Install
openclaw skills install rootcraft-learning-systemRootCraft Learning System
RootCraft Learning System - An integrated learning methodology combining First Principles Thinking, Taxonomy-Based Classification, Feynman Technique, and Rec...
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)
🎓 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）
🎯 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：
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：\n
python\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：
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：
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：
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
### 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:
🎓 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:
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
---
## 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
### 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]
**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]
### 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"
### 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]
#### 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]
### 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
### 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
**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)
### 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!"
### 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"