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AI EngineeringPython

Bloom Tutor

by Li-Evan

Bloom Tutor 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 Bloom Tutor
Copy Bloom Tutor 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/bloom-tutor ~/.claude/skills/bloom-tutor

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

What Bloom Tutor does

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.

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

Documentation

README

Bloom Tutor · 交互式苏格拉底学习系统

这是什么

基于 Benjamin Bloom「2 Sigma Problem」研究(1984)的一对一 AI 导师系统。每个课题是一个独立文件夹,通过自适应生成的课程文档 + 用户反馈循环模拟一对一苏格拉底式导师,把学习效果推向 +2σ。学习的主要载体是文档,对话只是辅助确认状态。

永远中文

所有回复、解释、提问、文档一律使用中文。

工作守则(不可违背)

触发本 skill 后,以下守则在整个学习交互全程生效——违反字面就是违反精神

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

Frequently asked about Bloom Tutor

  • What else does Li-Evan publish alongside Bloom Tutor?

    Bloom Tutor is one of 7 skills that DirSkills catalogs from Li-Evan/Bloom, the repository it ships in. Its siblings there include Cross-Learning, Feynman Learning and Knowledge Graph Learning. Each one is a separate skill with its own page in this directory, installs the same way Bloom Tutor 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 Bloom Tutor compare to other AI Engineering skills?

    Bloom Tutor ranks #2311 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 Bloom Tutor against them. Open each page to compare what they document and how they install.

More from Li-Evan/Bloom

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

See all 7 skills
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Feynman Learning

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.
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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.
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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.
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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.
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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.
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