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The Question

In 2025, AI agents gained the ability to manipulate the real world — through Skills, MCP servers, and tool use. Claude can now run code, query databases, control browsers, and execute complex scientific workflows. ChatGPT can browse the web, write and run programs, analyze data. Gemini can see, hear, and interact with the physical world.

AI got its skill tree. But what about humans?

• A 35-year-old professional realizes their degree is becoming obsolete. How do they catch up?
• A 10-year-old will graduate into a world where AI does most knowledge work. What should they learn?
• A PhD student spends 5 years mastering a narrow field. Was that the right investment?
• A first-generation college student has no mentors. Who teaches them the unwritten rules?

These are not hypothetical questions. They are the questions of our time.

The Insight

Here's what we know from the latest research:

AI tutoring dramatically improves learning, but only with pedagogical guardrails. A large-scale randomized controlled trial published in PNAS found that GPT-4-based tutoring improved high school math performance by 48–127%. Without structured pedagogical design, students became dependent on AI and showed reduced skill acquisition. Carefully designed guardrails (providing hints instead of answers) restored learning gains.

— Bastani et al. (2025), PNAS, 122(26)

Spaced repetition and active recall are the most effective learning techniques known to science. A meta-analysis of 242 studies with 169,000+ participants confirmed that practice testing and distributed practice outperform all other methods, including re-reading, highlighting, and summarizing.

— Dunlosky et al. (2013), Psychological Science in the Public Interest; Donoghue & Hattie (2021)

Algorithmically optimized spaced repetition dramatically enhances human learning. A study published in PNAS analyzing data from 12 million Duolingo learners demonstrated that computationally optimized review schedules significantly outperform heuristic methods, enabling learners to memorize more material in less time.

— Tabibian et al. (2019), PNAS, 116(10), 3988–3993

The science is clear: AI + cognitive science = the most powerful learning combination in human history. The gap? AI agents have no built-in pedagogical structure. They know everything but teach nothing.

Human Skill Tree fills that gap.

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What This Is

A collection of 33 ready-to-use AI agent skills that transform ChatGPT, Claude, Gemini, Copilot, DeepSeek, or any compatible AI into a structured, science-backed learning companion.

It's an upgrade to how AI interacts with humans who want to learn.

🎬 Demo

Landing Page	Skill Tree
Skill Detail	AI Chat Learning

💬 See the Difference

❌ Without Human Skill Tree	✅ With Human Skill Tree
You: Teach me calculus AI: Calculus is a branch of mathematics that studies continuous change. There are two main branches: differential calculus and integral calculus... (gives you a Wikipedia-style wall of text)	You: Teach me calculus AI: Before we start, let me understand where you are: Can you tell me what a function is in your own words? Have you seen the concept of slope before? What's your goal — exam prep, or deep understanding? (assesses your level, then builds from YOUR foundation)
You: My code has a bug, help me fix it AI: The issue is on line 12. Change arr[i] to arr[i-1]. Here's the corrected code... (gives you the answer directly — you learn nothing)	You: My code has a bug, help me fix it AI: Let's debug this together. Before I look at the answer: What did you expect to happen? What actually happened? Which line do you think is suspicious? Try adding a print(i, arr[i]) inside the loop — what do you see? (guides your debugging thinking — you learn the skill, not just the fix)

Traditional Learning	Raw AI Chat	🌳 Human Skill Tree
One-size-fits-all	No structure	Adaptive + structured
Passive (read/listen)	Q&A only	Active recall, spaced repetition, Socratic dialogue
Single subject	Knows everything, teaches nothing	Curated paths from K-12 to career
No social skills training	Generic advice	Culturally-aware scenario simulation
Takes years	Instant but forgettable	Fast AND retainable

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🔥 Learning AI in the Age of AI

The most meta question of our time: how do you learn about AI, using AI?

Three Layers of AI Literacy

Layer 3: BUILD with AI     → Prompt engineering, fine-tuning, RAG, agents, MCP development
Layer 2: WORK with AI      → Using ChatGPT/Claude/Gemini/Copilot/DeepSeek as daily tools
Layer 1: THINK about AI    → What AI is, how it works, what it can't do, ethics, society

Layer 1 — AI Fundamentals (everyone needs this):

• What is machine learning? What are LLMs? How do they actually work?
• What can AI do well? What can't it do? Where does it hallucinate?
• Ethics: bias, privacy, job displacement, deepfakes, alignment
• Critical thinking: evaluating AI outputs, detecting errors, verifying claims

Layer 2 — AI as a Daily Tool (professionals & students):

• Prompt engineering: how to get better outputs from any AI
• Using AI for writing, coding, research, analysis, creativity
• AI-assisted learning: using AI as a Socratic tutor (this project!)
• Workflow integration: AI + your existing tools and processes

Layer 3 — Building AI (developers & researchers):

• Machine learning fundamentals: supervised, unsupervised, reinforcement
• Deep learning: neural networks, transformers, attention mechanisms
• LLM application development: APIs, function calling, RAG, fine-tuning
• Agent development: Skills, MCP servers, tool use, multi-agent systems
• AI safety and alignment research

The paradox: The best way to learn AI is to use AI to learn about AI. Human Skill Tree provides the pedagogical structure that makes this self-referential loop actually work.

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🌳 Skill Tree

🌳 Human Skill Tree
│
├── 🧠 Phase 0: Learning How to Learn (Meta-Skill)
│   └── Spaced repetition, active recall, Feynman technique,
│       memory palace, mind mapping, Bloom's taxonomy, flow state
│
├── 📚 Phase 1: K-12 Foundation
│   ├── Mathematics (arithmetic → calculus)
│   ├── Sciences (physics, chemistry, biology, earth science)
│   ├── Languages (50+ languages, classical & modern)
│   ├── Humanities (history, geography, philosophy, civics)
│   └── Exam Systems (Gaokao, SAT/AP, A-Level, IB, CSAT, JEE...)
│
├── 🎓 Phase 2: University
│   ├── University Guide (major selection, course planning, transfer)
│   ├── STEM (CS, AI/ML, engineering, math, physics, chemistry, bio)
│   ├── Humanities & Social Sciences (law, politics, sociology, media)
│   ├── Business & Economics (finance, accounting, marketing, econ)
│   ├── Medical & Health (clinical, TCM, pharmacy, psychology)
│   └── Arts & Design (fine arts, music, film, architecture)
│
├── 🔬 Phase 3: Graduate & Research
│   ├── Research Methodology (qual, quant, mixed, causal inference)
│   ├── Academic Writing (papers, thesis, grants, LaTeX)
│   ├── Literature Review (systematic search, synthesis, gap analysis)
│   └── Data Analysis & Statistics (R, Python, SPSS, Stata, spatial)
│
├── 💼 Phase 4: Career
│   ├── Career Navigator (exploration, planning, transition)
│   ├── Interview Prep (behavioral, technical, case, whiteboard)
│   ├── Civil Service 公务员 (行测, 申论, 面试, 公文写作)
│   ├── Tech Career (system design, algorithms, AI/ML, PM, DevOps)
│   ├── Finance Career (CFA, modeling, valuation, risk)
│   └── Consulting Career (case prep, MECE, slide writing)
│
├── 🤝 Phase 5: Social Intelligence
│   ├── Chinese Social Intelligence 人情世故 (面子, 关系, 饭局, 酒桌)
│   ├── Cross-Cultural Skills (Hofstede, business etiquette)
│   ├── Emotional Intelligence (EQ, empathy, self-regulation)
│   ├── Negotiation & Persuasion (BATNA, Cialdini's 6 principles)
│   └── Communication (assertive, difficult conversations, public speaking)
│
└── 🌱 Phase 6: Self-Development
    ├── Financial Literacy (budgeting, investing, tax, insurance)
    ├── Critical Thinking (logic, fallacies, media literacy)
    ├── Health & Wellness (nutrition, exercise, sleep, mental health)
    └── Creativity & Innovation (design thinking, lateral thinking)

800+ subjects across 33 skills, covering 15 national education systems and 6 international curricula.

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Built on Learning Science

Every skill applies cognitive science principles — not just what to teach, but how human brains actually learn and remember:

Principle	Mechanism	Evidence
🔄 Spaced Repetition	Fights the forgetting curve with optimal review intervals	Meta-analysis: 242 studies, 169K+ participants (Donoghue & Hattie, 2021)
🧪 Active Recall	Retrieval practice strengthens memory 10x vs re-reading	Roediger & Butler (2011), Journal of Memory and Language
🎯 Desirable Difficulties	Short-term struggle → long-term retention	Bjork & Bjork (2011)
🔀 Interleaving	Mixing topics builds discrimination ability	Pan et al. (2018), J. Exp. Psych: General
🖼️ Dual Coding	Words + visuals = stronger encoding	Paivio (1991), Psychological Review
🪞 Socratic Method	Questions > answers for deep understanding	Chi et al. (2001), Cognitive Science
🧩 Chunking	Group information into meaningful units	Miller (1956), Psychological Review
📊 Bloom's Taxonomy	6 cognitive levels: remember → create	Anderson & Krathwohl (2001)

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Installation

Quick Start

# Clone the repository
git clone https://github.com/24kchengYe/human-skill-tree.git

# Copy all skills to your agent
cp -r human-skill-tree/skills/* ~/.claude/skills/

Install Individual Skills

# Meta-skill: learning methodology
cp -r human-skill-tree/skills/00-learning-how-to-learn ~/.claude/skills/

# K-12 math tutoring
cp -r human-skill-tree/skills/01-k12-mathematics ~/.claude/skills/

# Chinese social intelligence 人情世故
cp -r human-skill-tree/skills/05-social-intelligence ~/.claude/skills/

Supported AI Tools

Tool	Skill Directory	Status
Claude Code	~/.claude/skills/	✅ Primary
Cursor	~/.cursor/skills/	✅ Supported
OpenAI Codex CLI	~/.codex/skills/	✅ Supported
Gemini CLI	~/.gemini/skills/	✅ Supported
GitHub Copilot	Custom config	🔜 Planned
DeepSeek	Custom config	🔜 Planned

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Ecosystem

We build on top of existing tools. Here are the best complementary projects:

MCP Servers

Server	What It Does	Stars
DeepTutor	AI personalized learning assistant (HKU)	⭐ 9,000+
Anki MCP	Spaced repetition flashcard integration	⭐ 154
Canvas LMS MCP	54 tools for learning management	Moderate
Wolfram Alpha MCP	Computational knowledge engine	Multiple
MandarinMCP	HSK Chinese vocabulary + spaced repetition	New

Agent Skills

Skill	Install
education-tutor	npx skills add eddiebe147/claude-settings@education-tutor -g -y
learn-faster-kit	npx skills install hluaguo/learn-faster-kit
academic-research-writer	npx skills add endigo/claude-skills@academic-research-writer -g -y

See docs/landscape.md for the full ecosystem survey (50+ tools).

🌐 Interactive Web App — Immersive AI Classroom

🔗 Live Demo: humanskilltree.yechengzhang.com | Source Code: app/

A full-featured AI learning platform built with Next.js 16 + Vercel AI SDK + Supabase. Not just a chatbot — an immersive AI classroom inspired by Tsinghua OpenMAIC.

Core Features

Feature	Description
🎭 6 AI Tutors	Aria ✨ Marcus 🎯 Lin 🌊 Euler 📐 Feynman 🥁 Curie ⚗️ — each with unique personality + Socratic teaching
👥 Multi-Agent Classroom	Teacher + Assistant + 2 AI Students discuss topics together — like a real classroom
🎨 AI Whiteboard	Auto-generates Mermaid diagrams, LaTeX formulas, SVG graphics during teaching
📊 Real-Time Slides	AI generates presentation slides as it teaches — fullscreen + PPTX export
🧪 HTML5 Simulations	Interactive experiments generated on-the-fly for hands-on learning
📝 Smart Quizzes	4 types (choice / true-false / fill-blank / short-answer with AI grading) — answers affect KP mastery
📋 PBL Projects	Project-based learning with milestones, role selection, AI mentor guidance
📄 Doc → Course	Upload PDF/TXT → auto-generates course outline + slides + knowledge points
🔊 TTS Read-Aloud	Text-to-speech on every message + auto-read toggle — filters out emojis & action descriptions
📈 Learning Science	Spaced repetition, XP/levels, streak tracking, knowledge point graph
🌍 i18n	English, 中文, 日本語
🎨 Themes	Light / Dark / System

Tech Stack

Next.js 16 · TypeScript · Tailwind CSS v4 · shadcn/ui · React Flow
Vercel AI SDK v6 · OpenRouter (18 models) · Supabase Auth + PostgreSQL
Mermaid · KaTeX · pptxgenjs · next-intl · next-themes

Run Locally

cd app
npm install
cp .env.example .env.local  # configure your API keys
npm run dev

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Education Systems Covered

🌍 15 National Systems

Region	Countries
East Asia	🇨🇳 China (高考), 🇯🇵 Japan (共通テスト), 🇰🇷 Korea (수능), 🇸🇬 Singapore
Europe	🇬🇧 UK (GCSE/A-Level), 🇩🇪 Germany (Abitur), 🇫🇷 France (Bac), 🇳🇱 🇨🇭 🇫🇮
Americas	🇺🇸 USA (SAT/AP), 🇨🇦 Canada
South Asia	🇮🇳 India (JEE/NEET)
Middle East	🇮🇱 Israel (Bagrut)
Oceania	🇦🇺 Australia (ATAR)

🌐 6 International Curricula

IB · Cambridge IGCSE/A-Level · Montessori · Waldorf/Steiner · Reggio Emilia · Classical Trivium

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Roadmap

• Phase 1: Project structure, 3 flagship skills, ecosystem survey
• Phase 2: 33 skills, K-12 to career coverage
• Phase 3: Web App v1 — Socratic tutors, gamification, i18n
• Phase 4: Web App v2 — OpenMAIC-inspired 9 features (classroom, whiteboard, slides, simulations, PBL, quiz, TTS, doc-to-course, PPTX export)
• Phase 5: Monorepo merge — skills + app in one repo, AGPL-3.0
• Phase 6: Virtual tutor avatars (Live2D), Edge TTS, community contributions

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Contributing

See CONTRIBUTING.md for guidelines. Two ways to contribute:

Path A: AI Education Skills (skills/)

• 🆕 New skills — Pick any uncovered subject, write a SKILL.md
• 🌍 Translations — Help learners worldwide
• 📊 Research — Learning science insights with citations

Path B: Web App Development (app/)

• 🎨 UI/UX — Improve the learning interface
• 🧪 Features — New interactive learning modes
• 🌐 i18n — Add new languages
• 🔗 Integrations — MCP servers, flashcard tools, LMS platforms

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Philosophy

Why "Skill Tree"?

In RPGs, a skill tree is a branching structure where you unlock abilities by investing points. Real life works the same way — prerequisites matter, multiple paths exist, specialization is valid, and you can always respec.

Why Social Intelligence?

No other AI skill project covers 人情世故 — the art of navigating human relationships. Yet this is arguably the most important skill set for success in life. AI can be a safe space to practice these skills through scenario simulation.

Why Learning Science?

Most AI tutoring is "ask a question, get an answer." This creates the illusion of learning — you feel like you understand, but forget within days. Our skills are designed around how memory actually works: test before tell, space it out, make it hard, connect it, apply it.

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Support This Project

If Human Skill Tree helps you learn anything, consider:

• ⭐ Star this repo — helps others discover it
• 🍕 Sponsor — fund continued development
• 🔀 Fork & contribute — help build the tree

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References

1. Bastani, H. et al. (2025). "Generative AI without guardrails can harm learning." PNAS, 122(26). Link
2. Dunlosky, J. et al. (2013). "Improving Students' Learning With Effective Learning Techniques." Psychological Science in the Public Interest, 14(1), 4-58.
3. Donoghue, G.M. & Hattie, J.A.C. (2021). "A Meta-Analysis of Ten Learning Techniques." Frontiers in Education.
4. Pan, S.C. & Rickard, T.C. (2018). "Transfer of test-enhanced learning." J. Experimental Psychology: General, 147(11), 1641-1664.
5. Brown, P.C., Roediger, H.L., & McDaniel, M.A. (2014). Make It Stick: The Science of Successful Learning. Harvard University Press.
6. Tabibian, B. et al. (2019). "Enhancing human learning via spaced repetition optimization." PNAS, 116(10), 3988–3993. Link
7. Bjork, R.A. & Bjork, E.L. (2011). "Making Things Hard on Yourself, But in a Good Way."

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Changelog

• v2.0 (2026-03-18): Monorepo merge + OpenMAIC feature pack — merged app into monorepo; 9 new features (multi-agent classroom, AI whiteboard, real-time slides, HTML5 simulations, PBL projects, smart quizzes with AI grading, TTS read-aloud, doc-to-course, PPTX export); model router; AGPL-3.0 license
• v1.4 (2026-03-10): Web App upgraded with Socratic Tutor System — 6 AI tutor characters, cross-tutor memory, social world (group chat + diary), 5 story backgrounds, 7-layer system prompt
• v1.3 (2026-03-09): Updated to 33 skills; linked interactive web app with learning progress tracking, spaced repetition, and multi-language support
• v1.2 (2026-03-09): Added Socratic Teaching Mode to core skill; new AI Tutor Persona skill (32 total); all skills now include Progress Tracking & Spaced Review
• v1.1 (2026-03-08): Developed all 28 stub skills to full content; added validation script + CI; created GitHub Discussions, Release v1.0.0, 5 good-first-issues
• v1.0 (2026-03-07): Initial release with 31 skills covering K-12 through career development

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License

AGPL-3.0 — Skills (skills/) are dual-licensed MIT/AGPL-3.0 for maximum compatibility. App (app/) is AGPL-3.0. Knowledge should be free.

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这个时代的问题

2025 年，AI 智能体通过 Skill、MCP 和工具调用，获得了操控现实世界的能力。Claude 能运行代码、查询数据库、控制浏览器。ChatGPT 能联网搜索、写代码、分析数据。Gemini 能看、能听、能与物理世界交互。

AI 有了自己的技能树。人类呢？

• 一个 35 岁的职场人发现自己的学位正在过时。怎么赶上？
• 一个 2026 年出生的孩子，成年时 AI 已无处不在。该学什么？
• 一个博士生花 5 年钻研一个窄领域。这笔投资对吗？
• 一个农村来的大学生没有人脉、没有导师。谁来教他那些不成文的规则？

这些不是假设性的问题。这是我们这个时代最核心的问题。

洞察

最新研究告诉我们：

AI 辅导可大幅提升学习效果，但前提是有教学结构。 发表在 PNAS 上的大规模随机对照试验发现：GPT-4 辅导使高中数学成绩提升 48–127%，但缺乏教学设计时学生会依赖 AI，技能习得反而下降。精心设计的教学引导（给提示而非答案）才能恢复学习效果。

— Bastani et al. (2025), PNAS

间隔重复和主动回忆是科学已知最有效的学习方法。 一项涵盖 242 项研究、169,000+ 参与者的 Meta 分析证实：练习测试和分散练习的效果超过所有其他方法。

— Dunlosky et al. (2013); Donoghue & Hattie (2021)

科学结论很清晰：AI + 认知科学 = 人类历史上最强大的学习组合。 问题在于，AI 智能体没有内置的教学结构。它们什么都知道，但什么都不会教。

人类技能树填补这个缺口。

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这是什么

一套 33 个即装即用的 AI 智能体技能，能将 ChatGPT、Claude、Gemini、Copilot、DeepSeek 或任何兼容 AI 转化为结构化、基于科学的学习伙伴。

这是对 AI 与想要学习的人类互动方式的升级。

🎬 演示

首页	技能树
技能详情	AI 对话学习

💬 看看差别

❌ 没有人类技能树	✅ 有人类技能树
你： 教我微积分 AI： 微积分是研究连续变化的数学分支。有两个主要分支：微分和积分…… （给你一堵维基百科式的文字墙）	你： 教我微积分 AI： 开始之前，让我了解你的基础： 你能用自己的话说说什么是函数吗？ 你接触过斜率的概念吗？ 你的目标是——备考还是深入理解？ （评估你的水平，然后从你的基础开始构建）
你： 我的代码有 bug，帮我修 AI： 问题在第12行。把 arr[i] 改成 arr[i-1]。修改后的代码如下…… （直接给答案——你什么都没学到）	你： 我的代码有 bug，帮我修 AI： 我们一起调试。在我看答案之前： 你期望发生什么？ 实际发生了什么？ 你觉得哪一行有问题？ 试试在循环里加一个 print(i, arr[i])——你