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Li-Evan
learn-graph

当用户要系统学一个新领域、不知道从哪入手、或担心「学得不够系统」时使用。用「知识图谱学习法」和用户一起构建该领域的概念/用途/父子节点图谱(自己建图的过程本身就是学习),标出复用价值最高的节点和「从常识就能入门的点」,给出有效学习路径并回答「学到哪算够」。触发场景:系统学 X 领域、从哪开始学、学得不系统、想要 X 的全貌、规划学习路径、这个领域有多大。

Overview

PublisherLi-Evan
RepositoryBloom
Skill namelearn-graph
Stars
292
Forks
43
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Li-Evan on GitHub. Read the source before you install it.

Installation

Install the Learn Graph 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/Li-Evan/Bloom/tree/main/skills/learn-graph
  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/Li-Evan/Bloom.git /tmp/Bloom
mkdir -p .claude/skills
cp -r /tmp/Bloom/skills/learn-graph .claude/skills/learn-graph
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Learn Graph 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 Learn Graph 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 Learn Graph 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.

知识图谱学习法(learn-graph)

核心信条:自己一步步建图谱的过程,本身就是最有效的学习——不要直接套用别人给的图谱。 绝大部分知识,都有一个从常识就能入门的点。

何时用

用户要系统进入一个新领域,或焦虑"学得不够系统 / 不知何时算够"。

流程(关键:和用户一起建,不是直接灌一张完整图)

第一步:锁定目标领域 X 和目的

用户为什么学 X?(接 learn-occam 的"既定问题")目的决定图谱画到多细。

第二步:构建图谱——只抓三件事

概念/名称 · 用途 · 上下文关系(父子节点):

  • 子节点 = X 依托 / 基于什么;父节点 = X 服务于什么目标。
  • 以提问引导用户一起填(自己建图才学得到),别一次性灌完。先给骨架,留节点让他补。

第三步:标注两个关键

  • 复用价值:哪些节点父节点多(像 Python)→ 优先学,回报最高。
  • 入门点:哪个节点"从常识就能入门"→ 学习路径的起点。

第四步:输出学习路径 + 颗粒度

从入门点出发、沿父子关系排一条有效路径。颗粒度按需自由切换(领域图 → 细分学科图)。"学到哪算够"= 覆盖到能解决第一步那个目的的节点即可,不必学满。

第五步:交接

  • 拿不准某节点是不是缺前置知识 → 这正是图谱的强项,已在图上标出。
  • 找到入门点要动手 → 转 learn-prototype(在图上找"最垃圾原型"的起点)。

注意

⚠️ 铁律·只用确证的已会知识:判断用户「已经会什么」只能用他确证学过的知识(亲口确认或可靠背景);严禁把「正在讲的材料 / 文章作者背景 / 对话里别人的知识」当成用户会的。拿不准 → 直接问「⚠️ 你学过 ___ 吗?」,绝不替他假设。

  • 强调"自己建":多用提问让用户参与,别炫一张完美的图。
  • 同族 skill:learn-occam(该不该学) learn-crossover(已会什么) learn-prototype(动手) learn-feynman(自查)。

Frequently asked questions

What does the Learn Graph AI skill do?

当用户要系统学一个新领域、不知道从哪入手、或担心「学得不够系统」时使用。用「知识图谱学习法」和用户一起构建该领域的概念/用途/父子节点图谱(自己建图的过程本身就是学习),标出复用价值最高的节点和「从常识就能入门的点」,给出有效学习路径并回答「学到哪算够」。触发场景:系统学 X 领域、从哪开始学、学得不系统、想要 X 的全貌、规划学习路径、这个领域有多大。

Why use Learn Graph on TypingMind?

Because you install it once and use it with any model. Learn Graph 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 Learn Graph in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Li-Evan/Bloom/tree/main/skills/learn-graph. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Learn Graph?

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 Learn Graph?

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

Is the Learn Graph AI skill free?

Yes. It is published on GitHub by Li-Evan under the MIT license. 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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