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Llm Wiki

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bighardperson
llm-wiki

Build and maintain a personal knowledge base (wiki) using LLMs. Instead of RAG-style retrieval, the LLM incrementally compiles, cross-references, and maintains a persistent structured wiki from raw sources. Use when user wants to create a knowledge base, build a personal wiki, organize research notes, ingest documents into a structured wiki, or maintain a living knowledge repository.

Overview

Publisherbighardperson
Repositorycomputer-science-skills-collection
Skill namellm-wiki
Stars
66
Forks
10
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Llm Wiki 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/bighardperson/computer-science-skills-collection/tree/main/skills/Karpathy%20LLM%20Wiki
  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.

Use it in TypingMind

Enable Llm Wiki 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 Llm Wiki 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 Llm Wiki 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.

Karpathy LLM Wiki

A pattern for building personal knowledge bases using LLMs.

Based on Andrej Karpathy's LLM Wiki.

The Core Idea

Instead of RAG (retrieve-on-every-query), the LLM incrementally builds and maintains a persistent wiki — a structured, interlinked collection of markdown files. When you add a new source, the LLM reads it, extracts key information, and integrates it into the existing wiki — updating entity pages, revising topic summaries, noting contradictions, and strengthening the evolving synthesis.

The wiki is a persistent, compounding artifact. Cross-references are already there. Contradictions have been flagged. Synthesis reflects everything you've read. It gets richer with every source and every question.

You never write the wiki yourself — the LLM writes and maintains all of it. You're in charge of sourcing, exploration, and asking the right questions.

Architecture (Three Layers)

1. Raw Sources

Your curated collection of source documents — articles, papers, images, data files. These are immutable. The LLM reads from them but never modifies them. This is your source of truth.

2. The Wiki

A directory of LLM-generated markdown files — summaries, entity pages, concept pages, comparisons, overview, synthesis. The LLM owns this layer entirely: creates pages, updates them when new sources arrive, maintains cross-references, keeps everything consistent. You read it; the LLM writes it.

3. The Schema

A configuration document (this skill) that tells the LLM the wiki's structure, conventions, and workflows. You and the LLM co-evolve this over time.

Operations

Ingest

User drops a new source. The LLM:

  1. Reads the source and discusses key takeaways
  2. Writes a summary page in the wiki
  3. Updates the index
  4. Updates relevant entity and concept pages across the wiki
  5. Appends an entry to the log

A single source may touch 10-15 wiki pages. Prefer ingesting one source at a time for quality.

Query

Ask questions against the wiki. The LLM:

  1. Reads index.md to find relevant pages
  2. Reads those pages and synthesizes an answer with citations
  3. Good answers get filed back into the wiki as new pages — comparisons, analyses, connections should be persisted, not lost in chat history

Lint (Health Check)

Periodically health-check the wiki. Look for:

  • Contradictions between pages
  • Stale claims superseded by newer sources
  • Orphan pages with no inbound links
  • Important concepts mentioned but lacking their own page
  • Missing cross-references
  • Data gaps that could be filled with a web search

Default Location

The wiki lives at a user-level path so it's accessible from any workspace or conversation:

~/.workbuddy/wiki-knowledge/

This is the default. The LLM should:

  1. Always check ~/.workbuddy/wiki-knowledge/ first when the user mentions their wiki, regardless of the current workspace.
  2. If the directory exists and contains WIKI-SCHEMA.md, use it directly — no need to ask the user for a path.
  3. Only ask for a custom path if the user explicitly wants a separate wiki.

Getting Started

When a user wants to create a wiki, follow these steps:

Step 1: Initialize Structure

Create at ~/.workbuddy/wiki-knowledge/ (or a user-specified path):

wiki-knowledge/
├── raw/              # Immutable source documents
│   └── assets/       # Downloaded images
├── wiki/             # LLM-maintained markdown files
│   ├── index.md      # Content catalog
│   └── log.md        # Chronological operation log
└── WIKI-SCHEMA.md    # Schema & conventions (co-evolve with user)

Step 2: Create index.md

A catalog of everything in the wiki — each page listed with a link, a one-line summary, and metadata (date, source count). Organized by category. The LLM updates it on every ingest.

Step 3: Create log.md

Append-only chronological record. Each entry format:

markdown
## [YYYY-MM-DD] operation_type | Title
Brief description of what was done.

Use consistent prefixes so it's parseable with grep:

bash
grep "^## \[" log.md | tail -5

Step 4: Create WIKI-SCHEMA.md

A configuration file describing:

  • Directory structure and conventions
  • Page format templates
  • Cross-referencing rules
  • Domain-specific categories
  • Workflows for ingest/query/lint

Customize this with the user for their specific domain.

Page Format Template

markdown
---
title: Page Title
type: entity | concept | source-summary | comparison | synthesis
created: YYYY-MM-DD
updated: YYYY-MM-DD
sources: [list of source filenames]
tags: [relevant tags]
---

# Page Title

Content here. Use [[wiki-links]] for cross-references.

## See Also
- [[related-page-1]]
- [[related-page-2]]

Use Cases

DomainRaw SourcesWiki Pages
PersonalJournal entries, articles, podcast notesGoals, health tracking, self-improvement
ResearchPapers, articles, reportsEntities, concepts, evolving thesis
Book readingChapter notesCharacters, themes, plot threads
BusinessSlack threads, meeting transcripts, docsInternal knowledge base
Competitive analysisCompany reports, newsCompetitor profiles, comparisons
Course notesLecture notes, readingsTopic summaries, concept maps

Tips

  • Obsidian works great as the wiki viewer — graph view shows connections, Dataview plugin queries frontmatter metadata
  • The wiki is just a git repo of markdown files — version history, branching, and collaboration for free
  • Marp can generate slide decks from wiki content
  • Web Clipper (Obsidian plugin) converts web articles to markdown for easy source ingestion
  • Download images locally for persistence — set a fixed attachment directory

Important Principles

  1. Raw sources are immutable — never modify source documents
  2. The LLM owns the wiki — users read, LLM writes and maintains
  3. Knowledge compounds — every ingest and query enriches the wiki
  4. File good answers back — don't let valuable analysis disappear into chat
  5. Regular lint passes — keep the wiki healthy as it grows

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 Llm Wiki AI skill do?

Build and maintain a personal knowledge base (wiki) using LLMs. Instead of RAG-style retrieval, the LLM incrementally compiles, cross-references, and maintains a persistent structured wiki from raw sources. Use when user wants to create a knowledge base, build a personal wiki, organize research notes, ingest documents into a structured wiki, or maintain a living knowledge repository.

Why use Llm Wiki on TypingMind?

Because you install it once and use it with any model. Llm Wiki 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 Llm Wiki in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bighardperson/computer-science-skills-collection/tree/main/skills/Karpathy%20LLM%20Wiki. 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 Llm Wiki?

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 Llm Wiki?

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

Is the Llm Wiki AI skill free?

It is published on GitHub by bighardperson. Check the repository for licensing terms. 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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