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Obsidian Starter Kit - System - LLM Wiki System

🏠 User Guide / Systems / LLM Wiki System

Sebastien Dubois

21 Apr 2026 — 5 min read

Canonical version: Obsidian Starter Kit - System - LLM Wiki System.

🏠 User Guide / Systems / LLM Wiki System

The LLM Wiki System is the OSK's framework for building persistent, AI-maintained knowledge bases. Inspired by Andrej Karpathy's LLM Knowledge Base pattern (April 2026), it lets you collect raw sources on any topic and have an LLM compile, cross-reference, and maintain a structured wiki. The human curates sources and asks questions; the LLM handles the bookkeeping.

Part of the AI Assistant System.

TL;DR

Three-layer architecture: Raw Sources (immutable) -> Wiki (LLM-maintained) -> Schema (shared conventions)
Nine core operations: create, ingest, explore, query, lint, list, deepen, graduate, absorb
Note type: type/ai_wiki with wiki-specific tags ai_wiki/<wiki-slug>
Naming convention: AI Wiki - <Wiki Name> - <Article>.md prevents clashes
Each article tracks: wiki_name, wiki_role, explored status, confidence level, maturity, sources, graduated_notes
Index.md catalogs all articles; Log.md records all operations chronologically
Speculative links (red links) allowed; they signal future exploration targets
Dedicated agent (OSK Wiki Curator) and review panel (osk-panel-wiki-review)

🎯 Philosophy

The bottleneck in traditional knowledge management is human attention: cross-references rot, indexes go stale, contradictions go unnoticed.

The LLM Wiki approach moves the mechanical bookkeeping — writing summaries, maintaining cross-references, updating indexes, flagging contradictions — to the LLM. The human focuses on source curation and strategic questioning.

Key insight from Karpathy: "The tedious part of maintaining a knowledge base is not the reading or the thinking. It's the bookkeeping." LLMs excel at exactly this.

This creates:

Compounding knowledge. Every ingestion and query enriches the wiki
Source provenance. Every claim traces back to a source with confidence levels
Living structure. Cross-references stay current automatically
Low maintenance. The LLM does the tedious work; you do the thinking

🏗️ Architecture

Three Layers

Layer	Contents	Who Maintains	Rules
Raw Sources (`raw/`)	Articles, papers, PDFs, images, data	Human curates	Immutable. LLM reads, never modifies
The Wiki	Markdown articles, Index, Log	LLM maintains	LLM owns: creates, updates, cross-links
The Schema	`osk-wiki-shared` skill	Co-evolved	Defines structure, naming, workflows

Directory Structure

10 Meta/99 AI Assistant/Wikis/
  <Wiki Name>/
    AI Wiki - <Wiki Name> - Index.md       # Catalog of all articles
    AI Wiki - <Wiki Name> - Log.md         # Chronological operation record
    AI Wiki - <Wiki Name> - <Article>.md   # Wiki articles
    raw/                                    # Source documents

Note Type: AI Wiki Articles

Tag: type/ai_wiki Wiki-specific tag: ai_wiki/<wiki-slug> (e.g., ai_wiki/machine-learning) Template: TPL AI Wiki Article.md Folder: 10 Meta/99 AI Assistant/Wikis/<Wiki Name>/

Properties:

Property	Type	Required	Description
wiki_name	text	yes	Name of the wiki
wiki_role	select	yes	article, index, log, or source_summary
explored	boolean	yes	Whether topic is fully developed
ai_generated	boolean	yes	Always true for wiki articles
confidence	select	no	high, medium, low, or uncertain
maturity	select	no	stub, draft, substantial, or mature. Computed from content metrics
sources	list	no	Source material references
graduated_notes	list	no	Permanent notes extracted during graduation

Special Files

Index.md: The entry point for both humans and LLMs. Contains:

Wiki description and scope
Statistics (article count, explored ratio)
Categorized article links with one-line summaries
Unexplored topics list

Log.md: Append-only chronological record of all operations:

## [YYYY-MM-DD HH:MM] ingest | Article Title
Ingested: URL/file/note reference
- Files created: list
- Files updated: list

📝 Core Operations

Create (`osk-wiki-create`)

Scaffold a new wiki: directory, Index, Log, optional seed articles. Can seed from a topic description or existing vault notes.

Ingest (`osk-wiki-ingest`)

Process a source (URL, file, vault note) into the wiki:

Fetch/read the source
Create source summary article
Update existing concept articles with new info
Create stub articles for new concepts
Update Index and Log

A single source typically touches 5-15 wiki articles.

Explore (`osk-wiki-explore`)

Deeper exploration of topics. Three depth levels:

Shallow: Stubs with key points
Medium: Substantial articles (default)
Deep: Full coverage with web research

For 3+ independent topics, uses parallel agents for speed.

Query (`osk-wiki-query`)

Ask complex questions against the wiki:

Read Index to find relevant articles
Search and gather from multiple articles
Synthesize answer with wikilink citations
Optionally file the answer back as a new article (compounding!)

Lint (`osk-wiki-lint`)

Health check with 9 checks:

Frontmatter completeness
Index consistency
Orphan detection
Red link analysis
Contradiction detection
Confidence assessment
Exploration gaps
Cross-link density
Naming convention compliance

List (`osk-wiki-list`)

Show all wikis with stats: article count, word count, explored ratio, confidence distribution, last activity.

🤖 Agent and Panels

OSK Wiki Curator agent: Light agent that owns wiki quality, maturity assessment, graduation, and vault bridging. Accumulates knowledge about what works. Always loads osk-wiki-shared.

osk-panel-wiki-review panel: reviews wiki articles from multiple angles:

Wiki Curator (structure) + Researcher (accuracy) + Skeptic (claims) + Beginner (accessibility) + Editor (prose)

osk-panel-wiki-graduate panel: quality-gates graduation candidates:

Wiki Curator (decomposition quality) + Skeptic (claim strength) + Editor (standalone clarity) + Power User (depth/value)

🔄 Workflow Examples

Knowledge Maturation Pipeline

Deepen: osk-wiki-deepen to take draft articles to substantial/mature
Lint: osk-wiki-lint check #10 validates maturity matches content
Graduate: osk-wiki-graduate proposes atomic notes from mature articles
Panel: osk-panel-wiki-graduate reviews candidates before creation
Absorb: osk-wiki-absorb bridges vault notes back into wiki articles
Repeat: Each cycle moves knowledge from AI-maintained wiki to human-owned Zettelkasten

Building a Research Wiki

Create: osk-wiki-create with name "Quantum Computing" and seed topics
Ingest: Drop papers and articles into raw/, run osk-wiki-ingest for each
Explore: osk-wiki-explore to fill gaps and expand stubs
Query: Ask questions, file valuable answers back as articles
Lint: Periodic health checks to maintain quality
Repeat: Each cycle compounds knowledge

Personal Knowledge Wiki

Create wiki from existing vault notes on a topic
The LLM extracts concepts, creates cross-referenced articles
New learning gets ingested; wiki grows organically
Becomes a queryable personal knowledge base

🔧 Integration with Other Systems

AI Assistant System: Wikis are a subsystem; the Wiki Curator agent lives in the agent framework
Learning System: Wiki articles complement literature notes; ingest from Readwise highlights
Zettelkasten System: Wiki articles are NOT permanent notes, but mature articles can be graduated into atomic permanent notes via osk-wiki-graduate. The graduated_notes property tracks provenance
Maintenance System: Wiki lint integrates with vault health checks

🎨 Best Practices

One wiki per topic domain. Don't mix unrelated subjects
Ingest high-quality sources. Garbage in, garbage out; curate your raw/ folder
Review confidence levels. Articles with confidence: low need more sources
Run lint monthly. Catch contradictions and orphans before they accumulate
File query answers back. This is what makes the wiki compound
Use the Index as your entry point. Don't navigate the file system randomly
Let red links guide exploration. They show you where the wiki wants to grow
Don't manually edit wiki articles. Let the LLM maintain them; add your insights as sources

🚧 Common Challenges

Wiki too broad: Keep scope focused; one wiki per research domain
Stale content: Run lint to detect; re-ingest updated sources
Low confidence everywhere: Need more diverse sources; prioritize high-quality ingestion
Too many red links: Normal for young wikis; use explore to fill the most-referenced ones first
Context window limits: Large wikis (400K+ words) may need vector search; the Index helps navigate

🚀 Next Steps

Start small: Create a wiki on a topic you're actively researching
Seed from vault: Use existing permanent and literature notes as starting material
Ingest 5-10 sources: Get enough material for meaningful cross-references
Ask 3 questions: Test the query workflow; file valuable answers back
Run lint once: See how the health check works and what it catches
Expand gradually: Let the wiki grow organically through research and ingestion

OSK Systems:

OSK Reference:

AI Wiki Articles note type
AI Wiki Article template

Concepts and Architecture:

Tutorials:

About Sébastien

I'm Sébastien Dubois, and I'm on a mission to help knowledge workers escape information overload. After 20+ years in IT and seeing too many brilliant minds drowning in digital chaos, I've decided to help people build systems that actually work. Through the Knowii Community, my courses, products & services and my Website/Newsletter, I share practical and battle-tested systems.

I write about Knowledge Work, Personal Knowledge Management, Note-taking, Lifelong Learning, Personal Organization, Productivity, and more. I also craft lovely digital products and tools.

If you want to follow my work, then become a member and join our community.

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