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vaultcraft

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imageGet it in ChatGPT

imageLicense: MIT imageChatGPT and Codex imageObsidian imagePRs Welcome

A plugin for ChatGPT and Codex that turns any scattered materials (lecture slides, meeting notes, project docs, research papers) into a navigable, richly visual Obsidian knowledge vault. Hover-visible definitions, embedded PDFs, extracted figures, ELI5 analogies, comparison tables, Kanban boards, stakeholder profiles, and optional spaced-repetition flashcards.

Now in the official ChatGPT plugin directory. vaultcraft passed OpenAI's review, so you can add it from ChatGPT without touching GitHub. The same listing covers Codex.

Quick start · How to use · Vault types · Examples · Conventions · Roadmap · Contributing · Project page · AI note taking guide

Why this exists

Most people take notes as they go. By the time you need them — before an exam, in a client meeting, writing a report — those notes are scattered across PDFs, Notion pages, Google Docs, and handwritten pages. The information is there; it just isn't retrievable under pressure.

This agent inverts the workflow: take the same source material you already have and produce a linked knowledge graph optimised for how you actually use it.

• Students: hover over any wikilink → see the definition. Open Tables.md → recite the elevator pitch for every concept comparison the night before an oral exam.
• Professionals: open the Kanban board → see active projects and open actions. Click a stakeholder → see their profile, meeting history, and stored documents in one note.
• Researchers: browse Papers/ → every paper linked to the concepts it introduces, every concept linked back to the papers that define it.

The agent is subject-agnostic. Point it at law cases, anatomy, NLP slides, client briefings, or company research — the structure adapts to your vault type.

What it does

Point vaultcraft at a folder of materials. It asks a few intake questions, then runs a ten-phase pipeline:

1. Intake. Chip-style questions for vault type, format, depth, language, deadline, sources, and output path. Nothing touches disk yet.
2. Read and extract figures. Opens every PDF, PPTX, notebook, and markdown file. Extracts concept names, formulas, code patterns, and section headings. For PDFs containing diagrams or charts, runs pdfimages to extract embedded figures, names them descriptively, and copies them to Assets/ for inline use in notes.
3. Plan. Proposes the folder layout and the full list of notes to write. Waits for confirmation.
4. Write atomic notes. One concept per file, each opened by a > [!definition] callout so Obsidian shows the answer in hover preview without a click. Work vaults get Meeting, Company, Person, Decision, and Project templates instead.
5. Write lecture / topic sheets. Per session or topic: TL;DR callout, narrative, eight to twelve potential exam questions (studies) or action items and decisions (work).
6. Embed source PDFs. Automatically copies source materials into the vault, converts .pptx / .docx to PDF via LibreOffice, and inserts an inline callout in each note. Obsidian renders the original deck or document inline above your typed summary. Extracted figures embed at the relevant section of each note.
7. Cartography (low priority). Optional JSON Canvas concept map.
8. Hub and dashboard. Studies vaults: entry MOC + Tables.md for oral exams. Work vaults: Kanban board (00 - Dashboard.md) with active projects, open actions, and recent meetings via Dataview.
9. Quality pass. Broken links, bidirectional link audit (concept ↔ lecture), depth check, orphans, quality report (notes missing definition / example / flashcards).

Typical run on a twelve-lecture course: about thirty minutes, ~150 atomic notes, ~3,000 wikilinks. A work vault with 10 stakeholders and 20 meetings: ~45 minutes. Output is a working Obsidian vault you open the same day.

Vault types

The first intake question — what kind of vault is this? — determines folder structure, note templates, plugins, graph colours, and dashboard style.

Work vault

When you select work, the agent builds a professional knowledge base around people, projects, and decisions — not courses and lectures.

Folder structure

<VaultRoot>/
├── 00 - Dashboard.md          ← Kanban board + Dataview queries
├── Projects/                  ← one hub note per initiative
├── Meetings/                  ← YYYY-MM-DD meeting notes
├── Stakeholders/
│   ├── People/                ← contact profiles with interaction history
│   └── Companies/             ← organisation profiles
├── Decisions/                 ← decision log with options, rationale, review dates
├── Concepts/                  ← domain knowledge notes
├── Documents/
│   └── <person-or-project>/   ← PDFs embedded in the relevant profile
└── Assets/                    ← extracted figures, logos, diagrams

Kanban dashboard

00 - Dashboard.md uses the obsidian-kanban plugin:

🔴 Blocked  |  🟡 In progress  |  🟢 Done

Below the board, Dataview queries surface:

• Open action items across all meetings
• Active projects with their last update date
• Meetings from the past 14 days

Stakeholder profiles

During intake, the agent asks for your key stakeholders by name and creates profile stubs for each one — so meetings and decisions can link to real people from day one.

Each person note:

---
tags: [person]
company: [[Acme Corp]]
role: Head of Sustainability
status: active
---
# Jane Smith

> [!stakeholder] Role
> Jane is Head of Sustainability at [[Acme Corp]] — primary contact for the
> Q3 ESG reporting engagement.

## Documents
> [!example]+ 📄 Stored documents
> ![[Documents/jane-smith/contract-2026.pdf]]

## Interaction history
- [[Meeting — ESG scope kickoff (2026-05-14)]]
- [[Meeting — Data gap review (2026-06-03)]]

Source PDFs (contracts, reports, briefings) drop into Documents/<person>/ and embed inline in the profile note.

Work vault callout colours

Studies vault

The original vaultcraft context. Point it at lecture slides, lab notebooks, textbook chapters, or recorded transcripts and get an exam-ready knowledge graph.

Folder structure

<VaultRoot>/
├── Tables.md                  ← oral-exam comparison cheatsheet
├── MOC — <Course>.md          ← entry map of content
├── Lectures/                  ← one note per lecture/session
├── Labs/                      ← one note per lab
├── Concepts/                  ← atomic concept notes
├── Papers/                    ← paper notes (if research-heavy course)
├── _Shared/                   ← bridge notes across courses
└── Assets/                    ← extracted figures, diagrams

Tables.md — the oral-exam cheatsheet

Every studies vault gets a Tables.md at root with comparison tables. Each row has a "Say this" column — a one-sentence elevator pitch you can recite verbatim.

Studies callout colours

Example output

The screenshots below show the author's own vault — built from four CS courses (NLP, machine learning, predictive analytics, digital platforms) — purely to illustrate what the agent produces. Your vault will look structurally similar but populated with whatever subject you feed it.

The Obsidian graph view of the four-course vault:

imageGraph view of a 4-course vault built by vaultcraft

What you're looking at:

• Each colour cluster is one course (path-based colouring — no tag pollution)
• ~640 atomic notes across 4 courses, ~4,700 wikilinks holding them together
• Red dots: cross-course bridge notes in _Shared/ connecting concepts across courses
• White dots: lecture / lab study sheets at the centre of each cluster
• Larger dots: hub concepts referenced from many other notes

Source slides and figures embedded inline

Phase 6 runs automatically. The agent copies source materials into the vault, converts .pptx / .docx to PDF, drops an inline callout in each lecture note, and embeds extracted figures at the relevant section:

imageLecture note with the source slides embedded inline

What you're looking at (built from the bundled demo materials):

• The lecture note's properties: course, session, source file, exam-likely flag
• The > [!example]+ 🎞️ Course slides callout rendering the slide PDF in Obsidian's native viewer, opened at the relevant page
• Extracted figures (e.g. ![[Assets/ML-L08-fig12-relu-vs-sigmoid.png|600]]) are embedded the same way at the matching section, so notes and the original deck sit in one scroll

Installation

Install

From the plugin directory (no GitHub needed).

• ChatGPT and Codex: open vaultcraft in ChatGPT and select +, or search for vaultcraft in the Plugins tab of ChatGPT or the ChatGPT desktop app. In the Codex CLI, run /plugins.

Then ask: "Build an Obsidian study vault from these lecture slides", or type /vaultcraft.

Straight from this repository (works without a directory listing):

# Claude Code
/plugin install vaultcraft --marketplace MikolajSapek/vaultcraft

# Codex CLI
codex plugin marketplace add MikolajSapek/vaultcraft
codex plugin add vaultcraft@vaultcraft

On claude.ai you can also download the release zip and use Customize > Plugins > Add > Upload plugin.

Install script (macOS and Linux, Claude Code only):

git clone https://github.com/MikolajSapek/vaultcraft.git
cd vaultcraft
./install.sh                          # agent + skill into ~/.claude/
./install.sh --templates <folder>     # optional: copy note templates into <folder>
./install.sh --demo                   # install + show how to run the bundled NLP demo
./install.sh --uninstall              # remove only what the installer added

The script copies the agent and the skills/vaultcraft/ folder, records every file in ~/.claude/.vaultcraft-installed, and never overwrites or deletes any other skill or agent. Templates are copied only when you ask, and existing templates are kept.

Where it runs, and what you get

Optional helpers on your own machine: poppler (brew install poppler) extracts figures from PDFs, and LibreOffice (brew install --cask libreoffice) converts .pptx/.docx so they embed inline. Without them, figures stay inside the PDFs and slides are linked instead of embedded. Companion skills: the official Obsidian skills by Steph Ango (kepano/obsidian-skills, MIT) are used for Obsidian syntax, Bases and Canvas when installed.

What the plugin does on your machine

vaultcraft is instructions plus one small check script: no telemetry, no code that runs on install. When you run it, the assistant:

• reads the files and folders you point it at;
• writes notes, .obsidian/ settings and copies of your sources only inside the vault folder you chose (or into the zip in a sandbox);
• runs local commands such as pdfimages and soffice --convert-to pdf to extract figures and convert slides. It asks before installing anything;
• searches the web (your assistant's own web tool) only to fill gaps in a concept, and cites every source in the note. Those search queries leave your machine; your files do not.
• runs python3 skills/vaultcraft/scripts/check_vault.py <vault> (standard library only, reads the vault, writes nothing) to check the goal: coverage, links, definitions, examples, visuals (every concept and lecture note needs a Pele diagram or a slide image);
• in Claude Code and Cowork, starts its own worker agents (vaultcraft-extractor, vaultcraft-writer, vaultcraft-drafter) on your account, with the same file access.

Only the ChatGPT and Codex directory version adds an optional server (https://vaultcraft.sapcorp.eu/mcp) that returns vault settings, note templates and a link check; it keeps nothing. The Claude and Cursor versions never contact it.

Full details: Privacy.

Try the demo (60 seconds)

./install.sh --demo

This installs vaultcraft and shows you exactly what to paste into Claude Code to build a small NLP vault from three bundled lecture stubs. Output lands at ~/Documents/ObsidianVaults/vaultcraft-demo/.

Long runs

A single run stops after 150 turns. For a big course, Claude Code then returns a partial result: just say continue. The agent keeps its place in .vault-progress.md at the vault root and picks up where it stopped.

How to use

The agent always runs Phase 1 - Intake first, asking questions before touching any files. The first one — what kind of vault is this? — is the most load-bearing; everything else adapts to the answer.

Intake form

Batch A — vault shape

Batch B — style preferences

Batch C — free text

1. Course / project / team name?
2. Specific goal? (e.g. "exam 28 June", "client onboarding by Q3")
3. Priority topics?
4. Deadline / target date?
5. Vault path?
6. Input sources? (file paths, URLs, pasted text)

For work vaults, Batch C also asks: "List your key stakeholders (names / companies) — I'll create their profiles now."

Typical run time

In Claude Code and Cowork the work is split across agents, each pinned to its model, so your own model only decides:

It works in a loop, one batch of sources at a time: extract, decide, write, then run the goal check and fix until the batch is clean, and record progress before the next batch. At the start the agent writes a measurable goal (every named technique in your sources has a note, no broken links or orphans, every concept has a definition and an example, every concept and lecture note has a visual), and the run is not finished until skills/vaultcraft/scripts/check_vault.py says the goal is met. The check runs on your own machine, free. Full rules in Principle 18.

The crafting pipeline

What's in this repo

vaultcraft/
├── .claude-plugin/
│   ├── plugin.json              ← Claude plugin manifest (claude.ai, Cowork, Claude Code)
│   └── marketplace.json         ← Lets this repo act as its own Claude marketplace
├── .codex-plugin/plugin.json    ← ChatGPT and Codex plugin manifest
├── .agents/plugins/marketplace.json ← Lets this repo act as its own Codex marketplace
├── .cursor-plugin/plugin.json   ← Cursor plugin manifest
├── assets/                      ← Plugin logo and icon
├── agents/
│   ├── vaultcraft.md            ← Orchestrator (your session's model) for Claude Code and Cowork, generated from the skill
│   └── vaultcraft-*.md          ← Workers: extractor and writer (Haiku), drafter (Sonnet)
├── skills/
│   ├── vaultcraft/
│   │   ├── SKILL.md             ← Entry point: banner, intake, then generation
│   │   ├── references/          ← The generation pipeline, one file per phase group
│   │   └── templates/           ← Note templates (concept, lecture, golden, lab, bridge)
│   └── README.md                ← Recommended companion skills (kepano/obsidian-skills)
├── scripts/
│   ├── build-agent.sh           ← Regenerates the agent from the skill's references
│   └── package.sh               ← Builds the upload zip for both directories
├── tests/
│   ├── install-test.sh          ← Installer regression test (runs in CI)
│   └── manifest-test.sh         ← Manifest, version and directory-rule checks (runs in CI)
├── docs/
│   ├── installation.md
│   ├── usage.md
│   ├── vault-types.md
│   ├── conventions.md
│   ├── examples.md
│   └── codex-maintenance.md
├── examples/demo-materials/
├── .github/
├── README.md
├── ROADMAP.md
├── SECURITY.md
├── CONTRIBUTING.md
└── LICENSE

Conventions baked into the agent

• Atomic notes — one concept (or one meeting, or one person) per file.
• Hover-visible definitions — definition callout is the FIRST content after H1; Obsidian's hover preview shows it without clicking.
• Worked examples mandatory — every formula gets actual numbers; every theoretical concept gets a concrete scenario.
• ELI5 for hard concepts — math-heavy and abstract concepts get a "Simple explanation (ELI5)" section.
• Wikilinks not hashtags — topical clustering via [[wikilinks]]; tags are folder-level classifiers only.
• Path-based graph colours — different palettes for studies / work / research vaults; no tag pollution.
• Figure extraction — pdfimages pulls figures from source PDFs, names them descriptively, embeds them inline.
• PDF embedding — source slides and documents embed inline in every lecture or topic note automatically.
• Bidirectional link audit — Phase 9 verifies that concept → source lecture and lecture → concept list are mutually consistent.
• Quality report — after every run: count of notes missing definition / example / flashcards, status breakdown, difficulty distribution.
• Token economy — bulk writing on Haiku workers, exemplars and hard concepts on a Sonnet worker, decisions on your session's model.

See docs/conventions.md for the full spec.

Maintainer workflow

vaultcraft is maintained with a Codex-assisted workflow for reviewing agent prompt changes, checking installer safety, improving documentation, generating synthetic test fixtures, and validating generated Obsidian vault quality.

Codex is used as a review and maintenance tool, not an automatic committer. Every AI-assisted change is manually reviewed before merge, and private vault contents, source materials, API keys, and local usernames must never be committed.

• Roadmap: ROADMAP.md
• Security policy: SECURITY.md
• Codex-assisted maintenance notes: docs/codex-maintenance.md

Limitations

• Source quality matters. Pure bullet-point slides produce thinner vaults than detailed lecture decks.
• PPTX requires LibreOffice for conversion — brew install --cask libreoffice.
• Figure extraction requires poppler — brew install poppler. Without it, figures stay inside PDFs.
• Claude Code only. The agent is built for Claude Code's agent system.
• Math notation in scanned PDFs. OCR on scanned slides loses LaTeX. Works best on natively-digital PDFs/PPTX.

License

MIT — see LICENSE.

Contributing

Pull requests welcome — especially if you've used the agent on your own course or project and have improvements to suggest. Open an issue first to discuss large changes.

Built for students, professionals, and researchers who want their knowledge to be retrievable when it matters.