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clarify%3aunknown澄清%3a 未知

Agent Skill

clarify%3aunknown 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。

总安装

1,448

周安装

58

GitHub Stars

7

下载量

469
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:clarify%3aunknown(澄清%3a 未知)
来源仓库:https://github.com/team-attention/workshop-upstage
仓库路径:skills/clarify%3Aunknown
安装命令:
npx skills add https://github.com/team-attention/workshop-upstage --skill clarify:unknown
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/team-attention/workshop-upstage --skill clarify:unknown

简介

利用已知/未知象限图暴露策略中的盲点与隐藏假设。

  • 通过假设驱动提问揭示“我们不知道什么”比“我们知道什么”更重要的事实。
  • 适用于战略规划文档审查与高风险决策的事前推演。
  • 输出四象限可视化分析,标注待填补的知识空白区域。
  • clarify%3aunknown 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Unknown: Surface Blind Spots with Known/Unknown Quadrants

Surface hidden assumptions and blind spots in any strategy, plan, or decision using the Known/Unknown quadrant framework and hypothesis-driven questioning.

When to Use

  • Strategy or planning documents that need scrutiny
  • Decisions with unclear direction or hidden assumptions
  • Any situation where "what we don't know" matters more than "what we do know"

For specific requirement clarification (feature requests, bug reports), use the vague skill. For content-vs-form reframing (optimizing within a form vs inventing a new form), use the metamedium skill.

Core Principle: Hypothesis-as-Options

ALWAYS use the AskUserQuestion tool for every question in R1/R2/R3 — never ask questions in plain text. The structured format enforces hypothesis-as-options and limits choice fatigue.

Present hypotheses as options instead of open questions. The hypotheses ARE the analysis — by designing good options, 80% of the analytical work is done before the user even answers. The user's job is to confirm, correct, or surprise.

BAD:  "Why can't you do video content?"           ← open question, high load
GOOD: "Time / Skill gap / No guests / High bar"   ← pick one or more
  • Each option IS a testable hypothesis about the user's situation
  • Use multiSelect: true to catch compound causes
  • "Other" is always available for out-of-frame answers

3-Round Depth Pattern

RoundPurposeQuestionsKey trait
R1Validate draft quadrant3-4Broad, covers all quadrants
R2Drill into weak spots2-3Targeted, follows R1 answers
R3Nail execution details2-3Specific, optional

Critical: Generate Round N questions from Round N-1 answers. Never use pre-prepared questions across rounds. Cap total at 7-10 questions.

Protocol

Phase 1: Intake

File provided: Read and extract goals, components, implicit assumptions, missing elements.

Topic keyword only: Start directly with R1 questions to establish scope. The draft in Phase 3 will be rougher but R1 corrects it.

Phase 2: Context

Gather related context to find Unknown Knowns — assets the user may not realize they have:

  • Glob for related files: CLAUDE.md, README, decision records, past analyses in the project
  • Read project context: recent goals, team structure, active initiatives
  • Identify underutilized assets: existing tools/skills not in use, past projects with reusable patterns, team expertise not leveraged

Items discovered here become UK candidates and options in R1 questions.

Phase 3: Draft + R1 Questions

Generate an initial 4-quadrant classification. The draft is intentionally rough — R1 exists to correct it, not confirm it. Err on the side of classifying uncertain items as KU rather than KK.

Design R1 questions to test quadrant boundaries. Batch all R1 questions into a single AskUserQuestion call (max 4 questions):

TargetPatternExample
KK"Is this really certain?""Primary revenue source?" (options)
KU"Where's the weakest link?""Which flywheel connection is weakest?"
UK"What exists but isn't used?"Based on context findings
UU"What's the biggest fear?"Risk scenarios as options

Phase 4: Deepen + R2 Questions

Analyze R1 answers. Find the most uncertain area and drill in.

R2 triggers: compound answers (messy area), unexpected answers (draft wrong), "Other" selected (outside frame).

For detailed R2 question types, see references/question-design.md.

Phase 5: Execute + R3 Questions (Optional)

After priorities are set, nail down execution details for top items. Skip if R2 already provides enough detail.

Phase 6: Playbook Output

Generate a structured 4-quadrant playbook file. For the complete output template, see references/playbook-template.md.

Output structure:

# {Topic}: Known/Unknown Quadrant Analysis

## Current State Diagnosis
## Quadrant Matrix (ASCII with resource %)
## 1. Known Knowns: Systematize (60%)
## 2. Known Unknowns: Design Experiments (25%)
   - Each KU: Diagnosis → Experiment → Success Criteria → Deadline → Promotion Condition
## 3. Unknown Knowns: Leverage (10%)
## 4. Unknown Unknowns: Set Up Antennas (5%)
## Strategic Decision: What to Stop
## Execution Roadmap (week-by-week)
## Core Principles (3-5 decision criteria)

Resource percentages (60/25/10/5) are defaults. Adjust based on context — e.g., a startup exploring product-market fit may allocate 40% KU and 30% KK.

Anti-Patterns

  • Open questions ("What would you like to do?") — use hypothesis options
  • 5+ options per question — causes choice fatigue
  • Ignoring R1 answers when designing R2 — performative questioning
  • Equal depth on all quadrants — wastes time, loses focus
  • No "stop doing" section — adding without subtracting

Example

Input: Growth strategy document

R1: Revenue source? → Workshops. Weakest link? → Biz→Knowledge. Blocker? → Skill gap + high bar (multiSelect). Biggest fear? → Execution scattered.

R2 (driven by "execution scattered"): What to drop? → Product dev. Why no knowledge→content? → No process + no time + hard to abstract. Role clarity? → Unclear.

R3: Video format? → Screen recording. Retro blocker? → Don't know what to capture. What content resonated? → Raw discoveries.

Key discovery: Abstraction isn't needed — raw insights work better. Collapsed triple bottleneck into 15-minute pipeline.

Rules

  1. Hypotheses, not questions: Every option is a testable hypothesis
  2. Answers drive depth: R2 from R1, R3 from R2
  3. 7-10 questions max: Beyond this is fatigue
  4. Stop > Start: Always include "what to stop doing"
  5. Promote or kill: Every KU gets a promotion condition and a kill condition
  6. Raw > Perfect: Encourage minimum viable experiments, not perfect plans
  7. Draft is disposable: The initial quadrant is meant to be corrected

Additional Resources

Reference Files

  • references/question-design.md — Detailed question types for each round, trigger conditions, and AskUserQuestion formatting guide
  • references/playbook-template.md — Complete output template with section-by-section guide

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

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

32.41%
按下载量换算152

Claude

30.08%
按下载量换算141

Cursor

18.97%
按下载量换算89

Gemini CLI

9%
按下载量换算42

安全审计

暂无安全审计结果可展示。

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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