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workshop-facilitation研讨会协助

Agent Skill

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。

总安装

19,004

周安装

784

GitHub Stars

3,869

下载量

6,209
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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

SKILL.md

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.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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