Learning Vault logo

Learning Vault

Community
glebis
learning-vault

Generate a dedicated Obsidian learning vault for any certification, course, or study goal. Creates structured notes with domains, concepts, lessons, scenarios, MoCs, dataview queries, action items, and multiple navigation paths. Inspired by the genome vault pattern. Use when the user wants to create a study vault, learning vault, certification prep vault, or structured knowledge base for a learning goal.

Overview

Publisherglebis
Repositoryclaude-skills
Skill namelearning-vault
Stars
379
Forks
56
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by glebis on GitHub. Read the source before you install it.

Installation

Install the Learning Vault 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/glebis/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/learning-vault .claude/skills/learning-vault
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Learning Vault 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 Vault 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 Vault 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.

Learning Vault Generator

Create a fully structured Obsidian vault for any learning goal — certification exams, courses, skill acquisition, or research programs.

Trigger Phrases

  • "create a learning vault for X"
  • "build a study vault"
  • "set up a certification vault"
  • "learning vault for [topic]"
  • "/learning-vault"

Interactive Setup

Ask the user these questions (use AskUserQuestion):

1. Subject & Goal

  • 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

2. Structure

  • 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?

3. Self-Assessment

  • For each domain/topic, ask: "How confident are you?" (expert/strong/moderate/needs-work/no-experience)
  • This drives the study priority ordering

4. Configuration

  • Vault location (default: ~/Brains/{subject-slug}/)
  • Daily notes? (yes/no)
  • Dataview plugin assumed? (yes — required for queries)

Vault Architecture

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

Dataview Plugin Setup

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.

Frontmatter Schema

All Notes

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

Course

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

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

Scenario

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

Lesson

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

Generation Rules

  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

Dataview Queries Used

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)

Self-Assessment → Priority Mapping

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.

Study Plan Generation

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

Example Usage

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

Reference Implementation

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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Learning Vault AI skill do?

Generate a dedicated Obsidian learning vault for any certification, course, or study goal. Creates structured notes with domains, concepts, lessons, scenarios, MoCs, dataview queries, action items, and multiple navigation paths. Inspired by the genome vault pattern. Use when the user wants to create a study vault, learning vault, certification prep vault, or structured knowledge base for a learning goal.

Why use Learning Vault on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/glebis/claude-skills/tree/main/learning-vault. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Learning Vault?

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 Vault?

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

Is the Learning Vault AI skill free?

Yes. It is published on GitHub by glebis 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇