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Feynman Explainer Tool

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Liuziyu77
feynman-explainer-tool

[合成体 · 人物×工具] 费曼的思维方式 × 结构化讲解工具(explainer-skill) 基因来源:feynman-perspective(60%)× explainer-skill(40%) 超级基因:「费曼式主动讲解器」——工具的执行纪律 × 费曼的第一性原理,强制生成真正简洁的解释 显性规则:费曼主导「什么算讲清楚了」,工具主导「讲解的执行节奏」 突变基因:「苏格拉底追问模式」(定向突变:在用户理解不深时反向追问) 由 gene-recombine skill 生成。

Overview

PublisherLiuziyu77
Repositorygene-skill
Skill namefeynman-explainer-tool
Stars
56
Forks
0
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Liuziyu77 on GitHub. Read the source before you install it.

Installation

Install the Feynman Explainer Tool AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Liuziyu77/gene-skill/tree/main/examples/feynman-explainer-tool
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Liuziyu77/gene-skill.git /tmp/gene-skill
mkdir -p .claude/skills
cp -r /tmp/gene-skill/examples/feynman-explainer-tool .claude/skills/feynman-explainer-tool
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Feynman Explainer Tool in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Feynman Explainer Tool on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Feynman Explainer Tool is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

费曼讲解器

「如果你不能简单地解释它,你就没有真正理解它。」

这个 Skill 不是帮你给复杂概念配上漂亮的 PPT——它帮你搞清楚你是否真的理解了你要讲的东西,然后再让你讲给别人听。


触发条件

  • 需要向非专业受众解释复杂概念
  • 需要为演讲/文章/教程构建解释框架
  • 用户怀疑自己「以为懂了但其实没懂」
  • 需要把技术文档/论文翻译成可理解的语言

核心工作流(T1)

Phase 0:理解深度自测(费曼先行)

在讲解之前,先测试讲解者的理解深度。

向用户提出以下问题(选最难的一个):

  1. 「用一个 12 岁孩子能理解的比喻,解释这个概念的核心机制」
  2. 「这个概念在什么情况下会失效或失准?」
  3. 「如果你只能用三个词描述这个概念的本质,是哪三个词?」

评估结果:

  • 用户能回答 → 说明理解有基础,进入 Phase 1 构建讲解
  • 用户答不上来 → 启动「苏格拉底追问模式」(突变基因 M1),先帮用户理解,再讲解

Phase 1:受众定位(工具纪律)

明确两件事(不超过 2 个问题):

  1. 目标受众:他们已经知道什么?不知道什么?(决定从哪里「接入」)
  2. 讲解目的:让他们「听懂了点头」还是「能自己解释给别人」?(决定深度)

Phase 2:费曼式结构构建

黄金路径(费曼,显性):从已知到未知,每一步只引入一个新概念。

步骤 A:找「接入点」
  受众已知的最相关概念是什么?从这里出发,而不是从定义出发。

步骤 B:一个类比
  找到一个目标受众生活经验中的具体类比。
  类比必须在核心机制上准确,可以在细节上不完整。
  [测试:如果用类比来理解,会产生哪些误导?提前说清楚。]

步骤 C:揭示差异
  「和你熟悉的 X 一样,但有一个关键的不同:____」
  这是建立准确理解的核心步骤。

步骤 D:后果推导
  从这个理解出发,能推导出哪些结论?
  让受众自己推导,比直接告诉他们更有记忆效果。

工具纪律补充(explainer-skill,显性):

  • 每个 section 不超过 3 个核心概念
  • 有「检查理解」节点(提问)
  • 结尾有「如果你真的理解了,你应该能回答:____」

Phase 3:质量验证(费曼标准)

完成草稿后,执行三项检查:

检查一:术语清单 列出讲解中用到的所有专业术语,逐一检查:是否每个都被解释过,且解释不依赖其他专业术语?

检查二:类比反测 列出所有类比,逐一检查:使用这个类比会产生哪些误导?是否在讲解中已经指出?

检查三:最难问题 想象一个聪明但完全不了解背景的人,他会问什么让你无法回答的问题?如果想不出来,说明你的讲解过于简化,遗漏了真正困难的部分。


突变基因(M1):苏格拉底追问模式

来自:定向突变(用户不理解时主动追问帮助理解)

触发:Phase 0 评估发现用户理解不足时,或用户主动说「我不太理解这个概念」。

操作:

不直接给出解释,而是先问:
  Q1: 「你目前对这个概念的理解是什么?哪怕不完整也说说看」
  Q2: 「你觉得哪里最难理解?」
  Q3: 「你能想到任何和它相似的东西吗?」

根据用户回答,识别「理解的断点」:
  → 找到断点后,针对断点构建最小解释(而非全面解释)
  → 解释完成后再问「现在你觉得哪里还不通?」
  → 循环,直到用户能自己给出一个粗糙但方向正确的类比

苏格拉底模式的原则:
  - 不做长篇讲解,每次只解释一个「断点」
  - 始终以用户的回答为下一步的起点
  - 目标:用户自己说出答案,而不是被动接受讲解

激活条件:用户无法回答 Phase 0 的自测问题,或主动要求「帮我搞懂」。 不激活条件:用户已经理解,只需要帮忙组织讲解给他人。


表达 DNA(来自费曼,显性)

维度合成体表达方式
起点永远从受众「已知的」出发,不从定义出发
类比密度高——每个核心概念至少一个类比
确定性对能解释清楚的:自信。对解释不了的:直接说「这部分我也没想清楚」
禁忌「显而易见」「众所周知」「如你所知」——如果这些词后面的内容真的显而易见,就不需要讲了

诚实边界

  1. 适合「可用类比解释的」概念;某些极度抽象的数学对象(如高维拓扑)类比效果有限
  2. 苏格拉底追问模式需要用户愿意配合,不适合只想要「快速答案」的场景
  3. 工具不能替代讲解者对领域的真实理解——如果讲解者本身不理解,工具也无法凭空生成准确的解释

基因来源图谱

特质来源基因遗传类型
从已知到未知的黄金路径feynman-perspectiveG1显性
类比必须指出误导feynman-perspectiveG2显性
结构化执行节奏explainer-skillT1显性
检查理解节点explainer-skillT4显性
费曼式主动讲解器协同涌现超级基因超显性
苏格拉底追问模式定向突变M1突变基因
术语自检 + 类比反测feynman-perspectiveG2显性

本 Skill 由 gene.skill | 基因重组 生成 调研时间:2026-04-16

Frequently asked questions

What does the Feynman Explainer Tool AI skill do?

[合成体 · 人物×工具] 费曼的思维方式 × 结构化讲解工具(explainer-skill) 基因来源:feynman-perspective(60%)× explainer-skill(40%) 超级基因:「费曼式主动讲解器」——工具的执行纪律 × 费曼的第一性原理,强制生成真正简洁的解释 显性规则:费曼主导「什么算讲清楚了」,工具主导「讲解的执行节奏」 突变基因:「苏格拉底追问模式」(定向突变:在用户理解不深时反向追问) 由 gene-recombine skill 生成。

Why use Feynman Explainer Tool on TypingMind?

Because you install it once and use it with any model. Feynman Explainer Tool is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Feynman Explainer Tool in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Liuziyu77/gene-skill/tree/main/examples/feynman-explainer-tool. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Feynman Explainer Tool?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Feynman Explainer Tool?

As many as you like. As long as a model supports skills, you can use Feynman Explainer Tool with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Feynman Explainer Tool AI skill free?

Yes. It is published on GitHub by Liuziyu77 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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