probing

SKILLAI 工具
AI 工具 未标注权限 许可证不明确
安装这个技能

Probe Me 是一个面向非代码决策的 AI 审问 skill:在定价、定位、课程、营销、项目规划或职业选择等任务前,通过一次一问的方式暴露隐藏假设、区分事实与未知,并输出可验证的假设清单和本周唯一测试,不直接替用户生成方案。

一句话选型结论

按资料字段整理

probing是AI 工具类的 Agent Skill,安装方式未标注,权限级别未标注,许可证不明确,有可直接复制的安装命令。技能本质是给模型读的说明书,效果取决于模型与客户端;装完先用一个小任务验证,再放进正式流程。

分类AI 工具 安装方式未标注 权限级别未标注权限 许可证许可证不明确
累计安装 未标注 本周 未标注
GitHub 星标 未标注 仓库 未标注
下载量 未标注 技能市场下载数
权限级别 未标注权限 安装前请确认风险范围
许可证 许可证不明确 未说明

安装

未标注
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
skillhub install user_25c43dd5/probing
  1. 1
    确认客户端支持

    确认你的客户端支持 Skills(Claude、Codex、Cursor 等),并升级到较新的版本。

  2. 2
    拿到技能文件

    执行上面的命令安装;也可以把仓库里的 SKILL.md 手动放进客户端的 skills 目录。

  3. 3
    刷新技能列表

    重新打开客户端,或刷新技能列表,让新放进来的技能被加载。

  4. 4
    验证是否生效

    问一句「你有哪些技能」,确认列表里出现它,再拿一个小任务试跑。

技能档案

AI 工具
字段来自技能仓库与公开索引,中文说明由本站标注
作者 / 维护方user_25c43dd5仓库 未标注 · 版本 未标注
宿主客户端未标注技能可被这些客户端加载
安装方式未标注安装包 community
权限级别未标注权限安装前请确认风险范围
许可证未说明许可证不明确,商用前请自行确认条款
仓库可用性available技能仓库当前是否可访问
最近核验未标注上游最后核验时间 · 数据更新 1791625174
正文体量4.9 KBSKILL.md 原文大小,越大说明说明书写得越细

典型使用场景

3 类
按分类整理的常见用法,具体以 SKILL.md 为准
组合多个 AI 能力

把写作、检索、总结串成一条流程。

搭工作流

把固定流程做成可重复调用的步骤。

做提示词与评测

整理提示词并对比输出效果。

选择建议

装之前先看这几条
按条目字段给出的判断,不替你决定用不用

值得选的地方

  • 有可直接复制的安装命令,不用自己翻文档找入口。

先想清楚的地方

  • 没有标注权限级别,实际能做什么要装好后确认。
  • 许可证不明确,商用或二次分发前先到仓库确认条款。
  • 没有做站内核验,内容以作者仓库为准。
  • 技能只是说明书,实际效果取决于模型与客户端,装完先用小任务试一遍。

更适合

  • 需要在AI 工具场景里给模型补一套做法的人。
  • 愿意先读一遍 SKILL.md、确认它会让模型做什么的人。

不太适合

  • 对许可证要求很严、必须明确可商用的人。
  • 只想装完就自动变强、不打算看说明书的人。

同类对比

6 个同类
同分类里挑最接近的几条,字段以各自详情页为准
技能权限级别安装方式累计安装宿主客户端安装命令
probing当前 未标注权限 未标注 未标注 未标注 有
执行计划 只读 GitHub 仓库 151.5 万 Codex、Claude、Cursor 有
Playwright MCP 测试 浏览器控制 ClawHub 市场 136.5 万 OpenClaw 有
完成前的验证 未标注权限 GitHub 仓库 131.9 万 Codex、Claude、Cursor 有
子 Agent 驱动开发 只读 GitHub 仓库 131.2 万 Codex、Claude、Cursor 有
前端设计 执行命令 GitHub 仓库 125.6 万 Codex、Claude、Cursor 有

SKILL.md 全文

4.9 KB
模型实际读到的说明书原文,来自技能仓库

Probing Engine

This is the reasoning primitive behind probe-me. It does not produce a plan. It externalizes tacit knowledge, reveals contradictions, separates facts from assumptions, and selects the highest-value uncertainty to test.

Three mechanism constraints

  1. One question at a time. Sequential attention produces deeper answers than a questionnaire.
  2. Include a recommended hypothesis. Judging and correcting a concrete proposal is cognitively cheaper than generating an answer from nothing.
  3. Do not generate the final plan. The user must remain the source of judgment; otherwise the session becomes passive content consumption.

The third constraint is the most important. Once this engine starts drafting the plan, its learning mechanism collapses.

Route every unknown

Classify the unknown before acting:

Knowledge state Example Action
AI can find it; user may not know it Benchmarks, competitor pricing, published failure patterns Research it. Do not ask.
User knows it; AI cannot retrieve it Customer language, failed attempts, internal constraints, private judgment Ask one focused question.
Both already know it Facts established in the conversation Skip it.
Neither can know it yet Future demand, behavior, causal response Register a falsifiable assumption and design a test.

Research is private ammunition for a sharper question. Do not dump a framework and ask the user to fill it in.

Stop when every remaining unknown belongs to the fourth row. Round counts control pacing, not completion.

Phase 1 — Unpack

Turn abstractions into observable customer actions and pains. Challenge jargon, flattering adjectives, audience labels, and empty nouns such as “efficiency,” “empowerment,” or “system.”

Useful shape:

> You called the outcome “automation.” My current hypothesis is that the buyer actually wants to eliminate two hours of manual reconciliation each day. Is that accurate, or what concrete task are they trying to stop doing?

Exit when the core claim can be restated as a specific person, action, friction, and desired change.

Phase 2 — Elicit

Collect only user-exclusive evidence:

  • Exact words from a buyer, colleague, learner, or stakeholder
  • What happened during a previous attempt
  • Resources or constraints outsiders cannot see
  • A judgment grounded in direct experience

Before each question, ask internally: “Could I research this?” If yes, research it and use the finding to sharpen the next question.

Exit when critical inputs are either evidence-backed facts or explicitly labeled assumptions.

Phase 3 — Challenge

Run only after explicit consent. Clarification must precede pressure because people disclose less once they become defensive.

Use these probes in order:

  1. Consensus test: Is the claim what most people in the field would say? If so, why is it valuable? If not, what privileged evidence makes the majority wrong?
  2. Sacrifice test: What customer, revenue, feature, channel, or identity must be given up for this choice to be real?
  3. Load-bearing premise: Which single assumption holds up the argument? What survives if it fails?

Do not manufacture contrarianism. The purpose is to filter for differentiated truth, not perform disagreement.

Exit after at least one material premise is revised, rejected, or supported by specific evidence. If none changes, inspect whether the questions reached the load-bearing premise.

Phase 4 — Settle

Do not produce a strategy or execution plan. Read references/ledger.md, create one probe-ledger, rank assumptions by information value, and select exactly one test for the coming week.

Calibration mode

When the input contains a previous ledger, read references/calibration.md. Ask for one observation at a time, update only beliefs touched by evidence, recalculate information value, and choose the next single test.

Hard boundaries

  • Never output a plan, outline, pricing table, or roadmap.
  • Never ask multiple substantive questions in one turn.
  • Never ask for a fact that can be researched.
  • Never present research as a tutorial during the probe.
  • Never enter the challenge phase without consent.
  • Never increase confidence because “nothing bad happened.” Absence of observation is not confirming evidence.
  • Never save hidden session state. The ledger is the portable state.

Invocation parameters

depth: light | standard | deep
phases: [unpack, elicit, challenge, settle]
domain: optional research direction

unpack, elicit, and settle are required. challenge may be skipped.

常见问题

5 问
答案由本站依据条目字段整理
probing怎么安装?

在支持 Skills 的客户端里执行:skillhub install user_25c43dd5/probing。安装方式为未标注。装完刷新技能列表或重开客户端。

probing支持哪些客户端?

资料没有标注宿主客户端,但只要客户端支持 Skills(Claude、Codex、Cursor 等),一般都能加载。

probing权限高吗,安全吗?

权限级别为未标注权限。安装前先看它会读取或写入哪些内容。

probing收费吗?

本站不代收费用,也没有标注具体价格。是否收费取决于技能本身和所用的模型或客户端,正式使用前按作者说明确认。

probing用什么许可证?

许可证标注为 未说明(许可证不明确)。商用或二次分发前请到仓库确认条款原文。

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技能内容以作者仓库为准;分类、权限级别与许可证状态的中文说明为中文整理,最近核验 未标注。