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learning-vault

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Learning Vault Generator

学习库生成工具

Create a fully structured Obsidian vault for any learning goal — certification exams, courses, skill acquisition, or research programs.
为任意学习目标创建结构完整的Obsidian库——包括认证考试、课程学习、技能掌握或研究项目。

Trigger Phrases

触发短语

  • "create a learning vault for X"
  • "build a study vault"
  • "set up a certification vault"
  • "learning vault for [topic]"
  • "/learning-vault"
  • "为X创建学习库"
  • "搭建学习库"
  • "配置认证备考库"
  • "[主题]学习库"
  • "/learning-vault"

Interactive Setup

交互式设置

Ask the user these questions (use AskUserQuestion):
向用户询问以下问题（使用AskUserQuestion）：

1. Subject & Goal

1. 主题与目标

  • What is the learning goal? (certification, course, skill, research)
  • What is the subject? (e.g., "AWS Solutions Architect", "Rust programming", "Machine Learning")
  • Is there a specific exam or assessment? If yes, get: format, passing score, domains/topics, timeline
  • 学习目标是什么？（认证、课程、技能、研究）
  • 学习主题是什么？（例如："AWS Solutions Architect"、"Rust编程"、"机器学习"）
  • 是否有特定的考试或评估？如果有，请获取：考试形式、及格分数、涵盖领域/主题、时间规划

2. Structure

2. 结构设置

  • How many main topics/domains? (auto-detect from curriculum if URL provided)
  • Are there courses to track? (get URLs, lesson counts)
  • Are there scenarios/practice areas?
  • 主要主题/领域有多少个？（若提供课程URL则自动识别）
  • 是否需要追踪课程进度？（获取课程URL、课时数量）
  • 是否包含场景/实践环节？

3. Self-Assessment

3. 自我评估

  • For each domain/topic, ask: "How confident are you?" (expert/strong/moderate/needs-work/no-experience)
  • This drives the study priority ordering
  • 针对每个领域/主题，询问："你的掌握程度如何？"（专家/熟练/中等/需提升/零基础）
  • 该评估将决定学习优先级排序

4. Configuration

4. 配置选项

  • Vault location (default: ~/Brains/{subject-slug}/)
  • Daily notes? (yes/no)
  • Dataview plugin assumed? (yes — required for queries)
  • 库存储位置（默认：~/Brains/{主题缩写}/）
  • 是否启用每日笔记？（是/否）
  • 是否默认启用Dataview插件？（是——查询功能必需）

Vault Architecture

库架构

Based on the genome vault pattern at ~/Brains/genome/:
{vault}/
├── Dashboard.md              — central hub with dataview queries
├── MoC - Courses.md          — course progress tracker
├── MoC - Domains.md          — domain/topic overview
├── MoC - Concepts.md         — key concepts by domain
├── MoC - Scenarios.md        — practice scenarios (if applicable)
├── Action Items.md           — dataview task aggregator
├── Question Index.md         — navigate by question type
├── Key Pitfalls.md           — common mistakes to avoid
├── Exam Cheat Sheet.md       — last-minute review card
├── Courses/                  — one note per course
│   └── {Course Name}.md
├── Domains/                  — one note per domain/topic
│   └── {Domain Name}.md
├── Concepts/                 — atomic knowledge units
│   └── {Concept Name}.md
├── Scenarios/                — practice scenarios
│   └── {Scenario Name}.md
├── Lessons/                  — individual lesson notes
│   └── Lesson - {Name}.md
├── Resources/                — links, study plans
│   ├── Official Links.md
│   └── Study Plan.md
├── Templates/                — note templates
│   ├── _Course.md
│   ├── _Lesson.md
│   ├── _Concept.md
│   ├── _Scenario.md
│   └── _Domain.md
└── .obsidian/
    ├── app.json
    ├── community-plugins.json
    └── plugins/
        └── dataview/
            ├── main.js          — copy from reference vault
            ├── manifest.json
            ├── styles.css
            └── data.json        — enable DataviewJS, inline queries, HTML
基于~/Brains/genome/中的基因组库模式：
{vault}/
├── Dashboard.md              — 集成Dataview查询的中央枢纽
├── MoC - Courses.md          — 课程进度追踪器
├── MoC - Domains.md          — 领域/主题概览
├── MoC - Concepts.md         — 按领域分类的核心概念
├── MoC - Scenarios.md        — 实践场景（若适用）
├── Action Items.md           — Dataview任务聚合器
├── Question Index.md         — 按问题类型导航
├── Key Pitfalls.md           — 需避免的常见错误
├── Exam Cheat Sheet.md       — 考前快速复习卡片
├── Courses/                  — 单课程对应单笔记
│   └── {Course Name}.md
├── Domains/                  — 单领域对应单笔记
│   └── {Domain Name}.md
├── Concepts/                 — 原子化知识单元
│   └── {Concept Name}.md
├── Scenarios/                — 实践场景
│   └── {Scenario Name}.md
├── Lessons/                  — 单课时对应单笔记
│   └── Lesson - {Name}.md
├── Resources/                — 链接、学习计划
│   ├── Official Links.md
│   └── Study Plan.md
├── Templates/                — 笔记模板
│   ├── _Course.md
│   ├── _Lesson.md
│   ├── _Concept.md
│   ├── _Scenario.md
│   └── _Domain.md
└── .obsidian/
    ├── app.json
    ├── community-plugins.json
    └── plugins/
        └── dataview/
            ├── main.js          — 从参考库复制
            ├── manifest.json
            ├── styles.css
            └── data.json        — 启用DataviewJS、内联查询、HTML

Dataview Plugin Setup

Dataview插件配置

The vault MUST include a working Dataview plugin — not just config, but the actual plugin binary. During generation:
  1. Copy the bundled plugin from this skill's directory:
    bash
    SKILL_DIR="$(dirname "$0")"  # or resolve from ~/.claude/skills/learning-vault/
mkdir -p {vault}/.obsidian/plugins/dataview
cp ~/.claude/skills/learning-vault/dataview-plugin/* {vault}/.obsidian/plugins/dataview/
    The 
    dataview-plugin/
    directory inside this skill contains: 
    main.js
    , 
    manifest.json
    , 
    styles.css
    , 
    data.json
    — a complete, pre-configured Dataview plugin.
  2. Register in 
    community-plugins.json
    : 
    ["dataview"]
No manual plugin installation needed — Dataview works on first vault open.
库必须包含可正常运行的Dataview插件——不仅是配置文件，还需包含实际插件二进制文件。生成过程中：
  1. 从技能目录复制打包好的插件：
    bash
    SKILL_DIR="$(dirname "$0")"  # 或从~/.claude/skills/learning-vault/获取
mkdir -p {vault}/.obsidian/plugins/dataview
cp ~/.claude/skills/learning-vault/dataview-plugin/* {vault}/.obsidian/plugins/dataview/
    本技能内的
    dataview-plugin/
    目录包含：
    main.js
    、
    manifest.json
    、
    styles.css
    、
    data.json
    ——一套完整的预配置Dataview插件。
  2. 在
    community-plugins.json
    中注册    ：
    ["dataview"]
无需手动安装插件——打开库时Dataview即可直接使用。

Frontmatter Schema

前置元数据Schema

All Notes

所有笔记通用

yaml
type: course | domain | concept | scenario | lesson | resource | moc | meta | dashboard
created_date: 'YYYY-MM-DD'
tags: []
yaml
type: course | domain | concept | scenario | lesson | resource | moc | meta | dashboard
created_date: 'YYYY-MM-DD'
tags: []

Course

课程笔记

yaml
status: not-started | in-progress | completed
priority: 1-5
lessons_total: 0
lessons_done: 0
exam_weight: ""
difficulty: easy | moderate | hard
domains: []  # wikilinks
yaml
status: not-started | in-progress | completed
priority: 1-5
lessons_total: 0
lessons_done: 0
exam_weight: ""
difficulty: easy | moderate | hard
domains: []  # wikilinks

Concept

概念笔记

yaml
domain: "[[Domain Name]]"
status: not-started | in-progress | completed
confidence: low | medium | high
importance: critical | high | medium | low
yaml
domain: "[[Domain Name]]"
status: not-started | in-progress | completed
confidence: low | medium | high
importance: critical | high | medium | low

Scenario

场景笔记

yaml
number: 1-N
domains: []  # wikilinks
difficulty: easy | moderate | hard
yaml
number: 1-N
domains: []  # wikilinks
difficulty: easy | moderate | hard

Lesson

课时笔记

yaml
course: "[[Course Name]]"
section: ""
status: not-started | in-progress | completed
concepts: []  # wikilinks
yaml
course: "[[Course Name]]"
section: ""
status: not-started | in-progress | completed
concepts: []  # wikilinks

Generation Rules

生成规则

  1. Every concept note gets a 
    - [ ] #review Can I explain this without notes?
    task
  2. Every scenario note gets a 
    - [ ] #practice Build a mini-project for this scenario
    task
  3. Every lesson note gets a 
    - [ ] #review Review this lesson before exam
    task
  4. Wikilinks everywhere — concepts link to domains, scenarios link to concepts, courses link to both
  5. Question Index maps common questions to concept notes (like genome vault's "search by concern, not gene")
  6. Key Pitfalls lists wrong answers the exam loves to test (attractive distractors)
  7. Study Plan generates phases based on: easy stuff first → gaps second → big course → practice → review
  1. 每个概念笔记自动添加任务：
    - [ ] #review 我能否脱离笔记讲解这个概念？
  2. 每个场景笔记自动添加任务：
    - [ ] #practice 针对该场景搭建一个小型项目
  3. 每个课时笔记自动添加任务：
    - [ ] #review 考前复习本课时内容
  4. 全库使用wikilinks——概念链接到所属领域，场景链接到相关概念，课程同时链接领域和概念
  5. 问题索引将常见问题映射到对应概念笔记（类似基因组库的"按需求搜索，而非按知识点"）
  6. 常见误区列出考试中常见的干扰项（易选错的答案）
  7. 学习计划按以下阶段生成：先学简单内容→填补知识缺口→完成核心课程→实践练习→最终复习

Dataview Queries Used

使用的Dataview查询

The vault uses these Dataview query patterns:
  • TABLE
    from folders with filters on status, priority, confidence
  • TASK
    aggregation from all notes with tag filters (#review, #practice)
  • GROUP BY
    for domain-level summaries
  • SORT
    by priority, weight, confidence level
  • LIST
    for filtered views (not-started, in-progress, completed)
库中使用以下Dataview查询模式：
  • TABLE
    ：从文件夹中筛选状态、优先级、掌握程度相关内容
  • TASK
    ：聚合所有带标签的任务（#review、#practice）
  • GROUP BY
    ：生成领域级汇总
  • SORT
    ：按优先级、权重、掌握程度排序
  • LIST
    ：生成筛选视图（未开始、进行中、已完成）

Self-Assessment → Priority Mapping

自我评估→优先级映射

Self-AssessmentConfidenceStudy Priority
no-experiencelow1 (study first)
needs-worklow2
moderatemedium3
strongmedium-high4 (review only)
experthigh5 (quick check)
Higher exam weight × lower confidence = higher study priority.
自我评估掌握程度学习优先级
零基础低1（优先学习）
需提升低2
中等中3
熟练中高4（仅需复习）
专家高5（快速检查）
考试权重越高 × 掌握程度越低 = 学习优先级越高。

Study Plan Generation

学习计划生成

Phases are generated based on:
  1. Quick wins: courses with few lessons + high confidence → build momentum
  2. Gap-filling: domains with low confidence + high exam weight
  3. The big course: the largest course by lesson count
  4. Practice: scenarios, hands-on projects
  5. Final review: cheat sheet, pitfalls, low-confidence concepts
学习阶段基于以下逻辑生成：
  1. 快速积累信心：课时少且掌握程度高的课程→建立学习动力
  2. 填补知识缺口：掌握程度低且考试权重高的领域
  3. 核心课程攻坚：课时数量最多的课程
  4. 实践强化：场景练习、实操项目
  5. 最终冲刺复习：复习 cheat sheet、常见误区、掌握程度低的概念

Example Usage

使用示例

User: "Create a learning vault for the AWS Solutions Architect Associate exam"
→ Ask: domains, courses (e.g., Udemy course URL), timeline, self-assessment → Generate: vault at ~/Brains/aws-saa/ with domains (Compute, Storage, Networking, Security, etc.), concepts per domain, practice scenarios, course tracking, dataview-powered progress dashboard
用户："为AWS Solutions Architect Associate考试创建学习库"
→ 询问：涵盖领域、相关课程（例如Udemy课程URL）、时间规划、自我评估 → 生成：存储在~/Brains/aws-saa/的库，包含领域（计算、存储、网络、安全等）、各领域对应概念、实践场景、课程追踪、Dataview驱动的进度仪表盘

Reference Implementation

参考实现

The CCAF vault at ~/Brains/ccaf/ is the canonical example:
  • 88 files, 462 wikilinks
  • 5 domains, 31 concepts, 8 scenarios, 7 courses, 21 lessons
  • Full dataview integration
  • Multiple navigation paths: by domain, by concept, by scenario, by question type
~/Brains/ccaf/中的CCAF库是标准示例：
  • 88个文件，462个wikilinks
  • 5个领域、31个概念、8个场景、7门课程、21个课时
  • 完整的Dataview集成
  • 多种导航路径：按领域、按概念、按场景、按问题类型