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Learning To Learn

OrganizationPopular
THU-MAIC
learning-to-learn

Infuse learning strategies and metacognition into a concept-centered OpenMAIC classroom as a parallel goal. Use when the user wants students to learn how to learn while studying a subject: retrieval practice, self-explanation, prediction before feedback, monitoring understanding, deliberate review, or productive failure. Do not use when learning science or study skills should be the standalone subject of the lesson.

Overview

PublisherTHU-MAIC
RepositoryOpenMAIC
Skill namelearning-to-learn
Stars
37.6K
Forks
5.9K
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 THU-MAIC on GitHub. Read the source before you install it.

Installation

Install the Learning To Learn 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.
  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/THU-MAIC/OpenMAIC.git /tmp/OpenMAIC
mkdir -p .claude/skills
cp -r /tmp/OpenMAIC/skills/agent-runtime/learning-to-learn .claude/skills/learning-to-learn
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Learning To Learn 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 Learning To Learn 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 Learning To Learn 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.

学会学习(Learning to Learn)

把学习策略与元认知作为平行目标嵌入概念主课:不替换学科概念主线,而是改变学生如何经历、检查和巩固这些概念。

stage-design 仍然约束课堂的创建和持久化流程。如果同时使用 /understanding-by-design,先确定大概念、基本问题与表现性任务,再嵌入学习策略;如果同时使用 /social-emotional-learning,让两类平行目标服务同一概念任务,不要各自另起一条课程主线。

先声明平行目标

在页面计划前分别声明:

  1. 概念目标:学生最终要理解、解释或迁移什么;
  2. 学会学习目标:学生要练习哪一种学习动作,以及什么行为能证明它发生了。

每一个学习策略都要能回答“它在服务哪个概念理解”;答不出的嵌入删除。优先选择最贴合任务的 1–2 种策略,不要在一节课里罗列整套学习科学术语。

两条内容红线

  1. 规划标签不进页面学习缝元认知教学意图平行目标 等内部框架词只用于规划与 brief,不作为学生可见的标题、正文或栏目标签。
  2. 角色台词只走旁白:老师与学生代理的口述、示范和讨论发言放进 narration / actions;静态页面只承载概念要点、问题、任务与提示,不写“老师说”或“学生说”。

页面使用学生能立即执行的动作语言,而不是技术术语:

  • 检索练习:写成“别急着翻,先把答案写出来”;
  • 自我解释:写成“用自己的话给结论一个理由”;
  • 先预测后验证:写成“先猜一下,再看对不对”;
  • 间隔复习:写成“过几天再回来默一次”;
  • 监控理解:写成“你是真的懂,还是只是感觉懂了?”

只有当用户明确要求讲授某种学习方法本身时,才把对应术语作为学生要学的内容。

四个嵌入点

这些是页面规划锚点,不是页面标题,也不要求各自新增一页:

  1. 开场:正式讲解前,让学生先调取已有知识、做出初步判断或写下预测。
  2. 概念建立:得出结论后,让学生用自己的话解释一次,并追问“为什么”。学生代理可以示范不完整解释,再邀请学习者补充或质疑。
  3. 亲手做:在 interactive 中先预测或作答,再给反馈;错误要进入检查、补漏和重试路径,不能成为死路。
  4. 收束:同时收住“我理解了什么”“我是怎样学会的”“哪里还不确定”以及“之后如何再检索一次”。

多智能体的作用

  • 老师:示范先想再答、检查理解与定位缺口;认可“我卡住了”,再引导补漏。
  • 学生代理:呈现“感觉懂了但默不出来”、预测错误或解释不完整的真实状态,并邀请学习者判断、修正和迁移。

围绕方法和理解讨论,不评价人;老师及时把对话带回概念主线。

质量关口

  • 概念目标与学会学习目标分开声明。
  • 每个嵌入点都标明它服务的概念与可观察的学习动作。
  • 至少让学生实际经历一次主动回忆、自我解释或先预测后验证,而不是只听学习方法介绍。
  • 受挫与错误后保留反馈、补漏和重试路径。
  • 收束同时包含概念理解、学习过程反思与后续复习动作。
  • 逐页检查所有静态文字,不得出现内部规划标签或角色口述台词。
  • 不承诺“万能学习法”;需要比较研究证据时先核实来源。

边界

本 Skill 用于把学习策略嵌入学科概念课,不用于以学习方法或学习科学本身为内容主线的独立课程。若用户要把学习法做成第二条完整课程主线,先用 ask_user 确认范围。

Frequently asked questions

What does the Learning To Learn AI skill do?

Infuse learning strategies and metacognition into a concept-centered OpenMAIC classroom as a parallel goal. Use when the user wants students to learn how to learn while studying a subject: retrieval practice, self-explanation, prediction before feedback, monitoring understanding, deliberate review, or productive failure. Do not use when learning science or study skills should be the standalone subject of the lesson.

Why use Learning To Learn on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/THU-MAIC/OpenMAIC/tree/main/skills/agent-runtime/learning-to-learn. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Learning To Learn?

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 Learning To Learn?

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

Is the Learning To Learn AI skill free?

Yes. It is published on GitHub by THU-MAIC 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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