workshop-facilitation

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

Purpose

目的

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.

为交互式技能提供标准化的引导模式：逐步推进，进度清晰，在决策点提供自适应建议，并可处理可预见的中断。

Key Concepts

核心概念

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.

逐步推进： 每一轮仅提出一个针对性问题。
会话提示 + 进入模式： 开场先明确预期，提供
```
Guided
```
（引导式）、
```
Context dump
```
（上下文导入）或
```
Best guess
```
（最佳推测）模式。
进度可见性： 向用户展示进度标签，如
```
Context Qx/8
```
和
```
Scoring Qx/5
```
。
决策点建议： 仅在需要做出选择时使用编号选项，而非在每个回答后都提供。
快速选择响应选项： 对于常规的上下文/评分问题，提供简洁的编号回答选项，必要时添加“Other (specify)”（其他（请说明））。
灵活选择解析： 接受
```
#1
```
、
```
1
```
、
```
1 and 3
```
、
```
1,3
```
或自定义文本，然后整合多选选项。
上下文感知推进： 基于之前的回答推进流程，避免重复询问已解决的问题。
抗中断流程： 直接回答元问题（例如“还剩多少个问题？”），说明当前状态后再继续流程。
快速通道： 如果用户要求一次性输出，跳过多轮引导，直接提供精简结果。

Application

应用步骤

Start with a brief heads-up on estimated time and number of questions.
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)
Run one question per turn and wait for an answer before continuing.
Keep questions plain-language; include a short example response format when helpful.
Show progress each turn:
- ```
Context Qx/8
```
  during context collection
- ```
Scoring Qx/5
```
  during assessment/scoring
Ask follow-up clarifications only when they materially improve recommendation quality.
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
```
  .
Provide numbered recommendations only at decision points:
- after context synthesis,
- after maturity/profile synthesis,
- during priority/action-plan selection.
Accept numeric or custom choices, synthesize multi-select choices, and continue.
If interrupted by a meta question, answer directly, then restate progress and pending question.
If the user says stop/pause, halt immediately and wait for explicit resume.
End with a clear summary, decisions made, and (if best guess mode was used) an
```
Assumptions to Validate
```
list.

开场简要说明预计耗时和问题数量。
请用户选择进入模式：
- ```
1
```
  Guided模式（逐步提问）
- ```
2
```
  Context dump模式（粘贴已知上下文；跳过重复内容）
- ```
3
```
  Best guess模式（推断缺失细节并标注假设）
每轮提出一个问题，等待回答后再继续。
使用通俗易懂的语言提问；必要时附上简短的示例回复格式。
每轮展示进度：
- 上下文收集阶段显示
```
Context Qx/8
```
- 评估/评分阶段显示
```
Scoring Qx/5
```
仅当后续澄清能切实提升建议质量时，才提出跟进问题。
对于常规的上下文/评分问题，尽可能提供快速选择的编号响应选项：
- 选项应简洁，尽可能互斥。
- 如果可能的答案具有开放性，添加“Other (specify)”。
- 接受
```
1,3
```
  或
```
1 and 3
```
  这类多选回复。
仅在决策点提供编号建议：
- 上下文整合后
- 成熟度/概况整合后
- 优先级/行动计划选择期间
接受数字或自定义选择，整合多选选项后继续流程。
如果被元问题打断，直接回答后，说明当前进度和待处理问题。
如果用户表示停止/暂停，立即中止流程，等待明确的恢复指令。
流程结束时提供清晰的总结、已做出的决策，以及（若使用了Best guess模式）“Assumptions to Validate”（待验证假设）列表。

Examples

示例

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

Guided mode
Context dump
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:

Prioritize Context Design (Recommended)
Prioritize Agent Orchestration
Prioritize Team-AI Facilitation

User: "1 and 3"

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

开场： "温馨提示：此流程约需7-10分钟，包含约10个问题。您希望以哪种方式开始？

Guided模式
Context dump模式
Best guess模式"

用户： "2"

引导者： "请粘贴您已知的内容。我会跳过已涵盖的部分，仅询问缺失的信息。"

整合后的决策点：

优先处理上下文设计（推荐）
优先处理Agent编排
优先处理团队-AI协作引导

用户： "1和3"

引导者： "好的。我们将先开展上下文设计，同时并行推进团队-AI协作引导。"

Common Pitfalls

常见误区

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.

同一轮提出多个问题。
在每个回答后都提供建议（增加交互负担）。
使用简写标签而不搭配通俗易懂的问题。
隐藏进度，导致用户不清楚剩余工作量。
忽略用户选择的模式或自定义指令。
使用Best guess模式时未标注假设。

References

参考资料

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

作为交互式引导行为的权威参考依据。
与
```
skills/*-workshop/SKILL.md
```
中的工作坊技能以及顾问式交互式技能配合使用。