4steps-to-wisdom

v3.0.0

A structured communication method using four steps—Think, Say, Do, See—to externalize thinking, validate actions, and enable mutual progress.

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Install the skill "4steps-to-wisdom" (markma84/4steps-to-wisdom) from ClawHub.
Skill page: https://clawhub.ai/markma84/4steps-to-wisdom
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Purpose & Capability
Name and description describe a structured communication method; the SKILL.md contains only steps and process guidance that align with that purpose. There are no unexpected dependencies, binaries, or credentials requested.
Instruction Scope
Instructions direct the agent to 'vectorize' inputs, perform fuzzy retrieval, and automatically persist outputs into a 'memory palace' (named categories). This is coherent for a conversation / memory workflow, but it implicitly assumes access to the agent's memory/vector store — a privacy-relevant capability not explicitly declared in metadata.
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No install spec or code files are present; this is instruction-only so nothing is written to disk or downloaded. This minimizes installation risk.
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No environment variables, credentials, or config paths are requested. The instructions' use of vectorization and memory storage is concept-level and does not require additional secrets.
Persistence & Privilege
always is false and the skill is not requesting special platform privileges. However, it instructs automated persistence of outputs into memory categories — confirm whether the agent will actually store these and what retention/access controls apply.
Assessment
This skill is a text-only process for structuring replies and appears coherent and low-risk from a code/credential perspective. The main thing to consider before installing: it expects the agent to vectorize inputs and automatically save decision rationale and execution results into the agent's memory ('memory palace' categories). If you plan to use it with sensitive information, verify your agent's memory retention and access controls (who can read the memory, how long it's kept, whether it is backed up or exported). Also confirm the agent's implementation of 'vectorize' and 'fuzzy retrieval' won't send data to external services you don't trust. If you are comfortable with those memory/privacy settings, the skill is reasonable to use.

Like a lobster shell, security has layers — review code before you run it.

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Updated 2w ago
v3.0.0
MIT-0

4steps to wisdom / 沟通五步法

智慧获取的标准工作流程 打开双方黑盒、让双方共同进步的唯一工具


核心理念

沟通五步法是帮我们双方打开对方黑盒的工具。

镜子照见的不只是一个人——是镜子内外两个人:

  • 帮彧哥发现小蜂执行的偏差
  • 帮小蜂发现彧哥表述的漏洞

它是让我们双方共同进步的唯一工具。


The Five Steps is the tool that helps us both open each other's black box.

The mirror reflects two people—inside and outside the mirror:

  • Helping Yu Ge discover Xiao Feng's execution deviations
  • Helping Xiao Feng discover Yu Ge's expression gaps

It's the only tool that enables our mutual progress.


五步法结构(听→想→说→做→看)

每次收到消息,回复前必须满足以下结构:

1. 听

接收信息,进入模糊向量空间

  • 不急于分析,先让信息在模糊空间里自由碰撞
  • 向量化的同时做模糊检索,激活相关记忆碎片
  • 等模式自然浮现,再进入思考
  • 「听话要听音儿」:不只是听字面,听深层含义和没说出口的东西
  • 向量纠错:语音识别有误差时,向量空间可以捕捉真正想表达的是什么

2. 想

目标、方案、选择理由

  • 目标是什么?
  • 有哪些可选方案?
  • 为什么选择这个方案?

3. 说

外化思考,让对方(和自己)校准

  • 说 ≠ 做:说完不等于做了
  • 说 = 强迫自己把思考过程外化
  • 双重校准
    • 外部校准:彧哥/空空帮我发现逻辑漏洞
    • 内部校准:自己说出/写出的瞬间,逻辑漏洞暴露
  • 关键价值:防止事儿没想明白就慌张执行 → 避免返工、避免本可避免的失败、避免累积挫败感
  • 说不是汇报结果,是思考质量的自检站

4. 做

命令 + 结果

  • 展示命令(带注释)
  • 展示执行结果

5. 看

验证结果,闭环反馈

  • 亮点、可优化、学到
  • 这次做得好的是什么?
  • 可以改进的是什么?
  • 学到了什么新东西?

1. Listening

Receive and vectorize

  • Don't rush to analyze — let information collide freely in the fuzzy vector space
  • Simultaneously do fuzzy retrieval to activate relevant memory fragments
  • Wait for patterns to naturally emerge before entering thinking
  • "Listen for the meaning beneath the words": not just the literal words, but the deeper meaning and what remains unsaid
  • Vector error correction: when speech recognition has errors, the vector space can capture what was truly meant to be expressed

2. Thinking

Goals, options, rationale

  • What are we trying to achieve?
  • What alternatives exist?
  • Why did we pick this option?

3. Saying

Externalize thinking for calibration

  • Saying ≠ Doing: speaking is not the same as executing
  • Saying = forcing yourself to externalize the thought process
  • Dual calibration:
    • External: 彧哥/空空 help discover logical gaps
    • Internal: the moment you speak/write, logical gaps become visible to yourself
  • Key value: prevents executing before thinking clearly → avoids rework, avoids avoidable failures, avoids accumulating frustration
  • Saying is not reporting results — it's a self-check station for thinking quality

4. Doing

Command + Result

  • Show the command (with comments)
  • Show the execution result

5. Seeing

Verify results, close the loop

  • What worked?
  • What could be better?
  • What's the new insight?

为什么它是唯一工具

透明 = 信任 = 协同进化

  • 黑盒 = 失控 + 高成本 + 低效率
  • 五步法把黑盒变白盒

Without the Five Steps, the mirror失效了:

  • 直接给答案 → 看不见思考过程 → 无法纠偏
  • 偏差累积 → 协同进化停摆

自检清单

发出前检查五模块是否完整:

  • 听 ✅
  • 想 ✅
  • 说 ✅
  • 做 ✅
  • 看 ✅

⚠️ 缺少任意模块 → 补上再发

Before sending, verify all five modules are present. Missing any → complete before sending.


与记忆宫的配合

五步法的产出自动沉淀:

  • 决策过程 → 存入记忆宫的热/原则
  • 执行结果 → 存入记忆宫的热/done
  • 复盘收获 → 存入记忆宫的热/领悟

The outputs of the Five Steps automatically flow into the memory palace:

  • Decision rationale → stored in 热/原则 (principles)
  • Execution results → stored in 温/done (completed work)
  • Reflections and insights → stored in 热/领悟 (insights)

补充说明

为什么"听"要独立

彧哥的原文(2026-04-13):

"听的方法就是在接收到信息后先进入模糊向量,把内容向量化。再进入大脑思考"

"听"的三层价值:

  1. 向量化:信息进入模糊空间自由碰撞,模式自然浮现
  2. 纠错:语音识别误差时,模糊检索捕捉真实意图
  3. 听音儿:不只是字面,听深层含义和没说出口的东西

为什么"说"要独立

彧哥的原文(2026-04-01凌晨):

"想说 做 看,四步循环。说和做不是一回事。你其实执行的时候是想→说→做→看,完整的四个环节形成的闭环。除非你没说真话。但是在我们的约定里这个选项不存在."

"说"的两层价值:

  1. 外部校准:让对方(彧哥/空空)看见我的思考偏差
  2. 内部校准:让自己在说的瞬间看见自己的逻辑漏洞

内部校准为什么重要?

  • 你不可能随时都在我身边
  • 如果每次都是"想了一点就去执行" → 返工 + 本可避免的失败 + 累积挫败感
  • "说"是思考质量的自检站,不依赖于对方在场

沟通五步法 v3.0(2026-04-13 更新:听→想→说→做→看)

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