What makes RootCraft Learning System notable?

994 downloads. Trust evidence available

How should RootCraft Learning System be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

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RootCraft Learning System

RootCraft Learning System - An integrated learning methodology combining First Principles Thinking, Taxonomy-Based Classification, Feynman Technique, and Rec... Skill: RootCraft Learning System Owner: zhangboheng Summary: RootCraft Learning System - An integrated learning methodology combining First Principles Thinking, Taxonomy-Based Classification, Feynman Technique, and Rec... Tags: anki:1.1.0, education:1.1.0, first-principles:1.1.0, latest:1.1.5, learning:1.1.0, memory:1.1.0, study:1.1.0 Version history: v1.1.5 | 2026-05-08T06:46:03.769Z | user Added quality scorer (4-d

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

994

Updated

Oct 11, 2026

Version

1.1.5

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 994 downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 994 downloadsadoption · observed Oct 11, 2026
Latest release: 1.1.5release · observed May 8, 2026
Handshake status: UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

Release 1.1.5release
Added quality scorer (4-dimension) + recursive question generator (domain-specific)
Observed May 8, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s17aecn58hk3xdbx0aekn3xq3d85k4d7:rootcraft-learning-system

Node.js workspace detected. Install dependencies securely: run `npm ci --ignore-scripts` to prevent post-install lifecycle triggers from running arbitrary code, then selectively audit the dependency tree.
Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-zhangboheng-rootcraft-learning-system/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

146,869 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: rootcraft-learning-system
version: 1.1.5
author: 张权 (Zhang Quan)
author_website: https://www.luckydesigner.space
author_brand: Luckydesigner（行运设计师）
author_pen_name: 伯衡君
name_display: 格物本质赋能学习法
description: RootCraft Learning System - An integrated learning methodology combining First Principles Thinking, Taxonomy-Based Classification, Feynman Technique, and Recursive Questioning for deep knowledge mastery and "aha moment" discovery. Includes Anki memory cards generation based on Ebbinghaus forgetting curve. Chinese name: 格物本质赋能学习法.
trigger_keywords: learning method, first principles, taxonomy classification, feynman technique, recursive questioning, aha moment, efficient learning, study system, knowledge mastery, how to learn, learn about, 格物致知, 学习方法, 啊哈时刻, 如何学习, 想学习, 帮我学习, 我想学习, 学习教程, 教学, 培训
---

# 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 ---

1. 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）

2. 第一性原理
（解释该主题的本质是什么）

3. 分类学拆解（MECE）
（系统化拆解该主题的知识体系）

4. 费曼 + 递归追问
（核心概念检验 + 追问链）

5. 多视角学习
（从数学、工程、应用等角度分析）

6. 实践应用
（给出一个最小可运行的代码示例）

7. 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
```

8. 持续复习
（艾宾浩斯遗忘曲线复习计划）

9. 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**：\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

README.md

# RootCraft Learning System

<div align="center">

**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)

</div>

---

## 📖 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

---

## 🎯 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

---

## 🚀 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.

---

## 📚 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

---

## 💡 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!"
```

---

## 📁 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-a

_meta.json

{
  "ownerId": "kn7bj8mtcw6j7s73kyz9zhck6985kv85",
  "slug": "rootcraft-learning-system",
  "version": "1.1.5",
  "publishedAt": 1778222763769
}

skill-card.md

## Description:

RootCraft Learning System helps agents guide learners through a bilingual 9-step study workflow using first principles, taxonomy-based classification, the Feynman Technique, recursive questioning, spaced review, exam generation, and Anki-style memory cards.

This skill is ready for commercial/non-commercial use.

## Publisher:

[zhangboheng](https://clawhub.ai/user/zhangboheng)

### License/Terms of Use:

MIT-0

## Use Case:

Learners, educators, and agents use this skill to turn a study topic into structured learning goals, first-principles explanations, recursive question chains, practice tasks, review schedules, exam questions, and flashcards.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Generated study materials and flashcard contents may be retained locally in OpenClaw workspace directories.

Mitigation: Avoid sensitive study topics unless local retention is acceptable, and review or remove saved files after use.

Risk: Generated practice commands or code examples may be incorrect or unsuitable for a user's environment.

Mitigation: Review generated commands and examples before running them, especially when they install packages or write files.

## Reference(s):

- [RootCraft Learning System on ClawHub](https://clawhub.ai/zhangboheng/skills/rootcraft-learning-system)
- [Luckydesigner author website](https://www.luckydesigner.space)

## Skill Output:

**Output Type(s):** [Guidance, Markdown, Code, Shell commands, Files, Configuration instructions]

**Output Format:** [Markdown guidance with Python and shell snippets plus locally saved Markdown, CSV, and optional Anki package files]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May save generated study notes, exam papers, review schedules, and flashcard contents under local OpenClaw workspace directories.]

## Skill Version(s):

1.1.5 (source: SKILL.md frontmatter and server release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

package.json

{
  "name": "rootcraft-learning-system",
  "version": "1.0.0",
  "description": "RootCraft Learning System - An integrated learning methodology combining First Principles Thinking, Taxonomy-Based Classification, Feynman Technique, and Recursive Questioning for deep knowledge mastery and \"aha moment\" discovery.",
  "author": "zhangquan",
  "license": "MIT",
  "keywords": [
    "learning",
    "first-principles",
    "feynman-technique",
    "recursive-questioning",
    "knowledge-mastery",
    "study-method"
  ],
  "repository": {
    "type": "git",
    "url": "https://github.com/zhangquan/rootcraft-learning-system"
  },
  "files": [
    "SKILL.md"
  ],
  "main": "SKILL.md"
}

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