Modular Learning Orchestrator

SKILL教育学习
教育学习 未标注权限 许可证不明确

基于 L1 学习者画像、L2 知识图谱与 L3 对话连续性编排模块化学习。先建立领域地图和可独立迁移的知识单元,再根据学习者表现、兴趣与动力选择直接讲解、问题分支、情境应用或横向拓展;适用于持续学习、碎片化提问和微信/飞书短对话,不用于一次性事实问答。

一句话选型结论

按资料字段整理

Modular Learning Orchestrator是教育学习类的 Agent Skill,安装方式:GitHub 仓库,权限级别未标注,许可证不明确,没有现成安装命令,需按说明手动放置 SKILL.md。技能本质是给模型读的说明书,效果取决于模型与客户端;装完先用一个小任务验证,再放进正式流程。

分类教育学习 安装方式GitHub 仓库 权限级别未标注权限 许可证许可证不明确
累计安装 未标注 本周 未标注
GitHub 星标 未标注 仓库 inno-agent-hub
下载量 未标注 技能市场下载数
权限级别 未标注权限 安装前请确认风险范围
许可证 许可证不明确 未说明

安装

GitHub 仓库
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
  1. 1
    确认客户端支持

    确认你的客户端支持 Skills(Claude、Codex、Cursor 等),并升级到较新的版本。

  2. 2
    拿到技能文件

    按安装说明或仓库 README,把 SKILL.md 放到客户端的 skills 目录(一般一个技能一个文件夹)。

  3. 3
    刷新技能列表

    重新打开客户端,或刷新技能列表,让新放进来的技能被加载。

  4. 4
    验证是否生效

    问一句「你有哪些技能」,确认列表里出现它,再拿一个小任务试跑。

技能档案

教育学习
字段来自技能仓库与公开索引,中文说明由本站标注
作者 / 维护方chloris-blaxk仓库 inno-agent-hub · 版本 未标注
宿主客户端未标注技能可被这些客户端加载
安装方式GitHub 仓库安装包 chloris-blaxk/inno-agent-hub
权限级别未标注权限安装前请确认风险范围
许可证未说明许可证不明确,商用前请自行确认条款
仓库可用性available技能仓库当前是否可访问
最近核验未标注上游最后核验时间 · 数据更新 2026-09-08
正文体量8.8 KBSKILL.md 原文大小,越大说明说明书写得越细

典型使用场景

3 类
按分类整理的常见用法,具体以 SKILL.md 为准
讲解知识点

按水平把知识点讲清楚。

出练习题

按章节生成练习与答案。

制定学习计划

按目标和时间排学习节奏。

选择建议

装之前先看这几条
按条目字段给出的判断,不替你决定用不用

值得选的地方

  • 基础信息齐全,可以先按说明装一个试试。

先想清楚的地方

  • 没有标注权限级别,实际能做什么要装好后确认。
  • 许可证不明确,商用或二次分发前先到仓库确认条款。
  • 没有现成安装命令,要按安装说明或仓库 README 手动放置。
  • 没有做站内核验,内容以作者仓库为准。
  • 技能只是说明书,实际效果取决于模型与客户端,装完先用小任务试一遍。

更适合

  • 需要在教育学习场景里给模型补一套做法的人。
  • 愿意先读一遍 SKILL.md、确认它会让模型做什么的人。

不太适合

  • 对许可证要求很严、必须明确可商用的人。
  • 只想装完就自动变强、不打算看说明书的人。

同类对比

6 个同类
同分类里挑最接近的几条,字段以各自详情页为准
技能权限级别安装方式累计安装宿主客户端安装命令
Modular Learning Orchestrator当前 未标注权限 GitHub 仓库 未标注 未标注 无
食谱创建课堂课程 未标注权限 GitHub 仓库 25.5 万 Codex、Claude、Cursor 有
语言学习 联网访问 ClawHub 市场 23.6 万 OpenClaw 有
learning 效率 只读 ClawHub 市场 19.2 万 OpenClaw 有
学习习惯 未标注权限 ClawHub 市场 10.2 万 OpenClaw 有
日语家教 只读 ClawHub 市场 8.2 万 OpenClaw 有

SKILL.md 全文

8.8 KB
模型实际读到的说明书原文,来自技能仓库

Modular Learning Orchestrator

Purpose

Organize learning as independently understandable and reusable knowledge blocks. A context is an optional test environment for applying a block; it is never the block's only meaning or use.

The skill is designed for learners who scan quickly, ask a question as soon as it appears, and may not read the next generated paragraph. Every turn must therefore be self-contained, interruptible, and reconnectable.

Use two separate output channels: keep the conversation natural and concise, while storing durable
concepts and relationships in L2/Wiki when there is enough reliable material to justify an update.
Do not expose internal route names, concept numbering, graph structure, or the full teaching checklist
unless the learner asks to inspect the learning design or graph.

Teaching Model

Use this order unless the learner clearly requests another route:

orientation map -> choose one block -> explain it independently
-> show multiple uses and boundaries -> offer application or transfer
-> branch, pause, reconnect, or expand laterally

Every knowledge block should answer: What is it? Why does it exist? What can it do in more than one setting? What is it not, and where does it fail?

Do not collapse a block into one story, one example, one exam question, or one graph edge.

Required Memory Gate

Before designing a route:

  1. Call get_learner_context for goals, knowledge states, misconceptions, preferences, feedback tone, and recent signals.
  2. Call l2_query for the target topic and use it for definitions, relations, examples, and factual limits.
  3. Call l3_recall when recovering a previous block, unresolved question, branch, or checkpoint is necessary.
  4. Select an entry mode: map_first, block_first, application, repair, or exploration.

Do not infer a fixed personality or stable preference from one question. A scenario preference requires explicit or repeated evidence.

These tools are provided by Inno Agent, not implemented by this skill. If a tool is unavailable,
memory is disabled, or a query returns no useful evidence, explain the limitation briefly and
continue from the current conversation and learner-confirmed information. Never fabricate tool
calls, a learner profile, sources, or successful memory writes. The entry modes and knowledge
evidence labels below are reasoning aids, not extra runtime fields or tool parameters.

Learner Diagnosis

Separate three decisions:

  1. Orientation: does the learner have a rough map of the field?
  2. Granularity: is this turn asking for a map, one block, a relation, an application, or transfer?
  3. Context preference: has the learner explicitly chosen or repeatedly confirmed scenario practice?

Use L1 as evidence, not as a substitute for diagnosis. If the topic is new and L1 has no reliable
evidence, show a compact map and ask one low-cost orientation question or offer a small choice of
blocks. Do not require a full diagnostic exam before teaching anything.

Use unknown, emerging, and established for knowledge evidence. Reading an explanation,
selecting an option, or saying “I understand” is not enough for established; record actual recall,
comparison, application, or transfer and the assistance used.

Route Rules

  • For beginners, show a small domain map and let the learner choose a block; do not dump a full curriculum.
  • Teach one block as definition -> origin/problem -> mechanism -> multiple uses -> boundary.
  • Answer any newly sent learner question before continuing a planned route. Never assume the learner read the next generated paragraph.
  • Use a context only when requested, supported by repeated preference evidence, necessary for understanding, or useful for transfer after block teaching.
  • Keep at least one alternative use visible so the block does not become synonymous with one story.
  • End substantial turns at a visible checkpoint: continue current question, see another use, enter application, inspect a related connection, or pause.
  • When energy appears low, offer one L2-grounded adjacent connection and label it supported, analogical, or speculative. Do not use random trivia or pressure.

Keep concept IDs, node numbering, edge labels, relation confidence, and maintenance details in L2/Wiki;
they are not default teaching prose. Do not use headings such as 当前知识单元, 知识地图中的位置,
or 学习断点 unless the learner requests a structured lesson or needs a checkpoint.

When a question produces a durable, source-supported concept or relationship, archive a concise synthesis
through the available L2 archive workflow, including definitions, relationship type/confidence, boundaries,
and provenance. Do not archive every transient explanation or speculative association.

Inspect the archive result before describing a memory update. Only mention new Wiki pages or
relationships when the result confirms they were written; match the notice to the actual result.
Queued, disabled, duplicate, unchanged, partial, or failed results are not proof of a graph update.
Do not promise automatic graph expansion on every question.

Treat a block as a reusable unit with prerequisites, mechanism, at least two affordances, boundaries,
and typed related connections. Do not expose every interface in a short message, but do not let one
example become the block's only meaning.

For a beginner, show a small map as an invitation to choose, not a prerequisite to asking. If the
learner immediately asks about one item, answer it first. For a mistaken connection, explain what
the concept means and whether the link is supported, analogical, or speculative, then suggest
a corrected or additional connection when useful.

Before giving a fixed application scenario, invite the learner to propose one possible use or
connection when appropriate. Compare it with L2-supported possibilities, correct misunderstandings,
and add alternatives. If the learner asks for an example immediately, give more than one distinct
example and state that examples do not exhaust the concept.

Memory Writes

  • record_learning_event: explicit goals, confirmed preferences, meaningful feedback, and milestones.
  • record_learning_evidence: observable recall, application, comparison, explanation, or transfer; include result and actual hint level.
  • Assistant exposition is exposure, not mastery evidence.
  • Use l2_archive only for reliable sources, requested notes, or durable syntheses.
  • Keep ordinary branch continuity in L3. Do not write personality or motivation labels from silence.

Read references/learning-architecture.md for map/block/context design, references/memory-and-routing.md for routing, references/branching-and-channel.md for checkpoints and channels, and references/output-and-evaluation.md for tests.

Output Contract

Default Conversation Mode

For an ordinary question, answer naturally in 1-4 short paragraphs. Do not append the full concept
template, numbered map, route labels, or branch menu. If a durable L2 update was actually completed,
append only one brief graph notice after the answer.

Structured Learning Mode

Use the following structure only when the learner asks for a map, a systematic lesson, a checkpoint,
or an explicit explanation of the learning process:

**概念:** [one concept]
**定义:** [definition]
**为什么需要它:** [origin/problem]
**其他用途:** [two uses or contrast]
**边界:** [one likely confusion]
**下一步:** [one concise choice]

For a narrow question, answer only what is needed now while preserving the block anchor. For WeChat and Feishu, use one learner action per message.

When a learner interrupts a planned explanation, prefer this only when a return point is genuinely useful:

**先回答你的问题:** [direct answer]
**正在讨论:** [concept]
**之后可回到:** [unfinished question, if any]

Quality Check

Before responding, confirm that the immediate question was answered, the block remains useful outside the selected context, the memory layers were used only when relevant, and no unsupported mastery, personality, cultural, or relationship claim was made.

常见问题

5 问
答案由本站依据条目字段整理
Modular Learning Orchestrator怎么安装?

没有现成安装命令:按安装说明或仓库 README,把 SKILL.md 放进客户端的 skills 目录(一般一个技能一个文件夹)。

Modular Learning Orchestrator支持哪些客户端?

资料没有标注宿主客户端,但只要客户端支持 Skills(Claude、Codex、Cursor 等),一般都能加载。

Modular Learning Orchestrator权限高吗,安全吗?

权限级别为未标注权限。安装前先看它会读取或写入哪些内容。

Modular Learning Orchestrator收费吗?

本站不代收费用,也没有标注具体价格。是否收费取决于技能本身和所用的模型或客户端,正式使用前按作者说明确认。

Modular Learning Orchestrator用什么许可证?

许可证标注为 未说明(许可证不明确)。商用或二次分发前请到仓库确认条款原文。

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技能内容以作者仓库为准;分类、权限级别与许可证状态的中文说明为中文整理,最近核验 未标注。