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Agent Skill · 研究检索

clarify%3aunknown(澄清%3a 未知)

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。

clarify%3aunknown 是研究检索类 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,暂无第三方扫描结果,按安装量在研究检索类的 73,675 个 Skill 里排第 39,204。

总安装 1,448周安装 58GitHub Stars 7下载 469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: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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

来源仓库内路径:skills/clarify%3Aunknown

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/team-attention/workshop-upstage --skill clarif…;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

clarify%3aunknown 怎么安装?

在终端执行 npx skills add https://github.com/team-attention/workshop-upstage --skill clarify:unknown。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

clarify%3aunknown 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。

clarify%3aunknown 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。

clarify%3aunknown 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。

clarify%3aunknown 用的人多吗?

公开数据是累计安装 1,448 次,近一周安装 58 次,下载 469 次,GitHub 7 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。

clarify%3aunknown 安全吗?

没有查到第三方扫描结果,安全性无法从公开资料判断,安装前请自行审查源码、依赖和命令行为。

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。
  • 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。
  • 没有第三方扫描结果,安全性只能靠自己读源码判断。
  • 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。
  • 需要处理研究检索类任务、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。
  • 要求有安全扫描结论才肯安装的团队。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
clarify%3aunknown

澄清%3a 未知 · 本页

1,4487只读未明确未收录

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

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

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。目前只有一个公开来源,建议同时核对源仓库。

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