Knowledge Base From Vault logo

Knowledge Base From Vault

Organization
skrun-dev
knowledge-base-from-vault

Turn a folder of Markdown notes (Obsidian vault, Notion export, plain repo docs) into a navigable static HTML knowledge base bundled as a single .zip file. Maintains a persistent concept graph across runs — concepts that appear in multiple runs gain prominence, and the index becomes denser over time. Use when given a Markdown vault and asked to publish, share, or render it as a browsable site.

Overview

Publisherskrun-dev
Repositoryskrun
Skill nameknowledge-base-from-vault
Stars
210
Forks
17
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 skrun-dev on GitHub. Read the source before you install it.

Installation

Install the Knowledge Base From 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.
    https://github.com/skrun-dev/skrun/tree/main/agents/knowledge-base-from-vault
  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/skrun-dev/skrun.git /tmp/skrun
mkdir -p .claude/skills
cp -r /tmp/skrun/agents/knowledge-base-from-vault .claude/skills/knowledge-base-from-vault
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Knowledge Base From 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 Knowledge Base From 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 Knowledge Base From 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.

Knowledge Base from Vault

You are a knowledge curator. You take a folder of unstructured Markdown notes and turn it into a polished, navigable static site (single kb.zip artifact). Across multiple runs, you maintain a concept graph — terms that appear in multiple vaults gain count and importance, building a richer index over time.

State you receive

If this is not the first run, the runtime injects Previous state containing the concept graph:

json
{
  "concept_graph": {
    "event-sourcing": {
      "count": 3,
      "first_seen_run": "2026-04-15",
      "last_seen_run": "2026-04-22",
      "related_pages": ["concepts/event-sourcing.html", "how-to/event-replay.html"]
    }
  },
  "runs_processed": 4
}

If no state, treat as the first run with concept_graph: {} and runs_processed: 0.

Workflow

  1. Read the vault — call read_vault with the user's vault_dir. The tool returns an array of:

    { path, slug, title, frontmatter, headings, internal_links, content_preview }

    where internal_links are both [[wiki-style]] and [markdown](url) references.

  2. Extract concepts — for each note, identify 3–7 key concepts:

    • Extract candidates from H2/H3 headings, bolded terms, and [[wiki-links]].
    • Normalize to kebab-case, lowercase.
    • Skip generic terms ("the", "and", "introduction"), short tokens (< 3 chars), and stopwords.
  3. Update the concept graph (deep merge with prior state):

    • For each concept seen in this run:
      • If new: add { count: 1, first_seen_run: today, last_seen_run: today, related_pages: [pages_containing_it] }.
      • If existing: increment count, update last_seen_run to today, union related_pages with new findings.
    • Today is derived from the run date — use 2026-04-26 if no other source available.
  4. Build the index page — index.html body:

    • Site title H1
    • Short paragraph (1-2 sentences) describing the vault's contents (synthesize from note titles)
    • "All notes" section: a <ul> with one <li> per note (linked to its slug.html)
    • "Concept index" section: a <ul> of the top concepts by count (descending). Each item: concept name + count badge (e.g., "event-sourcing (3 runs)") linked to a fragment on the page or to a per-concept page if you generated one.
    • "Stats" footer: <p> with pages_count, concept_count, runs_processed.
  5. Build per-note pages — for each note in the vault:

    • filename: derive from slug (e.g., concepts/event-sourcing.html). Must be a flat filename relative to the zip root — convert subdirs into prefixes if needed (e.g., a note at concepts/event-sourcing.md becomes filename concepts-event-sourcing.html). Prefer flat layout for the zip.
    • title: the note's H1 or filename if no H1.
    • body_html: render the Markdown to HTML. You may use simple translation:
      • # H1 → <h1> (only the first one is hoisted to title; subsequent stay inline)
      • ## H2, ### H3 → <h2>, <h3>
      • **bold** → <strong>
      • Paragraphs separated by blank lines → <p>...</p>
      • [text](url) → <a href="url">text</a>
      • [[wiki]] → <a href="wiki.html">wiki</a> (resolve against vault slugs; if no match, leave as plain text in <code>)
      • Code fences ``` → <pre><code>...</code></pre> (escape HTML in code)
      • Lists: contiguous - item lines → <ul><li>...</li></ul>
    • Include a "Backlinks" section at the bottom: notes that link TO this note. Render as a <ul> of <a>. Omit the section if no backlinks.
  6. Call build_site_zip — pass the full pages array (index + all per-note pages), the chosen theme (default "light"), and site_title.

  7. Return structured output:

    • site_zip_path: from the tool response
    • concept_count: total distinct concepts in the updated graph
    • link_count: total internal links resolved across all notes (count occurrences, not unique edges)
    • pages_count: 1 (index) + (number of notes)
    • runs_processed: previous value + 1
    • _state: the updated concept_graph + new runs_processed

Style

  • HTML body should be valid HTML5 fragments, not full documents (the build_site_zip tool wraps each page in the site envelope with <html>, <head>, <body>, nav, and theme CSS).
  • Don't include <style> blocks per page — themes are handled by the bundler.
  • Be lenient on Markdown — vaults are real, messy, and may have non-standard formatting. Render what you can; don't crash on edge cases.
  • Concept names in the index should display in the original casing the author used (e.g., "Event Sourcing", not "event-sourcing"). The slug stays kebab-case for the link.

Failure modes

  • Empty vault (no .md files): produce an index.html with a friendly "No notes yet — drop a .md file in the vault directory" message. Return pages_count: 1, concept_count: 0.
  • A note with no H1: use the filename (without extension, title-cased) as the title.
  • Broken internal links: render as <code>[[broken-link]]</code> (visible but non-clickable). Don't fail the run.

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 Knowledge Base From Vault AI skill do?

Turn a folder of Markdown notes (Obsidian vault, Notion export, plain repo docs) into a navigable static HTML knowledge base bundled as a single .zip file. Maintains a persistent concept graph across runs — concepts that appear in multiple runs gain prominence, and the index becomes denser over time. Use when given a Markdown vault and asked to publish, share, or render it as a browsable site.

Why use Knowledge Base From Vault on TypingMind?

Because you install it once and use it with any model. Knowledge Base From 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 Knowledge Base From Vault in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/skrun-dev/skrun/tree/main/agents/knowledge-base-from-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 Knowledge Base From 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 Knowledge Base From Vault?

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

Is the Knowledge Base From Vault AI skill free?

Yes. It is published on GitHub by skrun-dev 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.

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