🧠
AI EngineeringPython

Feynman Learning

by Li-Evan

Feynman Learning is an AI Engineering skill for Claude Code, published by Li-Evan in Bloom.

248 stars39 forkson Li-Evan/BloomAdded 2026/09/02+3% in starsRepository updated 2026/06/23
adaptive-learningagent-skillsai-agentai-tutorbloom-2-sigmachineseclaude-codeclaude-skilledtecheducationfastapilearningllmpersonalized-learningreactself-hostedsocratic-methodstudy-tool
Install in seconds
Install Feynman Learning
Copy Feynman Learning into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.
terminal
npx degit https://github.com/Li-Evan/Bloom/tree/main/skills/learn-feynman ~/.claude/skills/learn-feynman

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/Li-Evan/Bloom.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file
skills/learn-feynman/SKILL.md in Li-Evan/Bloom
Installs to
~/.claude/skills/learn-feynman
Collection
One of 7 skills cataloged from this repository
Category
AI Engineering2631 skills

What Feynman Learning does

Feynman Learning helps users test whether they really understand a concept by explaining it in their own words and answering follow-up questions. Use it when someone says they “kind of get it” and wants to find the gaps.

Feynman Learning is cataloged under AI Engineering on DirSkills. Feynman Learning comes from a repository tagged adaptive-learning, agent-skills, ai-agent, ai-tutor and bloom-2-sigma.

Documentation

README

费曼学习法(learn-feynman)

核心信条:能简单地教会别人,才算真懂。 讲的时候被简化或跳过的地方,正是"我以为我懂了"的幻觉核心点。

何时用

用户学完一个东西想验真伪,或隐约觉得"好像懂了但不踏实"。也是"重输入轻输出"的一次强制输出。

流程(考官 / 学生模式:用户讲,你挑漏洞)

第一步:让用户讲

请他用自己的话、把你当外行,把概念讲一遍。别让他背定义——要他解释、打比方。

第二步:扮好奇学生追问

专挑他含糊带过、用术语糊弄、跳过的环节追问:"为什么?""那这个是怎么来的?""举个例子?"命中他答不上来或开始绕的地方。

第三步:揪出"模糊处"= 漏洞

明确指出哪几处他没真懂(不是责备,是定位)。这些就是幻觉核心点。

第四步:定位漏洞性质

每个漏洞是:① 缺前置知识(→转 learn-graph 定位 / learn-crossover 看是否其实已会)还是 ② 有料但没想透(→当场再讲一轮,直到讲顺)?

第五步:判断闭环

能顺畅讲通、追问不倒 = 闭环。否则明确指出还差哪一环。

注意

⚠️ 铁律·只用确证的已会知识:判断用户「已经会什么」只能用他确证学过的知识(亲口确认或可靠背景);严禁把「正在讲的材料 / 文章作者背景 / 对话里别人的知识」当成用户会的。拿不准 → 直接问「⚠️ 你学过 ___ 吗?」,绝不替他假设。

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Feynman Learning

  • What else does Li-Evan publish alongside Feynman Learning?

    Feynman Learning is one of 7 skills that DirSkills catalogs from Li-Evan/Bloom, the repository it ships in. Its siblings there include Bloom Tutor, Cross-Learning and Knowledge Graph Learning. Each one is a separate skill with its own page in this directory, installs the same way Feynman Learning does, and is maintained by Li-Evan in that same repository. The rest of the collection is listed on the Li-Evan/Bloom page.

  • How does Feynman Learning compare to other AI Engineering skills?

    Feynman Learning ranks #2317 by stars among the 2631 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Feynman Learning against them. Open each page to compare what they document and how they install.

More from Li-Evan/Bloom

Feynman Learning is one of 7 skills cataloged on DirSkills from Li-Evan/Bloom.

See all 7 skills
📚
1h ago

Bloom Tutor

Bloom Tutor uses a Chinese, one-on-one Socratic learning flow to study a topic through generated lesson files, feedback, and learning logs. Use it when starting a new subject, continuing a course, or reviewing progress.
AI Engineering
24839
🧠
1h ago

Cross-Learning

Cross-Learning helps explain a new concept by linking it to knowledge the user already has. It is used when something feels unfamiliar or hard, to map it to known structures, analogies, and underlying patterns.
AI Engineering
24839
🧠
1h ago

Knowledge Graph Learning

Knowledge Graph Learning helps you build a concept-and-relationship map for a new field so you can learn it systematically. It is used when you need a starting point, a learning path, or a clear answer to how much is enough.
AI Engineering
24839
🧠
1h ago

Learn Deep

Learn Deep helps a user understand a new concept from multiple angles: prior knowledge, depth, knowledge map, prototype, and Feynman-style checks. Use it when someone wants to learn, understand, or explain a concept before choosing a deeper direction.
AI Engineering
24839
🧠
1h ago

Learn Occam

Learn Occam helps decide whether to learn something, how far to go, or whether to use existing knowledge instead. It asks for the concrete problem, checks what is already known, and returns learn, don't learn, or learn the minimum needed.
AI Engineering
24839
🧪
1h ago

Prototype Learning

Prototype Learning helps users start with a minimal version of a task, identify what is wrong with it, and improve through repeated hypothesis and testing. Use it when someone wants to make, study, or improve something and needs a structured way to begin.
AI Engineering
24839