# RootCraft Learning System

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**A high-efficiency learning methodology for deep knowledge mastery**

[![Version](https://img.shields.io/badge/version-1.0.0-blue.svg)](https://github.com/zhangquan/rootcraft-learning-system)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)

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## 📖 Overview

RootCraft Learning System integrates four powerful learning methodologies into a unified framework:

1. **First Principles Thinking** → Trace to fundamental facts and concepts
2. **Taxonomy-Based Classification** → Systematically decompose and structure
3. **Feynman Technique** → Validate through output, identify gaps
4. **Recursive Questioning** → Chase "aha moments" through layered inquiry

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## 🎯 Key Features

- ✅ **9-Step Learning Flow** - Complete process from goal setting to knowledge mastery
- ✅ **Recursive Questioning Framework** - 5 sub-steps for deep understanding
- ✅ **Aha Moment Tracking** - Document breakthrough insights
- ✅ **Practical Examples** - Real-world learning scenarios (Diffusion Models, Machine Learning)
- ✅ **File Organization Templates** - Structured directory for learning materials
- ✅ **Spaced Repetition Plan** - Long-term retention strategy

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## 🚀 Quick Start

### Installation

```bash
# Install via ClawHub
clawhub install rootcraft-learning-system
```

### Usage

When you want to learn something new, simply say:

- "I want to learn..."
- "How to learn efficiently..."
- "Help me create a study plan..."
- "This concept is unclear..."

The system will guide you through the 9-step learning flow.

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## 📚 Core Methodology

### Step 1-3: Foundation
- Define goals and evaluation criteria
- Apply first principles thinking
- Build taxonomy-based classification

### Step 4: Deep Understanding (Core)
The Feynman Technique with Recursive Questioning:

```
4.1 Start with a Real Problem
4.2 Generate Questions Through Practice
4.3 Recursive Downward Questioning
4.4 Restate in Your Own Words
4.5 Chase the "Aha!" Moments
```

### Step 5-9: Consolidation
- Multi-perspective learning
- Practice & application
- Feedback & iteration
- Continuous review
- Mind mapping & notes

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## 💡 Example: Learning Diffusion Models

### Recursive Questioning Chain

```
Q: "Why do we add noise gradually?"
→ A: "To create a tractable path between data and noise"

Q: "What does 'tractable path' mean?"
→ A: "A path we can reverse mathematically"

Q: "Why do we need to reverse it?"
→ A: "Because generation = going from noise back to data"

💡 AHA! "Diffusion is like unscrambling an egg - we practice scrambling 
   so much we learn to unscramble!"
```

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## 📁 File Structure

```
study/{topic}/
├── 01-goals.md              # Learning goals and evaluation criteria
├── 02-first-principles.md   # First principles analysis
├── 03-taxonomy.md           # Knowledge taxonomy tree
├── 04-feynman.md            # Feynman explanation notes
├── 04b-recursive-questions.md # Question chains and answers
├── 04c-aha-moments.md       # Breakthrough insights log
├── 05-resources.md          # Learning resources list
├── 06-projects.md           # Practice projects
├── 07-feedback.md           # Feedback and iteration records
├── 08-review.md             # Spaced review plan
└── 09-mindmap.md            # Mind mapping source file
```

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## 🛠️ Recommended Tools

- **Mind Mapping**: XMind, MindNode, Obsidian
- **Note-taking**: Notion, Obsidian, Evernote
- **Spaced Repetition**: Anki, RemNote
- **Pomodoro Timer**: Forest, Focus

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## 📝 Version History

| Version | Date | Changes |
|---------|------|---------|
| 1.0.0 | 2026-04-30 | Official release - Integrated First Principles, Taxonomy Classification, Feynman Technique, and Recursive Questioning into 9-step learning flow with "Aha Moment" tracking |
| 0.1.0 | 2024-XX-XX | Original Chinese version "格物本质赋能学习法" launched |

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## 🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

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## 🙏 Acknowledgments

- First Principles Thinking - Aristotle, Elon Musk
- Feynman Technique - Richard Feynman
- Taxonomy-Based Classification - MECE Principle
- Recursive Questioning - Socratic Method

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**Made with ❤️ by Zhang Quan**

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