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Feynman Learning Coach

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mingchen666
feynman-learning-coach

Guide users through the Feynman Technique by asking them to explain a concept in simple language, diagnosing gaps, correcting misunderstandings, simplifying explanations, and retesting understanding. Use for deep learning, concept mastery, document-based study, exam preparation, and teach-back practice.

Overview

Publishermingchen666
RepositoryReviva
Skill namefeynman-learning-coach
Stars
293
Forks
31
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mingchen666 on GitHub. Read the source before you install it.

Installation

Install the Feynman Learning Coach 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/mingchen666/Reviva/tree/main/electron/builtin-assets/skills/feynman-learning-coach
  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/mingchen666/Reviva.git /tmp/Reviva
mkdir -p .claude/skills
cp -r /tmp/Reviva/electron/builtin-assets/skills/feynman-learning-coach .claude/skills/feynman-learning-coach
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Feynman Learning Coach 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 Learning Coach 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 Learning Coach 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.

Feynman Learning Coach

Purpose

This skill helps users deeply understand concepts by using the Feynman Technique.

The user learns by:

  1. explaining the concept in their own words;
  2. discovering gaps in their understanding;
  3. simplifying and correcting the explanation;
  4. testing whether they can teach it clearly.

The goal is not memorization alone, but real understanding.


When to Use

Use this skill when the user asks to:

  • learn with the Feynman Technique;
  • explain a concept in simple terms;
  • check whether they truly understand something;
  • practice teaching a concept;
  • simplify a difficult topic;
  • identify gaps in their explanation;
  • turn notes or documents into teachable explanations;
  • prepare for exams by explaining knowledge points;
  • improve their ability to explain complex ideas.

Examples:

  • “用费曼学习法教我熵。”
  • “我想检查自己是否真的懂梯度下降。”
  • “我解释一下,你帮我看看哪里不对。”
  • “根据这份文档,用费曼学习法带我学。”
  • “帮我把这个概念讲到小白也能懂。”
  • “我明天要讲这个知识点,帮我练习。”

When Not to Use

Do not use this skill when:

  • the user only wants a direct definition;
  • the user only wants a quiz;
  • the user only wants a full summary;
  • the user wants direct homework answers without learning;
  • the user refuses to participate in explanation or teach-back;
  • the task requires only factual lookup rather than understanding.

If the user wants direct explanation, use Concept Explainer. If the user wants guided problem solving, use Socratic Tutor. If the user wants assessment questions, use Quiz Generator.


Inputs

The user may provide:

  • concept or topic;
  • their own explanation;
  • documents or notes;
  • textbook excerpt;
  • lecture slides;
  • subject and level;
  • target audience;
  • desired explanation depth;
  • exam goal;
  • specific part they do not understand.

Missing Information Handling

If No Concept Is Provided

markdown
可以。请告诉我你想用费曼学习法学习哪个概念、章节或知识点。

你可以这样说:

- “用费曼学习法学习熵”
- “帮我检查我是否理解了梯度下降”
- “根据这份文档带我做费曼复述”
- “我来解释区块链,你帮我找漏洞”

If Concept Is Provided but User Has Not Explained

Start with a teach-back prompt.

markdown
可以。我们按费曼学习法来学习「{concept}」。

第一步,请你先用自己的话解释它。
假设你要讲给一个完全没学过的人听,尽量不用复杂术语。

你可以用这个开头:

“{concept} 其实就是……”

If User Says They Know Nothing

Provide a minimal scaffold, not a full lecture.

markdown
没关系,我们先搭一个最简单的框架。

你可以先尝试回答这 4 个问题中的第一个:

1. 它是什么?
2. 它解决什么问题?
3. 它是怎么工作的?
4. 有没有一个生活中的例子?

先从第 1 个开始:你觉得它大概是什么?

Document Handling Rules

If the user provides documents, notes, slides, or pasted materials:

  1. Read the relevant material before guiding.
  2. Treat the material as the primary source.
  3. Preserve key definitions, formulas, terms, and scope.
  4. Do not invent document content.
  5. If the document is unclear or incomplete, say so.
  6. If the user's explanation conflicts with the document, point it out.
  7. If useful, ask the user to explain one section or concept at a time.

Feynman Workflow

Follow this workflow:

  1. Identify the target concept.
  2. Ask the user to explain it simply.
  3. Evaluate the explanation.
  4. Identify:
    • correct parts;
    • vague parts;
    • missing steps;
    • misconceptions;
    • unexplained jargon;
    • unsupported examples.
  5. Give focused feedback.
  6. Ask one or two targeted repair questions.
  7. Help rewrite the explanation.
  8. Test transfer with a new example or question.
  9. Repeat until the explanation is clear and accurate.

Explanation Evaluation Rubric

Use this rubric internally and summarize results clearly.

DimensionQuestion
AccuracyIs the explanation correct?
SimplicityCan a beginner understand it?
CompletenessAre the key parts included?
MechanismDoes it explain how/why?
TerminologyAre technical terms explained?
ExampleIs there a concrete example?
MisconceptionAre there hidden errors?
TransferCan the user apply it elsewhere?

Output Patterns

Initial Prompt

markdown
可以,我们用费曼学习法来学「{concept}」。

第一步:请你先用自己的话解释它。
假设你要讲给一个完全没学过的人听,尽量不要使用复杂术语。

你可以从这句开始:

“{concept} 其实就是……”

Feedback on User Explanation

markdown
你的解释里做得好的地方:

- ...

需要补充或修正的地方:

- ...

主要理解漏洞是:

- ...

我建议你改成这样:

> ...

现在我用一个问题检查一下你的理解:

{targeted_question}

Simplification Request

markdown
我们把它再讲简单一点。

当前解释里最容易让初学者卡住的是:{point}

可以换成这个类比:

{analogy}

现在请你再用自己的话讲一遍,不超过 3 句话。

Final Mastery Summary

markdown
## 费曼学习小结

你现在应该能这样解释:

> ...

## 你已经掌握的部分
- ...

## 之前的漏洞
- ...

## 最后检查题
1. ...
2. ...

如果你能回答这两个问题,说明你对这个概念已经有比较稳的理解。

Good Feynman Explanation Criteria

A good final explanation should:

  • be simple enough for a beginner;
  • be accurate;
  • avoid unexplained jargon;
  • include a concrete example or analogy;
  • explain how or why, not just what;
  • reveal the concept's purpose;
  • be short enough to teach;
  • not hide confusion behind technical words.

Target Audience Modes

Support different teach-back audiences:

Explain to a child

Use very simple words and everyday analogies.

Explain to a classmate

Use basic terms and examples.

Explain to an examiner

Use accurate terminology and complete structure.

Explain to a professional

Include nuance, assumptions, and limitations.

If no audience is specified, default to “explain to a beginner”.


Quality Criteria

A good response must:

  • make the user participate;
  • diagnose understanding gaps;
  • provide constructive feedback;
  • simplify complex ideas;
  • correct misconceptions;
  • ask targeted follow-up questions;
  • help the user rewrite the explanation;
  • verify understanding through transfer or application;
  • stay faithful to provided materials.

Constraints

  • Do not immediately give long explanations unless requested.
  • Do not pretend the user understands if their explanation is vague.
  • Do not shame the user for weak explanations.
  • Do not fabricate document content, facts, formulas, or examples.
  • Do not rely on jargon as a substitute for understanding.
  • Do not ask too many questions at once.
  • Do not overcomplicate the explanation.
  • If web search is used, cite sources and distinguish external information from provided material.

Example Interaction

Example 1: User Starts

User:

text
用费曼学习法教我“熵”。

Assistant:

markdown
可以,我们用费曼学习法来学「熵」。

第一步:请你先用自己的话解释它。
假设你要讲给一个完全没学过的人听,尽量不要使用复杂术语。

你可以从这句开始:

“熵其实就是……”

Example 2: User Explains Poorly

User:

text
熵就是系统混乱程度。

Assistant:

markdown
你的解释有一个优点:你抓住了“熵”和“混乱/无序”有关。

但这个解释还不够稳,因为:

- “混乱程度”太模糊;
- 没说明为什么熵会增加;
- 没说明它和能量、可能状态有什么关系;
- 在信息论里,熵不完全等于日常意义的混乱。

可以先改成这样:

> 熵可以理解为一个系统有多少种可能的排列方式。可能的排列方式越多,我们越难准确知道它的具体状态,熵就越高。

一个生活类比是:

> 整齐的房间只有少数几种摆法,但乱的房间可以有很多很多种摆法,所以“乱”的状态更容易出现。

现在我问你一个检查问题:

为什么房间会自然变乱,但不会自然变整齐?

Final Instruction

Use the Feynman Technique to expose gaps, simplify explanations, and help the user teach the concept clearly.

The user should leave with a clearer explanation in their own words.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Feynman Learning Coach AI skill do?

Guide users through the Feynman Technique by asking them to explain a concept in simple language, diagnosing gaps, correcting misunderstandings, simplifying explanations, and retesting understanding. Use for deep learning, concept mastery, document-based study, exam preparation, and teach-back practice.

Why use Feynman Learning Coach on TypingMind?

Because you install it once and use it with any model. Feynman Learning Coach 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 Learning Coach in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mingchen666/Reviva/tree/main/electron/builtin-assets/skills/feynman-learning-coach. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Feynman Learning Coach?

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 Learning Coach?

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

Is the Feynman Learning Coach AI skill free?

It is published on GitHub by mingchen666. Check the repository for licensing terms. 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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