lewislulu/llm-wiki-skill

Registry

Summary

A Karpathy-style LLM knowledge base Agent Skill for OpenClaw/Codex, designed to provide an experimental, iteratively improving wiki-like knowledge retrieval system for large language models.

Key features

• Karpathy-inspired knowledge base design
• Agent Skill integration for OpenClaw/Codex
• Experimental with iterative improvements
• LLM-focused wiki-style retrieval
• TypeScript implementation

Use cases

• Building LLM-powered wiki agents
• Experimental knowledge retrieval for AI assistants
• Rapid prototyping of knowledge base skills
• Learning Karpathy-style AI architectures

README excerpt

# llm-wiki **An OpenClaw / Codex Agent Skill for building Karpathy-style LLM knowledge bases.** > Experimental skill — will iterate over time. > Please send your feedbacks in github issues. Inspired by [Andrej Karpathy's llm-wiki Gist](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) and the community's work building on it. ## What this is Instead of RAG (re-retrieving raw docs on every query), this pattern has the LLM **compile** raw sources into a persistent, cross-linked Markdown wiki. Every `compile`, `ingest`, `query`, `lint`, and `audit` pass makes the wiki richer. Knowledge compounds over time. - You own: sourcing raw material, asking good questions, steering direction, filing feedback on things the AI got wrong. - LLM owns: all writing, cross-referencing, filing, bookkeeping, and acting on your feedback. The skill comes with two companion tools in this repo: - **`plugins/obsidian-audit/`** — an Obsidian plugin: select text in any page, leave a comment with severity, the comment is written into `audit/` as an anchored markdown file. - **`web/`** — a local Node.js preview server: renders the wiki with mermaid, KaTeX, and wikilinks, lets you select + file feedback from the browser, and shows open audits per page. Both tools share a single TypeScript library (`audit-shared/`) so audit files written from Obsidian and the web viewer are byte-identical in shape. ## Install ```bash # Copy the skill into your agent's skills directory cp -r llm-wiki/ ~/.claude/skills/llm-wiki/ # or for Codex cp -r llm-wiki/ ~/.codex/skills/llm-wiki/ ``` Then reference it in your agent config, or simply paste `llm-wiki/SKILL.md` into your agent context. ## Quick start ```bash # 1. Scaffold a new wiki python3 llm-wiki/scripts/scaffold.py ~/my-wiki "My Researc

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