

16.64%
按下载量换算44

Gemini CLI

9.06%
按下载量换算24

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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