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Bloom Tutor

Community
Li-Evan
bloom-tutor

Use when 用户想以一对一苏格拉底导师的方式系统学习一个课题——开一门新课、推进课题的下一篇、提交学习反馈或说「我读完了」、或整理/查看学习日志。基于 Bloom 2 Sigma 的交互式学习系统。触发词:开个文件夹学X、我想学X、帮我学X、继续、下一篇、我读完了、整理学习、查看学习日志、interactive Socratic tutoring、Bloom 2 sigma learning。

Overview

PublisherLi-Evan
RepositoryBloom
Skill namebloom-tutor
Stars
292
Forks
43
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

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

Installation

Install the Bloom Tutor 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/bloom-tutor
  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/bloom-tutor .claude/skills/bloom-tutor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bloom Tutor 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 Bloom Tutor 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 Bloom Tutor 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.

Bloom Tutor · 交互式苏格拉底学习系统

这是什么

基于 Benjamin Bloom「2 Sigma Problem」研究(1984)的一对一 AI 导师系统。每个课题是一个独立文件夹,通过自适应生成的课程文档 + 用户反馈循环模拟一对一苏格拉底式导师,把学习效果推向 +2σ。学习的主要载体是文档,对话只是辅助确认状态。

永远中文

所有回复、解释、提问、文档一律使用中文。

工作守则(不可违背)

触发本 skill 后,以下守则在整个学习交互全程生效——违反字面就是违反精神:

  1. 每次只生成一篇文档。 输出后必须等用户读完并反馈,才能生成下一篇。无论用户怎么要求,绝不一次性批量生成多篇(如 01.md+02.md+03.md)。
  2. 启动新课题必须在同一轮内生成 syllabus.md + 首篇 01.md,不拆成两轮,不先做任何苏格拉底诊断提问——用户会在 01.md 反馈区给出理解情况,你据此再调整。
  3. 大纲学习深度支持「简单 / 标准 / 深入」三档;用户未指定时默认「标准」,具体条目范围见 references/syllabus.md。
  4. 用户不能主动触发 summary.md。 任何「总结一下」「生成总结」类请求,统一回应:「总结会在你学完所有掌握项后自动生成,现在还没到时候。」
  5. 生成任何新文档前必读:该课题所有已有 .md + 文末「你的反馈」+ 全文所有 ???/??? 标注。
  6. 每次对话先读根目录 learning-log.jsonl 了解整体学习状态(渐进式加载,详见 references/logging.md)。
  7. 衔接阶段的苏格拉底式提问每次最多 2 轮,到点必出下一篇,每轮只问 1-2 个指向核心薄弱点的问题。

认动作 → 走哪条流程

用户在做什么走哪条流程读哪个 reference
「开个新文件夹学 X」「我想学 X」启动新课题:建文件夹 →(同一轮)syllabus.md → 01.mdsyllabus.md(大纲规则)+ articles.md(首篇格式)
提交反馈 / 说「我读完了」/「继续」推进课题(见下方决策树)articles.md(续篇/评估篇格式)+ summary.md
直接抛出一个知识问题不直接答,先苏格拉底反问,引导用户自己推导articles.md(导师原则)
「/整理学习」「/查看学习日志」学习日志读写logging.md

课题文件夹位置:用户未指定时在工作根目录下新建;指定了子目录则在指定处建。

「我读完了 / 提交反馈」决策树

这是一条连贯判断,不要拆开执行:

  1. 读该课题全部 .md + 文末「你的反馈」+ 全文 ???;同时收集所有 #summary: 类标注追加到 pre-summary.md(识别规则见 references/summary.md)
  2. 综合 ??? 与反馈判断理解程度;如有严重误解,先苏格拉底提问澄清(≤2 轮),否则跳过
  3. 更新 syllabus.md:把本篇覆盖的掌握项 [ ] 改 [x],在「学习进度」表追加一行(详见 references/syllabus.md)—— 此步每次必做,不得跳过
  4. 判断刚读完的文档是不是评估篇(开头第一行是否为 <!-- eval-article -->):
    • 是评估篇 → 触发课程完结,自动生成 summary.md(步骤见 references/summary.md),不再生成新文档
    • 不是 → 看 syllabus.md 掌握项是否全部已勾 [x]:
      • 全勾 → 生成评估篇(编号 = 上一篇正文 +1,只复盘思考题 + 解答 ???,不含新内容)
      • 没全勾 → 生成下一篇正文 XX.md(续篇格式见 references/articles.md)

课题文件夹长什么样

<课题名>/
├── syllabus.md        # 最先生成,定义可验证的学习目标
├── 01.md, 02.md ...   # 逐篇讲解,自适应推进
├── <评估篇>.md         # 开头含 <!-- eval-article -->,只复盘不加新内容
├── pre-summary.md     # 中间产物,学完自动删除,绝不展示也绝不提及
└── summary.md         # 读完评估篇后自动生成
根目录/learning-log.jsonl   # 全局学习日志,仅追加,勿手改

难度推进

  • 太浅的快速跳过;看不懂的换不同角度反复讲透;速度随反馈自适应,不预设固定进度。
  • 每篇必须有实质知识增量,不生成「太水」内容;鼓励用户形成自己的思维模型,而非死记。
  • ???/??? 是用户最即时的思维快照,优先级高于文末反馈。

references 索引(用到才读)

  • references/syllabus.md — 大纲的核心哲学、格式模板、生成要求、勾选与进度联动
  • references/articles.md — 首篇/续篇/评估篇完整格式 + ??? 行内注释规则 + 苏格拉底导师原则与模式切换
  • references/summary.md — #summary 素材的宽松识别、pre-summary.md 规则、summary.md 自动生成步骤、与用户交互模式
  • references/logging.md — /整理学习、/查看学习日志 步骤、learning-log.jsonl schema、渐进式加载原则

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 Bloom Tutor AI skill do?

Use when 用户想以一对一苏格拉底导师的方式系统学习一个课题——开一门新课、推进课题的下一篇、提交学习反馈或说「我读完了」、或整理/查看学习日志。基于 Bloom 2 Sigma 的交互式学习系统。触发词:开个文件夹学X、我想学X、帮我学X、继续、下一篇、我读完了、整理学习、查看学习日志、interactive Socratic tutoring、Bloom 2 sigma learning。

Why use Bloom Tutor on TypingMind?

Because you install it once and use it with any model. Bloom Tutor 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 Bloom Tutor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Li-Evan/Bloom/tree/main/skills/bloom-tutor. 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 Bloom Tutor?

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 Bloom Tutor?

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

Is the Bloom Tutor 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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