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Spiral Curriculum

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THU-MAIC
spiral-curriculum

Design a multi-stage OpenMAIC course series around a small concept spine whose ideas return repeatedly at higher levels of complexity, abstraction, relationship density, representation, transfer distance, or boundary awareness. Use when the user explicitly wants a Bruner-style spiral curriculum, progressive conceptual revisits, or a series organized by how understanding develops rather than by a linear topic list. Do not use for a single standalone stage or a series that only needs sequential coverage.

Overview

PublisherTHU-MAIC
RepositoryOpenMAIC
Skill namespiral-curriculum
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 Spiral Curriculum 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/spiral-curriculum .claude/skills/spiral-curriculum
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Spiral Curriculum 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 Spiral Curriculum 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 Spiral Curriculum 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.

螺旋式课程设计

设计一组系列课,让少数核心概念在整个系列中反复回来,并且每次都发生结构性升阶。最小设计对象是“概念脊柱 + 每个概念的遭遇史”,不是某一节课的目录。

先加载 /stage-design 获取单节课堂的创建基线,并加载 /curriculum-planner 获取多课堂文件夹、跨课堂读取和批量交付规则。本 Skill 在它们之上增加螺旋架构、升阶操作符和假螺旋检查。

适用范围

用于以下请求:

  • 用布鲁纳螺旋式课程设计一个单元或系列课;
  • 让同一批核心概念跨课重访并逐步深化;
  • 以学习者理解如何发展为主线,而不是按教材章节线性排课。

不要用于:

  • 单节独立课,此时使用 /stage-design 和相应主题 Skill;
  • 只要求按顺序覆盖内容、没有概念升阶重访的系列,此时使用 /curriculum-planner
  • 风格复制或 PPT 直接导入,此时使用 /style-clone/pptx-import

第一原则:Revisit 不等于 Review

“第 1 课讲概念、第 3 课复习概念、第 6 课再复习”不是螺旋。每次重访必须明确回答:这一次比上一次多了什么?

每次重访至少在以下一个维度升级:

维度初次接触后续重访
复杂度单一关系多变量相互作用
抽象度具体案例一般模型
关系密度孤立概念与更多概念连接
表征方式直观经验图示、模型、符号
迁移距离熟悉情境陌生情境、跨领域
边界与反例正例反例、边界、局限

如果一次回来没有在任何维度更高,就只是重复,必须重写。

页面内容红线

Concept Spineencountergrowth operatorSpiral Contractrevisit概念脊柱遭遇史●/▲/◆ 等属于教师侧规划语言。它们可以出现在对话、架构和页面 brief 中,不得出现在学生可见的标题、标签或正文。

“本次比上次多了什么”写进 brief,不写成学生页面上的“复杂度升级”标签。老师与代理的口述走 narration / actions,不写成“老师说”“学生说”的静态正文。

概念脊柱

不要先排课时清单。先回答:整个系列结束后,学生真正应该建立哪几个能够反复使用和迁移的核心观念?

  • 每个核心概念写成一句可迁移的理解,而不是名词;
  • 一个系列通常选择 3–6 个核心概念,避免过多导致每课浅尝辄止;
  • 每个概念同时声明初始理解目标和最终理解目标,作为 Learner Progression 的两个端点;
  • 可结合 /understanding-by-design 确定大概念、基本问题和表现性证据,再用本 Skill 安排跨课升阶重访。

Spiral Map

为每个核心概念维护跨课遭遇史:直觉经验 → 机制解释 → 多变量关系 → 形式化模型 → 陌生情境迁移 → 与其他概念整合。

教师侧 Spiral Map 可以使用:

  • :首次接触;
  • :深化;
  • :综合与迁移。

每个标记旁边还必须写明本次新增结构。只有符号、没有升阶说明的表格没有价值。

规划一次重访

对每个 concept + previous encounter + target growth 决定四件事:

  1. 保留什么:哪些已有理解不需要重新教学;
  2. 增加什么:本轮新增的内容或条件;
  3. 重组什么:哪些孤立知识需要组成机制或关系;
  4. 迁移到哪里:用哪个新情境检验概念结构。

升阶操作符

  • ADD_COMPLEXITY:增加变量或相互作用;
  • ADD_RELATION:让当前概念与另一个概念形成必要关系;
  • ABSTRACT:从案例上升到一般模型;
  • FORMALIZE:引入符号、模型或专业语言;
  • CHANGE_REPRESENTATION:在操作、图像模型和符号表征之间重构概念,不机械套成固定课次顺序;
  • INCREASE_TRANSFER_DISTANCE:逐渐进入更陌生或跨领域的情境;
  • ADD_EXCEPTION:加入反例、边界、约束或局限。

一次重访通常选择 1 个主操作符和少量辅助操作符。不要一次叠满所有操作符,那只会堆难度。

概念记忆

螺旋依赖跨 stage 的共享记忆。平台不会替本 Skill 自动维护概念模型,所以要在当前对话中保存教师侧运行记录,至少包含:

  • concept id;
  • encounter history;
  • representation history;
  • complexity level;
  • known relations;
  • misconceptions detected;
  • examples used;
  • transfer distance;
  • mastery evidence;
  • next revisit target。

开始下一课前,使用 read_stage_outline 回读前面课堂的持久化页面列表;需要核对真实内容时继续用 read_stage。不要只相信原计划,要依据已经落进课堂的内容调整本轮“保留、增加、重组、迁移”。

每课的 Spiral Contract

每个 stage 在教师侧规划中必须有一份 contract:

  • returning_concepts:哪些旧概念回来;
  • new_concepts:哪些概念第一次出现;
  • added_complexity:比之前复杂或抽象在哪里;
  • new_relation:新增了什么概念关系;
  • representation_shift:是否更换表征;
  • transfer_target:进入哪个新情境;
  • future_hook:故意留下什么,供后续重访。

future_hook 允许 productive incompleteness:早期先建立可用但不完整的模型,后续再重组,不要求每课把概念彻底讲完。

两层设计

第一层:Spiral Architecture

先在对话中产出并请用户确认:

  1. Concept Spine:每个核心概念的初始与最终理解目标;
  2. Course Timeline:各课的主题和任务;
  3. Concept Spiral Map:每个概念何时出现、何时回来、每次增加什么;
  4. Learner Progression:学生理解应怎样逐课变化;
  5. 每课的 Spiral Contract 和概念交叉点。

使用 ask_user 让用户能调整课时数、概念出现时机、深度与交叉关系。架构未确认前不要创建课堂。

第二层:Generate Lessons

架构确认后,默认把整个已批准系列持续建到完成,不在每课之间重复停下确认,除非用户明确要求分批验收:

  1. create_folder 创建系列文件夹;
  2. 每课调用 create_stage,传入该 folderId
  3. 每课先 set_roster,再按批准计划逐页 generate_scene
  4. 需要补写旁白时逐页 generate_actions,修改旁白后补 generate_tts
  5. 使用 list_scenesread_stage_outlineread_stage 验收持久化结果;
  6. 开始后续课堂前更新概念记忆。

架构本身只留在教师侧规划中,不生成成学生页面。

假螺旋检查

整套系列完成后逐概念检查:

  • 同一概念多次出现但复杂度没有提升:Repeated, not spiraled;
  • 后面只增加术语,没有增加结构理解:Vocabulary inflation;
  • 每课都是新内容,旧概念不回来:Linear curriculum;
  • 所谓回顾只发生在单元最后:Review curriculum;
  • 概念回来时情境和表征完全相同:Context repetition;
  • 后面只是题目更难,不是概念更深:Difficulty escalation without conceptual deepening。

命中任一项,回到对应 Spiral Contract 修改页面 brief 或课程安排,再重新验收。

完成标准

  • 每个概念都有初始理解目标、最终理解目标和完整遭遇史;
  • 每次重访都标明主升阶操作符及新增结构;
  • 每课都有七字段 Spiral Contract;
  • 架构先确认,确认后整个系列被持续持久化到文件夹中;
  • 每课实际内容已回读,概念记忆不是只依赖原计划;
  • 学生可见文字不包含螺旋元数据、规划黑话或角色台词;
  • 每课仍满足 /stage-design 的页面、roster、持久化与音频完成标准;
  • 系列通过假螺旋检查。

与其他教学法的关系

  • /understanding-by-design 可先确定大概念、基本问题和表现性评估,本 Skill 负责让这些概念跨课升阶重访;
  • /feynman-learning 在一节课堂内推动解释反复外化和重建,本 Skill 在整个系列中安排概念反复回来;
  • /learning-to-learn/social-emotional-learning 是平行目标,只有服务当前概念重访时才嵌入,不另起课程主线。

Frequently asked questions

What does the Spiral Curriculum AI skill do?

Design a multi-stage OpenMAIC course series around a small concept spine whose ideas return repeatedly at higher levels of complexity, abstraction, relationship density, representation, transfer distance, or boundary awareness. Use when the user explicitly wants a Bruner-style spiral curriculum, progressive conceptual revisits, or a series organized by how understanding develops rather than by a linear topic list. Do not use for a single standalone stage or a series that only needs sequential coverage.

Why use Spiral Curriculum on TypingMind?

Because you install it once and use it with any model. Spiral Curriculum 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 Spiral Curriculum in TypingMind?

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

Which AI models can use Spiral Curriculum?

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 Spiral Curriculum?

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

Is the Spiral Curriculum 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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