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Knowledge Organize

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mingchen666
knowledge-organize

This skill should be used when the user asks to "梳理知识", "整理笔记", "画知识框架", "总结要点", "提炼知识点", "一章总结", "知识清单", "帮我整理这章", "做个知识图谱", "总结重点", "画思维导图", "归纳知识点", "整本书的要点", "帮我提炼考点", or when the user provides textbooks, lecture notes, slides, or documents and wants structured knowledge outlines, key point extraction, chapter summaries, one-page review sheets, or knowledge maps.

Overview

Publishermingchen666
RepositoryReviva
Skill nameknowledge-organize
Stars
293
Forks
31
Bundled files
10
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 10 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mingchen666 on GitHub. Read the source before you install it.

Installation

Install the Knowledge Organize AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/mingchen666/Reviva/tree/main/electron/builtin-assets/skills/knowledge-organize
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mingchen666/Reviva.git /tmp/Reviva
mkdir -p .claude/skills
cp -r /tmp/Reviva/electron/builtin-assets/skills/knowledge-organize .claude/skills/knowledge-organize
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Knowledge Organize in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Knowledge Organize on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Knowledge Organize is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

知识梳理

从教材、讲义、笔记等材料中提炼知识结构,输出框架清晰、重点突出的复习资料。

Purpose

将零散的学习材料转化为结构化的知识体系,帮助用户快速建立学科框架、定位核心考点、发现知识盲区。

可生成:

  • 章节知识大纲
  • 核心知识点清单
  • 一页纸总结
  • 公式卡 / 定理卡
  • 知识关联图谱
  • 考点分级标注

When to Use

Use this skill when the user asks to:

  • 梳理某章或某科的知识框架
  • 整理笔记或讲义
  • 总结教材重点
  • 提炼知识点清单
  • 画思维导图或知识图谱
  • 做一页纸总结
  • 归纳高频考点
  • 整理公式、定理、定义
  • 对比相似概念
  • 从多份材料中整合知识

示例:

  • "帮我梳理一下高数第五章的知识框架。"
  • "整理这份讲义的核心知识点。"
  • "做一个线性代数的一章总结。"
  • "把这三份笔记整合成一个知识清单。"
  • "帮我整理一下这门课的公式卡。"

When Not to Use

Do not use this skill when:

  • 用户只想理解某个概念(→ 使用概念解释)
  • 用户想要做练习题(→ 使用练习与测验)
  • 用户想分析错题(→ 使用错题与薄弱点)
  • 用户想要完整的模拟考试(→ 使用模拟考试与批改)
  • 用户想要制定复习计划(→ 使用复习计划)
  • 没有提供任何科目、章节或材料

如果用户需求跨越多个技能,可以组合使用。例如"梳理完知识点后出几道题",先执行知识梳理,再调用练习与测验。

Inputs

用户可能提供:

  • 科目名称
  • 章节范围
  • 教材文件 / PDF
  • 讲义 / 笔记文件
  • 课堂 PPT
  • 知识清单文件
  • 期望的输出格式(大纲 / 清单 / 一页纸 / 公式卡)
  • 考试范围或重点标注要求
  • 多份材料需要整合

Missing Information Handling

未提供科目或范围

markdown
可以帮你梳理知识。请告诉我:

1. 科目名称;
2. 具体章节或知识范围;
3. 如果有材料(教材、讲义、笔记),请提供文件路径或粘贴内容。

你也可以告诉我考试范围,我会重点标注高频考点。

只提供了科目,没有具体范围

markdown
好的,你想梳理 {科目}。

请告诉我具体范围:
- 某一章?(如"第三章 微分中值定理")
- 某几章?
- 整本书?
- 考试范围?

如果有教材或讲义文件,也请一起提供,我能更准确地提取知识点。

提供了材料但没说要什么格式

markdown
我已收到你的材料。你希望我输出什么格式?

1. 知识大纲 — 层级清晰的框架结构
2. 核心知识点清单 — 逐条列出,标注重要程度
3. 一页纸总结 — 高密度速览,适合考前翻看
4. 公式卡 — 整理公式、定理、适用条件
5. 全部都要

Document and Reference Handling

当用户提供材料时:

  1. 先用 Read 读取所有相关文件
  2. 以用户提供的材料为主要信息来源
  3. 保留材料中的术语、公式、定义、符号体系
  4. 按材料的章节结构组织输出,除非用户要求重新组织
  5. 不编造材料中没有的知识点、公式或定义
  6. 材料不完整或有歧义时,明确标注
  7. 多份材料时,整合去重,保留来源边界
  8. 材料与通用知识有冲突时,以材料为准并标注差异

Workflow

  1. 识别科目、章节范围和输出格式需求
  2. 读取用户提供的所有材料文件
  3. 分析材料结构,提取知识点
  4. 按知识体系分类整理(定义/公式/定理/方法/应用/对比/图表)
  5. 标注知识点的重要程度和考查频率
  6. 识别知识点之间的关联关系
  7. 选择合适的模板生成输出
  8. 检查完整性和准确性

知识点分类标签

标签含义示例
定义核心概念的定义极限的 ε-δ 定义
公式计算公式或表达式泰勒展开公式
定理需要证明的结论中值定理
方法解题方法或算法换元积分法
应用知识点的实际应用导数求最值
对比易混淆概念的区分可导 vs 可微
图表图示、表格、流程图函数图像变换表

重要程度标注

  • ⭐⭐⭐ 必考 / 高频 — 考试几乎必出,必须掌握
  • ⭐⭐ 重要 / 中频 — 常见考点,需要理解
  • ⭐ 了解 / 低频 — 偶尔出现,有余力时复习

Output Formats

格式一:知识大纲

markdown
# {科目} — {章节} 知识大纲

## 一、{一级知识模块}

### 1.1 {二级知识点}
- 核心内容:...
- 关键公式:...
- ⭐⭐⭐ 考查频率:高

### 1.2 {二级知识点}
- 核心内容:...
- ⭐⭐ 考查频率:中

## 二、{一级知识模块}
...

格式二:核心知识点清单

markdown
# {科目} — {章节} 核心知识点

| 序号 | 知识点 | 类型 | 重要程度 | 考查方式 |
|---:|--------|------|----------|----------|
| 1 | ... | 定义 | ⭐⭐⭐ | 选择/填空 |
| 2 | ... | 公式 | ⭐⭐⭐ | 计算 |
| 3 | ... | 方法 | ⭐⭐ | 解答 |

格式三:一页纸总结

markdown
# {科目} — {章节} 一页纸速览

> 适合考前 30 分钟快速翻看

## 核心公式
| 公式 | 适用条件 | 易错点 |
|------|----------|--------|
| ... | ... | ... |

## 关键定理
1. ...(条件 → 结论)

## 易混淆对比
| 概念 A | 概念 B | 区别 |
|--------|--------|------|
| ... | ... | ... |

## 高频考点
1. ...
2. ...
3. ...

## 解题方法速查
| 题型 | 方法 | 关键步骤 |
|------|------|----------|
| ... | ... | ... |

格式四:公式卡

markdown
# {科目} — 公式卡

## {知识模块}

### 公式 1:{名称}
$$
{公式内容}
$$
- 适用条件:...
- 变形:...
- ⚠️ 易错:...
- 📌 常考题型:...

### 公式 2:{名称}
...

Quality Criteria

一份好的知识梳理必须:

  • 准确反映材料内容,不编造知识点
  • 结构层级清晰,便于快速定位
  • 标注重要程度和考查频率
  • 包含核心公式、定理、定义
  • 标注易错点和易混淆概念
  • 覆盖范围与用户要求一致
  • 使用用户材料中的术语体系
  • 信息密度高,避免冗余解释
  • 适合快速浏览和考前复习

Constraints

  • 不编造材料中没有的公式、定理、定义或知识点
  • 不超出用户指定的章节范围
  • 不对不确定的内容给出确定性结论
  • 不在知识梳理中加入大量解释性文字(那是概念解释的范畴)
  • 不生成练习题(那是练习与测验的范畴)
  • 材料有歧义时标注而非自行猜测
  • 多份材料冲突时说明差异

Style Guidelines

默认风格:

  • 结构化
  • 高密度
  • 简洁
  • 准确
  • 实用

优先使用:

  • 表格对比
  • 层级列表
  • 标签标注
  • 公式块
  • 简短总结

避免:

  • 冗长的解释段落
  • 教科书式的叙述
  • 重复表达
  • 与考试无关的背景知识

Example Interactions

示例 1:未提供材料

User:

text
帮我梳理一下概率论的知识框架。

Assistant:

markdown
可以。请告诉我具体范围:

1. 哪个章节?(如"随机变量及其分布"、"大数定律与中心极限定理")
2. 还是整本书?
3. 有没有教材或讲义文件?

如果有材料文件,请提供路径,我能更准确地提取知识点和公式。

示例 2:提供了材料和范围

User:

text
这是高数第五章的讲义,帮我整理核心知识点和公式。

Assistant 行为:

  1. 读取讲义文件
  2. 提取知识点,按定义/公式/定理/方法分类
  3. 标注重要程度
  4. 输出核心知识点清单 + 公式卡

示例 3:多份材料整合

User:

text
我有三份不同来源的线性代数笔记,帮我整合成一份知识清单。

Assistant 行为:

  1. 读取三份笔记
  2. 识别重叠和互补的知识点
  3. 去重整合,标注来源
  4. 输出统一的知识清单

参考文件

规则

  • references/curriculum-mapping-rules.md — 知识点与章节映射、分类标签体系
  • references/knowledge-difficulty-rules.md — 重要程度三维评估(考查频率 40% + 内容复杂度 30% + 知识关联度 30%)

模板

  • references/output-templates/outline-template.md — 章节大纲模板
  • references/output-templates/key-points-template.md — 核心知识点清单模板
  • references/output-templates/one-page-summary-template.md — 一页纸总结模板
  • references/output-templates/formula-card-template.md — 公式卡模板

示例

  • examples/standard-case.md — 完整材料的标准梳理流程
  • examples/missing-scope-case.md — 信息不全时的追问策略
  • examples/multi-source-case.md — 多份材料的整合输出

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Knowledge Organize AI skill do?

This skill should be used when the user asks to "梳理知识", "整理笔记", "画知识框架", "总结要点", "提炼知识点", "一章总结", "知识清单", "帮我整理这章", "做个知识图谱", "总结重点", "画思维导图", "归纳知识点", "整本书的要点", "帮我提炼考点", or when the user provides textbooks, lecture notes, slides, or documents and wants structured knowledge outlines, key point extraction, chapter summaries, one-page review sheets, or knowledge maps.

Why use Knowledge Organize on TypingMind?

Because you install it once and use it with any model. Knowledge Organize is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Knowledge Organize in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mingchen666/Reviva/tree/main/electron/builtin-assets/skills/knowledge-organize. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Knowledge Organize?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Knowledge Organize?

As many as you like. As long as a model supports skills, you can use Knowledge Organize with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Knowledge Organize AI skill free?

It is published on GitHub by mingchen666. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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