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.

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

💬 See the Difference

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:

1. Can you tell me what a function is in your own words?
2. Have you seen the concept of slope before?
3. 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:

1. What did you expect to happen?
2. What actually happened?
3. 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 |

🔥 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.

🌳 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, empath