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adaptive-learning适应性学习

Agent Skill

adaptive-learning 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 OpenClaw 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

5,566

周安装

223

GitHub Stars

公开资料未说明

下载量

1,802
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:adaptive-learning(适应性学习)
来源仓库:https://github.com/weishuz/adaptive-learning
安装命令:
openclaw skills install adaptive-learning
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install adaptive-learning

简介

从课程材料生成自适应抽认卡应用,支持间隔重复学习。

  • 适用于知识记忆强化与考试准备场景。
  • 采用 FSRS 算法和贝叶斯模型优化复习计划。
  • 处理 PDF 或 URL 内容时注意版权合规。
  • 输出文件需用户授权后方可保存。adaptive-learning 属于效率类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
adaptive-learning
description
Create adaptive learning flashcard apps from course materials (URLs, PDFs, or folders). Uses FSRS (Free Spaced Repetition Scheduler) and Bayesian Knowledge Tracing for intelligent review scheduling. Use when asked to create study materials, flashcards, review apps, spaced repetition systems, or adaptive quizzes from course content. Triggers on "make me a study app", "create flashcards for this course", "adaptive learning", "spaced repetition", "review system for [course]".

Adaptive Learning Skill

Create self-contained, browser-based adaptive learning apps from any course material.

Architecture

  • FSRS (ts-fsrs): Per-card spaced repetition scheduling (Stability, Difficulty, Retrievability)
  • BKT: Per-topic Bayesian Knowledge Tracing for mastery estimation
  • Two modes: Breadth-first (cover all topics, weakest first) / Depth-first (drill one topic deep)
  • Pure frontend: HTML + CSS + JS, works offline via file://, no server needed

Workflow

1. Gather Course Material

From a URL:

1. Fetch the course page, extract topic list and resource links
2. Download HW/Discussion/Lecture PDFs to ~/COURSE_NAME/

From a local folder:

1. List files, identify PDFs and documents
2. Read/parse to understand topics and content

2. Generate Question Bank

Create questions.json with this schema:

[{
  "id": "unique-id",
  "topic": "Topic Name",
  "topicIndex": 0,
  "difficulty": 1,
  "question": "Supports $LaTeX$ via KaTeX",
  "answer": "Supports $LaTeX$ and \\
 for line breaks",
  "tags": ["tag1", "tag2"]
}]

Guidelines:

  • 5-8 questions per topic minimum, across 3 difficulty levels
  • Difficulty 1 (基础): Definitions, "what is", simple complexity questions
  • Difficulty 2 (中等): Apply algorithms, analyze examples, describe procedures
  • Difficulty 3 (高级): Proofs, novel problem design, optimization, "why" questions
  • Use LaTeX ($...$ inline, $$...$$ block) for math
  • Use `\\

` for line breaks in question/answer text

  • topicIndex controls topic ordering (0-based)

3. Build the App

Run the bundler script:

bash SKILL_DIR/scripts/generate-course.sh <course-id> <questions.json> <output-dir>

Then register the course in engine.js COURSE_REGISTRY:

{ id: 'course-id', name: 'Course Name', desc: 'Description', school: 'School', term: 'Term' }

4. Verify

Open <output-dir>/index.html in a browser. Verify:

  • Course appears in selector
  • Cards render with KaTeX math
  • Flip/rating/FSRS scheduling works
  • Mode toggle (breadth/depth) works

Framework Files (assets/framework/)

FilePurpose
index.htmlMain page with course selector + learning UI
style.cssDark theme, responsive styles
engine.jsFSRS + BKT engine, question selection, state management
ts-fsrs.umd.jsFSRS algorithm library (UMD build of ts-fsrs)

Key Features

  • FSRS scheduling: Cards show Stability/Difficulty values; review intervals adapt to performance
  • BKT mastery: Per-topic mastery percentage in progress drawer
  • Configurable: Target retention (70-97%), daily new card limit
  • localStorage: All progress persists across sessions
  • Keyboard shortcuts: Space=flip, 1=Good, 2=Hard, 3=Again, f=follow-up, n=next-topic
  • KaTeX: Full LaTeX math rendering
  • Drag & drop: Import any questions.json directly in the UI
  • Multi-course: One framework, multiple course data packs

Adding to Existing Installation

To add a new course to an existing adaptive-learning setup at ~/adaptive-learning/:

  1. Save questions.json to ~/adaptive-learning/courses/<id>/
  2. Generate preload: bash scripts/generate-course.sh <id> questions.json ~/adaptive-learning/framework/
  3. Add to COURSE_REGISTRY in engine.js

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

71.77%
按下载量换算1,293

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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