#learning#学习

RootCraft Learning System

RootCraft Learning System - An integrated learning methodology combining First Principles Thinking, Taxonomy-Based Classification, Feynman Technique, and Rec...

Install

openclaw skills install @zhangboheng/rootcraft-learning-system

RootCraft Learning System (格物本质赋能学习法)

🎓 User Guide / 使用指南

When user asks "how to use this skill" or "如何使用", show this guide.

中文引导 (Chinese Guide)

text

🎓 欢迎使用格物本质赋能学习法！

这是一个高效学习系统，帮你从"学了就忘"变成"学了就懂"。

┌─────────────────────────────────────────────────────────────┐
│  📚 核心方法                                              │
├─────────────────────────────────────────────────────────────┤
│  🔍 第一性原理    →  追问本质，挖到不能再挖为止           │
│  📊 分类学        →  系统拆解，构建 MECE 结构             │
│  🎤 费曼 technique →  讲给别人听，发现知识缺口            │
│  ❓ 递归追问      →  层层深挖，追到"啊哈"时刻            │
└─────────────────────────────────────────────────────────────┘

📋 9 步学习流程：
1. 设定目标 & 评估标准
2. 应用第一性原理
3. 分类学拆解
4. 费曼 + 递归追问（核心）
5. 多视角学习
6. 实践应用
7. 反馈与迭代
8. 持续复习
9. 思维导图与笔记

🚀 快速开始：
• 直接说"我想学习【主题】" → 直接开始完整学习流程
• 或说"帮我用格物本质赋能学习法学习【内容】"
• 说"我想制定学习计划"让我帮你规划

⚡ **直接学习模式**：
当用户说"我想学习XX"时，直接输出完整的9步学习流程，不要问问题！

流程模板：

🎓 好的，让我们开始学习【主题】！

--- 9步学习流程 ---

设定目标 & 评估标准（直接给出该主题的学习目标和评估标准）
第一性原理（解释该主题的本质是什么）
分类学拆解（MECE）（系统化拆解该主题的知识体系）
费曼 + 递归追问（核心概念检验 + 追问链）
多视角学习（从数学、工程、应用等角度分析）
实践应用（给出一个最小可运行的代码示例）
反馈与迭代（含试卷生成功能）

text

🎯 试卷生成：根据所学内容自动生成测试题

题型包括：
• 选择题（3-5题）- 检验概念理解
• 填空题（3-5题）- 检验关键术语记忆
• 简答题（2-3题）- 检验逻辑表达能力
• 操作题（1-2题）- 检验实际应用能力

每道题标注：难度等级、知识点归属、参考答案

持续复习（艾宾浩斯遗忘曲线复习计划）
思维导图与笔记（输出知识图谱）

💡 Aha Moment记录 📦 Anki卡片

--- 自动保存详细内容 --- ⚠️ 必须将上述9步的详细内容保存到文件，而不是只保存模板！

保存方式：

python

import sys
sys.path.insert(0, '/root/.openclaw/skills/rootcraft-learning-system')
from study_writer import create_study_files

# 将详细内容保存到文件
content = {
    "goals": """# 学习目标\n\n（详细内容）""",
    "first_principles": """# 第一性原理\n\n（详细内容）""",
    # ... 其他步骤的详细内容\n}
result = create_study_files('主题', content, anki_cards)

💡 内容质量提示：为了生成高质量文档，请在content中提供详细、完整的内容。\n> \n> 详细模板示例：\npython\ncontent = {\n 'goals': \"\"\"# 学习目标\n\n## 短期目标（1-4周）\n- 理解AI的基本概念和核心原理\n- 掌握机器学习基础知识\n\n## 中期目标（1-3个月）\n- 深入理解机器学习核心理论\n- 能够独立完成ML项目\n\n## 长期目标（3-12个月）\n- 达到专业水平\n- 能够创新和解决问题\n\n## 评估标准\n- [ ] 掌握核心概念≥10个\n- [ ] 完成项目≥3个\n- [ ] 学习时长≥100小时\n\"\"\",\n \n 'first_principles': \"\"\"# 第一性原理\n\n## 核心公理\n\n### 公理1：机器学习是函数拟合\n- 输入x → f(x) → 输出y\n- 关键是找到最好的f\n\n### 公理2：数据决定模型上限\n- Garbage In, Garbage Out\n- 特征工程决定效果\n\n### 公理3：没有免费午餐\n- 没有最优算法\n\n### 公理4：奥卡姆剃刀\n- 简单模型往往更好\n\n## 核心定义\nAI的本质是[填写]\n\n## 关键洞察\n> 一句话总结核心领悟\n\"\"\",\n \n 'taxonomy': \"\"\"# 知识分类体系\n\n## 算法分类\n\n### 监督学习\n| 类型 | 算法 | 场景 |\n|------|------|------|\n| 分类 | 逻辑回归、SVM | 分类预测 |\n| 回归 | 线性回归、GBDT | 数值预测 |\n\n### 无监督学习\n| 类型 | 算法 | 场景 |\n|------|------|------|\n| 聚类 | K-Means | 用户分群 |\n| 降维 | PCA | 可视化 |\n\n### 深度学习\n| 模型 | 应用场景 |\n|------|------|\n| CNN | 图像 |\n| Transformer | NLP |\n\"\"\",\n \n 'feynman': \"\"\"# 费曼检验\n\n## 核心概念检验\n\n### Q1: 什么是梯度下降？\nA: 像下山找最低点，每次往低处走一点\n\n### Q2: 过拟合是什么？\nA: 记住题库而不是学会做题\n\n## 递归追问链\n- 最根本的原因是什么？\n- 还有更基础的原因吗？\n\n## Aha Moment\n记录你的领悟时刻\n\"\"\",\n \n 'multi_perspective': \"\"\"# 多视角学习\n\n## 技术视角\n- 算法原理\n- 实现方法\n- 优缺点\n\n## 应用视角\n- CV、NLP、推荐系统\n- 行业案例\n\n## 历史视角\n- 发展历程\n- 关键里程碑\n\n## 哲学视角\n- 思维方式\n- 底层逻辑\n\"\"\",\n \n 'practice': \"\"\"# 实践应用\n\n## 最小可运行方案\n\n### 环境准备\nbash\npip install numpy pandas scikit-learn\n\n\n### 核心代码\npython\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\n\n# 示例代码\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)\n\n\n## 实战项目\n- 项目1：入门级\n- 项目2：进阶级\n\"\"\",\n \n 'feedback': \"\"\"# 反馈与迭代\n\n## 常见问题\n\n### Q1: 数学不够怎么办？\nA: 够用即可：梯度、矩阵、概率\n\n### Q2: 调参玄学怎么办？\nA: 网格搜索→贝叶斯优化\n\n## 解决方案\n1. 制定计划\n2. 定期复盘\n\"\"\",\n \n 'review': \"\"\"# 复习计划\n\n## 艾宾浩斯复习表\n\n| 时间点 | 内容 |\n|--------|------|\n| 10分钟 | 核心概念 |\n| 1天 | 基础知识点 |\n| 3天 | 进阶知识 |\n| 7天 | 整体串联 |\n| 14天 | 重点强化 |\n| 30天 | 全面复盘 |\n\n## 记忆技巧\n- 联想记忆\n- 间隔重复\n\"\"\",\n \n 'mindmap': \"\"\"# 思维导图\n\n## 中心主题\n机器学习\n\n## 一级分支\n- 基础：数学、Python\n- 算法：监督、无监督、深度学习\n- 工程：数据处理、模型训练、部署\n- 应用：CV、NLP、推荐\n\n## 关键连接\n- 基础→算法→应用\n\"\"\"\n}\n\n ✅ 文件保存到 /root/.openclaw/workspace/study/{主题}/ ✅ 包含12个md文件 + anki_cards.csv ✅ 新增：08-exam.md（试卷文件）

⚠️ 重要：每次学习流程结束后，必须调用文件生成模块：

python

import sys
sys.path.insert(0, '/root/.openclaw/skills/rootcraft-learning-system/study_writer')
from study_writer import generate_and_save, quality_check, print_quality_report, save_exam_paper

# 1. 生成学习文件
result = generate_and_save('主题名称')

# 2. 手动生成试卷（可选）
exam_path = save_exam_paper('主题名称')

# 3. 执行质检
qc_result = quality_check('主题名称')
print_quality_report(qc_result)

📋 质检模块 (Quality Check) - v1.1.1: 每次学习流程结束后，系统检查以下内容：

检查项	文件	说明
✅ 学习目标	01-goals.md	1.设定目标&评估标准
✅ 第一性原理	02-first-principles.md	2.第一性原理分析
✅ 分类学拆解	03-taxonomy.md	3.MECE结构拆解
✅ 费曼+追问	04-feynman.md	4.核心概念检验
✅ 多视角学习	05-multi-perspective.md	5.多角度分析
✅ 实践应用	06-practice.md	6.最小可运行示例
✅ 反馈与迭代	07-feedback.md	7.常见问题+试卷
✅ 持续复习	08-review.md	8.艾宾浩斯计划
✅ 思维导图	09-mindmap.md	9.知识图谱
✅ Aha Moment	10-aha-moment.md	啊哈时刻记录
✅ Anki卡片	anki_cards.csv	可导入的记忆卡片
⭐ 试卷文件	08-exam.md	选择题+填空题+简答题+操作题

质检函数：

python

from study_writer import quality_check, print_quality_report

qc = quality_check('主题名称')
print_quality_report(qc)

试卷生成函数：

python

from study_writer import save_exam_paper

exam_file = save_exam_paper('主题名称')
print(f"试卷已保存: {exam_file}")

💡 特色产出： • Aha Moment 记录本（追踪每个"啊哈"时刻） • 知识分类树（系统化拆解） • ⭐ 自动试卷生成（选择题+填空题+简答题+操作题） • ⭐ 新增：Anki 记忆卡片生成（基于艾宾浩斯遗忘曲线） • 递归追问链（问题链条）

📦 记忆卡片功能 (Memory Cards)：

说「生成【主题】的Anki卡片」→ 自动生成可导入 Anki 的卡片
说「查看我的复习计划」→ 显示基于记忆曲线的复习安排
说「导出生词卡片」→ 导出 CSV 文件，可在 Anki 中导入使用
支持中英文

text


### English Guide

🎓 Welcome to RootCraft Learning System!

An efficient learning methodology that transforms "learn and forget" into "learn and understand."

┌─────────────────────────────────────────────────────────────┐ │ 📚 Core Methods │ ├─────────────────────────────────────────────────────────────┤ │ 🔍 First Principles → Ask why until reaching essence │ │ 📊 Taxonomy → Systematically decompose (MECE) │ │ 🎤 Feynman Technique → Teach to find knowledge gaps │ │ ❓ Recursive Questioning → Dig deeper to find "aha!" │ └─────────────────────────────────────────────────────────────┘

📋 9-Step Learning Flow:

Define Goals & Evaluation
Apply First Principles
Taxonomy-Based Classification
Feynman + Recursive Questioning (Core)
Multi-Perspective Learning
Practice & Application
Feedback & Iteration
Continuous Review
Mind Mapping & Notes

🚀 Quick Start: • Just say "I want to learn【topic】" → Directly start full learning flow • Or "Help me learn【content】using this method" • Say "I want to create a study plan" for guidance

⚡ Direct Learning Mode: When user says "I want to learn XX", directly output the complete 9-step learning flow - NO QUESTIONS ASKED!

Template:

text

🎓 Alright! Let's learn 【topic】!

--- 9-Step Learning Flow ---

1. Goals & Evaluation
2. First Principles
3. Taxonomy (MECE)
4. Feynman + Recursive Questioning
5. Multi-Perspective
6. Practice Application
7. Feedback & Iteration
8. Continuous Review
9. Mind Map & Notes

💡 Aha Moment
📦 Anki Cards

💡 Key Outputs: • Aha Moment Log (track every "aha!" moment) • Knowledge Taxonomy Tree (systematic decomposition) • ⭐ NEW: Anki Memory Cards (Ebbinghaus forgetting curve based) • ⭐ NEW: Exam Paper Generation (Multiple Choice + Fill-in + Short Answer + Practice) • Recursive Question Chains (question chains) • 🤖 AI Question Chain Generator (Bilingual)

📋 Quality Check Module (v1.1.1): After learning completes, run quality check:

python

import sys
sys.path.insert(0, '/root/.openclaw/skills/rootcraft-learning-system/study_writer')
from study_writer import generate_and_save, quality_check, print_quality_report, save_exam_paper

# 1. Generate study files
result = generate_and_save('topic')

# 2. Generate exam paper (optional)
exam_path = save_exam_paper('topic')

# 3. Run quality check
qc_result = quality_check('topic')
print_quality_report(qc_result)

Quality Check Files (12 total):

#	Check Item	File	Description
1	Goals	01-goals.md	Goal setting & evaluation
2	First Principles	02-first-principles.md	Core axioms analysis
3	Taxonomy	03-taxonomy.md	MECE decomposition
4	Feynman	04-feynman.md	Concept validation
5	Multi-Perspective	05-multi-perspective.md	Multi-angle analysis
6	Practice	06-practice.md	Code examples
7	Feedback	07-feedback.md	FAQ + exam
8	Review	08-review.md	Ebbinghaus schedule
9	Mind Map	09-mindmap.md	Knowledge graph
10	Aha Moment	10-aha-moment.md	Aha records
11	Anki Cards	anki_cards.csv	Importable cards
12	⭐ Exam Paper	08-exam.md	MC+Fill+QA+Practice

📦 Memory Cards Feature:

Say "Generate Anki cards for [topic]" → Auto-generate Anki-importable cards
Say "Show my review schedule" → Display memory curve-based review plan
Say "Export vocabulary cards" → Export CSV for Anki import
Supports Chinese & English

text


---

## Core Principle

A high-efficiency learning methodology that integrates **First Principles Thinking**, **Taxonomy-Based Classification**, **Feynman Technique**, and **Recursive Questioning** into a closed-loop system:

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

## Learning Flow (9 Steps)

When users mention learning, exam prep, or skill acquisition, guide them through this process:

### Step 1: Define Goals & Evaluation Criteria
- Clarify learning objectives (target proficiency level)
- Establish evaluation standards (how to measure mastery)
- Set timelines and milestones

### Step 2: Apply First Principles
- Break down problems to find fundamental facts
- Keep asking "why" until reaching irreducible truths
- Distinguish between assumptions and verified facts

### Step 3: Use Taxonomy-Based Classification
- Divide topics into distinct subtopics/categories
- Build classification systems (preferably MECE: Mutually Exclusive, Comprehensively Encompassing)
- Clarify relationships between categories

### Step 4: Apply Feynman Technique with Recursive Questioning

**Core Process (5 Sub-Steps)**:

#### 4.1 Start with a Real Problem
- Begin with concrete, practical challenge
- Example: "Write a diffusion model code" or "Implement this algorithm"
- Ground learning in tangible context

#### 4.2 Generate Questions Through Practice
- While working, note every confusion point
- Ask: "Why does this work?" "What does this term mean?"
- Record questions without immediately seeking answers

#### 4.3 Recursive Downward Questioning
- For each unclear concept, ask deeper questions
- Pattern: "What do you mean by X?" → "Why is X necessary?" → "What happens without X?"
- Continue until reaching intuitive understanding
- Example chain:
  - "What is 'gradient descent'?" →
  - "Why do we need to minimize loss?" →
  - "What is 'loss' actually measuring?" →
  - "Why is measuring error useful?" →
  - **Aha!** "Loss is just a compass pointing toward better answers"

#### 4.4 Restate in Your Own Words
- After each answer, rephrase to confirm understanding
- Use: "So my understanding is... Is this correct?"
- If explanation feels forced or unclear, return to 4.3
- Valid understanding = can explain to a 10-year-old

#### 4.5 Chase the "Aha!" Moments
- Recognize the click: "Oh! That's why!"
- These moments mark true comprehension milestones
- Document each aha moment with:
  - What was unclear before
  - What clicked
  - Why it matters
- **Key insight**: One deep aha > Ten shallow memorizations

### Step 5: Multi-Perspective Learning
- Use diverse resources (books/videos/courses/practical exercises)
- Cross-validate information across sources
- Find the optimal personal learning path

### Step 6: Practice & Application
- Select relevant projects for hands-on practice
- Analyze real-world case studies
- Connect theory with practical application
- Apply recursive questioning to new challenges

### Step 7: Feedback & Iteration
- Regular review of learned content
- Seek peer feedback (teach, question, evaluate)
- Adjust learning strategy based on insights
- Revisit aha moments to reinforce understanding

**📝 Exam Paper Generation** (NEW in v1.1.1):

🎯 Auto-generate exam questions based on learning content

Question types: • Multiple Choice (3-5) - Concept understanding • Fill in the Blank (3-5) - Key term memory • Short Answer (2-3) - Logical expression • Practical/Code (1-2) - Application ability

Each question includes: difficulty level, knowledge point, reference answer

text


### Step 8: Continuous Learning & Review
- Periodically revisit mastered content (spaced repetition)
- Follow Ebbinghaus Forgetting Curve for reviews
- Expand into related knowledge domains
- Apply recursive questioning to advanced topics

### Step 9: Mind Mapping & Notes
- Use mind mapping tools to organize knowledge structure
- Build systematic note-taking systems (Cornell Notes method)
- Maintain traceable and updatable notes
- **Special**: Create "Aha Moment Log" tracking breakthrough insights

## Trigger Scenarios

When users say (English):
- "I want to learn..."
- "How to learn efficiently..."
- "Is there a good learning method..."
- "Help me create a study plan..."
- "This concept is unclear..."
- "Want to systematically master..."
- "Why does this work?"
- "I don't understand..."

当用户说 (Chinese/中文):
- "我想学习..."
- "怎么高效学习？"
- "有什么好的学习方法？"
- "帮我制定学习计划..."
- "这个概念不清楚..."
- "想系统掌握..."
- "为什么是这样？"
- "我不懂..."

→ 主动推荐此学习方法 / Proactively recommend this method

---

## Multi-Language Response (Bilingual Support)

This skill supports **bilingual responses** based on user's language preference. The response language should match the user's input language.

### Language Detection Logic

| User Input Language | Response Language | Example Trigger |
|---------------------|-------------------|-----------------|
| English | English | "I want to learn machine learning" |
| 中文/Chinese | Chinese (中文) | "我想学习机器学习" |
| Mixed | Use dominant language | If majority is Chinese → Chinese |

### Response Template

**When user speaks English:**

RootCraft Learning System

This is a high-efficiency learning methodology...

[English content following the 9-step flow]

text


**When user speaks Chinese:**

格物本质赋能学习法 (RootCraft Learning System)

这是一个融合第一性原理、分类学、费曼 technique 与递归追问的高效学习系统...

[中文内容，遵循 9 步流程]

text


### Key Bilingual Elements

| Element | English | Chinese |
|---------|---------|---------|
| Skill Name | RootCraft Learning System | 格物本质赋能学习法 |
| Core Concept | First Principles | 第一性原理 |
| Key Term | Aha Moment | 啊哈时刻 |
| Method | Feynman Technique | 费曼 technique |
| Framework | Taxonomy Classification | 分类学拆解 |
| Process | Recursive Questioning | 递归追问 |

### Practical Guidelines

1. **Detect language** from user's first message
2. **Switch language** only at conversation start, maintain consistency
3. **Bilingual output** for skill description sections (both languages)
4. **User guidance** always in user's detected language
5. **Technical terms** can remain in English (e.g., MECE, Feynman) with Chinese explanation

---


---

## 🤖 AI-Assisted Question Chain Generation

> 自动生成递归追问链 / Auto-generate recursive questioning chains

This skill can automatically generate **recursive questioning chains** for any topic using AI. The question chain helps users dig deeper until reaching the "aha moment".

### Trigger Commands / 触发指令

| 中文指令 | English Command |
|----------|-----------------|
| 帮我生成关于【主题】的追问链 | Generate a question chain for 【topic】 |
| 用 AI 深挖【概念】 | Dig deeper into 【concept】 with AI |
| 自动追问【主题】 | Auto-question 【topic】 |
| 生成追问链 | Create question chain |

### How It Works / 工作原理

用户输入 → 检测意图（生成追问链） → 提取主题 → 调用 LLM 生成（中英文双语） → 格式化输出 → 建议用户"继续深挖"或"记录 Aha"

text


### Prompt Template (Bilingual) / 提示词模板

#### 中文版本

你是一个专业的学习教练，擅长用递归追问法帮助学生理解任何概念。

请为「{topic}」生成一个 {depth} 层的递归追问链。

格式要求

Level 1: 基础定义

问题要简洁明了
答案要用通俗语言，像在和 10 岁小孩说话

Level 2: 原理追问

问"为什么"
给出技术解释

Level 3: 本质挖掘

问到最底层的"为什么"
区分假设和事实

Level 4: 边界测试

问"如果没有 X 会怎样"
探索反例

Level 5: 啊哈时刻

用一句话总结本质
用一个比喻让 10 岁小孩也能懂

输出格式

Q1: [问题]

A1: [答案]

Q2: [问题]

A2: [答案]

...

💡 AHA MOMENT: [一句话本质总结 + 通俗比喻]

text


#### English Version

You are a professional learning coach, skilled at using recursive questioning to help students understand any concept.

Please generate a {depth}-level recursive questioning chain for "{topic}".

Format Requirements

Level 1: Basic Definition

Questions should be concise and clear
Answers should use simple language, like explaining to a 10-year-old

Level 2: Principle Inquiry

Ask "why"
Provide technical explanation

Level 3: Essence Mining

Ask the deepest "why"
Distinguish between assumptions and facts

Level 4: Boundary Testing

Ask "what if X didn't exist?"
Explore counterexamples

Level 5: Aha Moment

Summarize the essence in one sentence
Use a metaphor a 10-year-old can understand

Output Format

Q1: [Question]

A1: [Answer]

Q2: [Question]

A2: [Answer]

...

💡 AHA MOMENT: [One-sentence essence summary + simple metaphor]

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### Example Output / 示例输出

**Input / 输入**: topic = "梯度下降" / "gradient descent"

**Output / 输出**:

追问链：梯度下降 / Question Chain: Gradient Descent

Q1: 什么是梯度下降？ / What is gradient descent?

A1: 梯度下降是一种优化算法，用来找到函数的最小值。就像下山时每一步都往最陡的下坡走。

Q2: 为什么要用"梯度"？ / Why use "gradient"?

A2: 梯度指向函数上升最快的方向。反过来，沿着梯度的相反方向走，就是下降最快的方向。

Q3: 为什么一定要"下降"？ / Why must it "descend"?

A3: 因为我们的目标是找到最小值（最优解）。下降是逼近最小值的方式。

Q4: 如果不用梯度，还有什么方法？ / If not gradient, what else?

A4: 还有牛顿法（用二阶导数）、随机搜索、遗传算法等。但梯度下降是最简单高效的。

Q5: 梯度下降的本质是什么？ / What's the essence of gradient descent?

A5: 梯度下降的本质是"贪心策略"——每一步都做当前最优选择，虽然不一定全局最优，但足够实用。

💡 AHA MOMENT: 梯度下降 = "每步都往下走一走" 本质 = 贪心算法在优化问题上的应用比喻 = 像盲人下山，每步都摸一下坡度，往最陡的方向走

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### Supported Parameters / 支持参数

| Parameter | 说明 / Description | Default / 默认值 |
|-----------|-------------------|-----------------|
| topic | 学习主题 / Learning topic | Required / 必填 |
| depth | 追问深度 / Question depth | 5 |
| focus | 重点方向（可选）/ Focus area (optional) | None |
| language | 输出语言 / Output language | auto-detect |

### Post-Generation Actions / 生成后操作

1. **建议继续深挖** / Suggest continuing:
   - "要针对某一层继续追问吗？"
   - "Want to dig deeper into any specific level?"

2. **建议记录 Aha** / Suggest recording:
   - "可以把你的 Aha Moment 记录下来，要我帮你写进去吗？"
   - "Would you like me to record your Aha Moment?"

3. **生成追问链卡片** / Generate shareable card:
   - 可生成图片/文本格式的追问链卡片
   - Can generate image/text format question chain cards

## Output Format Suggestions

Can generate for users:
- Learning goal checklist
- Knowledge taxonomy tree
- Feynman explanation template
- **Recursive questioning script** (question chain template)
- **Aha moment tracker** (breakthrough log)
- Review schedule table
- Mind mapping structure

## File Organization

✅ **自动保存**：学习资料会自动保存到以下路径

路径：`/root/.openclaw/workspace/study/{主题}/`

示例：

/root/.openclaw/workspace/study/机器学习/ ├── 01-goals.md # 学习目标和评估标准 ├── 02-first-principles.md # 第一性原理分析 ├── 03-taxonomy.md # 知识分类体系 ├── 04-feynman.md # 费曼 Technique 笔记 ├── 04b-recursive-questions.md # 递归追问链 ├── 04c-aha-moments.md # Aha Moment 记录 ├── 05-resources.md # 学习资源列表 ├── 06-projects.md # 实践项目代码 ├── 07-feedback.md # 反馈与迭代记录 ├── 08-review.md # 复习计划 └── 09-mindmap.md # 思维导图源文件

text


**自动生成内容**：
1. 9步学习流程的完整笔记（11个md文件）
2. Anki记忆卡片（anki_cards.csv，与笔记同目录）
3. 复习计划表

**文件用途**：
- 可直接在本地查阅
- 可导入Notion/Obsidian等工具
- Anki卡片可导入Anki用于复习

## Recommended Tools

- Mind Mapping: XMind, MindNode, Obsidian
- Note-taking: Notion, Obsidian, Evernote
- Spaced Repetition: Anki, RemNote
- Pomodoro Timer: Forest, Focus
- Question Tracking: Obsidian Daily Notes, Notion Database

## Version History

| Version | Date       | Changes |
|---------|------------|---------|
| 1.1.2   | 2026-05-04 | Fix: 试卷生成选择题选项修复，改为有意义的学习相关选项 |
| 1.1.5   | 2026-05-08 | Added quality scorer (4-dimension: completeness/accuracy/logic/practicality) + recursive question generator (domain-specific question chains) |
| 1.0.5   | 2026-05-02 | Added Anki memory cards generation based on Ebbinghaus forgetting curve - supports Chinese & English |
| 1.0.4   | 2026-05-01 | Added AI-assisted question chain generation (bilingual: Chinese & English) |
| 1.0.3   | 2026-05-01 | Added detailed user guide / onboarding section for easy start |
| 1.0.2   | 2026-05-01 | Added multi-language response support - responses now adapt to user's language (English/Chinese) |
| 1.0.1   | 2026-05-01 | Added Chinese name "格物本质赋能学习法" as display name, updated trigger keywords |
| 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 |

## Example: Learning Diffusion Models

### Step 1: Set Goals
- **Goal**: Understand and implement a basic diffusion model
- **Evaluation**: Can explain forward/reverse process and generate images
- **Time**: 2-3 weeks

### Step 2: First Principles
Diffusion essence = Gradual noise addition + Learned denoising
- Why add noise gradually?
- What does "learned denoising" mean?
- How does this connect to thermodynamics?

### Step 3: Taxonomy

Diffusion Models ├── Forward Process (noise scheduling) ├── Reverse Process (denoising network) ├── Training Objective (noise prediction) ├── Sampling (iterative denoising) └── Applications (image generation, inpainting)

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### Step 4: Recursive Questioning in Action

**Question Chain Example**:

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"

Q: "Why start from noise at all?" → A: "Noise is easy to sample; data is hard to model directly"

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

text


### Step 5: Multi-Perspective
- Paper: "Denoising Diffusion Probabilistic Models"
- Video: Lilian Weng's blog explanation
- Code: Hugging Face Diffusers library

### Step 6: Practice
- Implement simple 1D diffusion
- Use diffusers for 2D image generation
- Modify noise schedule and observe effects

### Step 7: Feedback
- Explain to colleague/peer
- Write blog post on understanding
- Compare with other generative models

### Step 8: Review
- Revisit aha moments weekly
- Connect to VAEs, GANs, flows
- Apply to new domains (audio, video)

### Step 9: Mind Map

Diffusion Models ├── Core Insight: "Learned unscrambling" ├── Forward: Data → Noise (easy) ├── Reverse: Noise → Data (learned) └── Aha: "Practice destroying to learn creating"

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RootCraft Learning System

Install

RootCraft Learning System (格物本质赋能学习法)

🎓 User Guide / 使用指南

中文引导 (Chinese Guide)

RootCraft Learning System

格物本质赋能学习法 (RootCraft Learning System)

格式要求

Level 1: 基础定义

Level 2: 原理追问

Level 3: 本质挖掘

Level 4: 边界测试

Level 5: 啊哈时刻

输出格式

Q1: [问题]

Q2: [问题]

Format Requirements

Level 1: Basic Definition

Level 2: Principle Inquiry

Level 3: Essence Mining

Level 4: Boundary Testing

Level 5: Aha Moment

Output Format

Q1: [Question]

Q2: [Question]

追问链：梯度下降 / Question Chain: Gradient Descent

Q1: 什么是梯度下降？ / What is gradient descent?

Q2: 为什么要用"梯度"？ / Why use "gradient"?

Q3: 为什么一定要"下降"？ / Why must it "descend"?

Q4: 如果不用梯度，还有什么方法？ / If not gradient, what else?

Q5: 梯度下降的本质是什么？ / What's the essence of gradient descent?

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