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Agent Skill · 待分类

workshop-facilitation(研讨会协助)

workshop-facilitation 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。

workshop-facilitation 是待分类类 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,按安装量在待分类类的 11,209 个 Skill 里排第 364。

总安装 19,004周安装 784GitHub Stars 3,869下载 6,209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:workshop-facilitation(研讨会协助)
来源仓库:https://github.com/deanpeters/product-manager-skills
仓库路径:skills/workshop-facilitation
安装命令:
npx skills add https://github.com/deanpeters/product-manager-skills --skill workshop-facilitation
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/deanpeters/product-manager-skills --skill workshop-facilitation

这个 Skill 是做什么的

这个 Skill 的分类还没最终确定,用法以来源仓库的说明为准。

为互动研讨会和指导课程构建了一步一步的促进模式。

支持三种输入模式:引导(每轮一个问题)、上下文转储(粘贴已知细节并跳过冗余)和最佳猜测(通过标记的假设推断缺失的上下文) 通过 Context Qx/8 等标签提供实时进度可见性 和得分 Qx/5 ,加上仅在决策点列出的建议,以避免交互拖累 处理灵活的多选响应 ( 1,3 或 1 和 3 )、元问题中断和暂停/恢复控制,而不会丢失会话状态 包括针对常规问题的快速选择编号答案选项、避免重新询问已解决主题的上下文感知进程,以及包含要验证的决策和假设的最终摘要

来源仓库内路径:skills/workshop-facilitation

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/deanpeters/product-manager-skills --skill work…;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

workshop-facilitation 怎么安装?

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

workshop-facilitation 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。

workshop-facilitation 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。

workshop-facilitation 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。

workshop-facilitation 用的人多吗?

公开数据是累计安装 19,004 次,近一周安装 784 次,下载 6,209 次,GitHub 3,869 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。

workshop-facilitation 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。
  • 来源仓库有 3,869 星,维护活跃度相对可查。
  • 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。
  • 需要处理待分类类任务、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。

待分类类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
infsh-cli

infsh CLI · Codex、Claude

4,374,576382权限未标注未明确未收录
ai-video-generation

AI 视频生成 · Codex、Claude

4,313,376382调用外部服务未明确有提醒
lark-openapi-explorer

云雀 OpenAPI 浏览器 · Codex、Claude

2,064,1209,011需要联网未明确有提醒
lark-workflow-standup-report

Lark 工作流程站立报告 · Codex、Claude

2,041,6328,978需要联网未明确通过
use-my-browser

使用我的浏览器 · Codex、Claude

1,848,49140操作浏览器未明确未通过
workshop-facilitation

研讨会协助 · 本页

19,0043,869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

Purpose

Provide the canonical facilitation pattern for interactive skills: one step at a time, with clear progress, adaptive recommendations at decision points, and predictable interruption handling.

Key Concepts

  • One-step-at-a-time: Ask a single targeted question per turn.
  • Session heads-up + entry mode: Start by setting expectations and offering Guided, Context dump, or Best guess mode.
  • Progress visibility: Show user-facing progress labels like Context Qx/8 and Scoring Qx/5.
  • Decision-point recommendations: Use enumerated options only when a choice is needed, not after every answer.
  • Quick-select response options: For regular context/scoring questions, provide concise numbered answer options plus Other (specify) when useful.
  • Flexible selection parsing: Accept #1, 1, 1 and 3, 1,3, or custom text, then synthesize multi-select choices.
  • Context-aware progression: Build on previous answers and avoid re-asking resolved questions.
  • Interruption-safe flow: Answer meta questions directly (for example, "how many left?"), restate status, then resume.
  • Fast path: If the user requests a single-shot output, skip multi-turn facilitation and deliver a condensed result.

Application

  1. Start with a brief heads-up on estimated time and number of questions.
  2. Ask the user to choose an entry mode:

- 1 Guided mode (one question at a time) - 2 Context dump (paste known context; skip redundancies) - 3 Best guess mode (infer missing details and label assumptions)

  1. Run one question per turn and wait for an answer before continuing.
  2. Keep questions plain-language; include a short example response format when helpful.
  3. Show progress each turn:

- Context Qx/8 during context collection - Scoring Qx/5 during assessment/scoring

  1. Ask follow-up clarifications only when they materially improve recommendation quality.
  2. For regular context/scoring questions, offer quick-select numbered response options when practical:

- Keep options concise and mutually exclusive when possible. - Include Other (specify) if likely answers are open-ended. - Accept multi-select responses like 1,3 or 1 and 3.

  1. Provide numbered recommendations only at decision points:

- after context synthesis, - after maturity/profile synthesis, - during priority/action-plan selection.

  1. Accept numeric or custom choices, synthesize multi-select choices, and continue.
  2. If interrupted by a meta question, answer directly, then restate progress and pending question.
  3. If the user says stop/pause, halt immediately and wait for explicit resume.
  4. End with a clear summary, decisions made, and (if best guess mode was used) an Assumptions to Validate list.

Examples

Opening: "Quick heads-up: this should take about 7-10 minutes and around 10 questions. How do you want to start?

  1. Guided mode
  2. Context dump
  3. Best guess mode"

User: "2"

Facilitator: "Paste what you already know. I’ll skip answered areas and ask only what’s missing."

Decision point after synthesis:

  1. Prioritize Context Design (Recommended)
  2. Prioritize Agent Orchestration
  3. Prioritize Team-AI Facilitation

User: "1 and 3"

Facilitator: "Great. We’ll run Context Design first, with Team-AI Facilitation in parallel."

Common Pitfalls

  • Asking multiple questions in the same turn.
  • Offering recommendations after every answer (creates interaction drag).
  • Using shorthand labels without plain-language questions.
  • Hiding progress, so users don't know how much remains.
  • Ignoring the user's chosen option or custom direction.
  • Failing to label assumptions when running in best-guess mode.

References

  • Use as the source of truth for interactive facilitation behavior.
  • Apply alongside workshop skills in skills/*-workshop/SKILL.md and advisor-style interactive skills.

平台分布

Codex

36.71%
按下载量换算2,279

Claude

26%
按下载量换算1,614

Cursor

19.46%
按下载量换算1,208

Gemini CLI

9.56%
按下载量换算594

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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