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llm-wiki

Create and maintain an interlinked markdown knowledge base with YAML frontmatter.

Updated Apr 12, 2026
Search Tags:#markdown#research#wiki#knowledge-base#cross-reference#ingest
One-click install
npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill llm-wiki-daddyelonmusk69
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-wiki
Source: https://github.com/DaddyElonMusk69/motis-agent/tree/main/skills/research/llm-wiki
Command: npx skills add https://github.com/DaddyElonMusk69/motis-agent --skill llm-wiki-daddyelonmusk69

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Researchers and knowledge workers spend excessive time manually organizing scattered sources into coherent notes. This Skill automates the creation of a persistent, interlinked markdown wiki, turning raw articles, papers, and transcripts into structured, searchable knowledge without needing external databases.

Core Features & Use Cases

  • Ingest Sources: Capture web articles, PDFs, and meeting notes into a raw layer and automatically generate summarized entity and concept pages.
  • Cross‑Reference & Tag: Enforce a taxonomy, add wikilinks between pages, and maintain consistent frontmatter metadata.
  • Query & Lint: Retrieve information from the wiki with natural language queries and run health checks to detect orphan pages, broken links, or stale content.
  • Use Cases: Building an AI research repository, maintaining product intelligence notes, or creating a personal academic study wiki.

Quick Start

Ask the assistant to create a new LLM Wiki in ~/wiki and ingest the article https://example.com/ai-research.

Frequently Asked Questions about llm-wiki

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a persistent interlinked markdown wiki from research sources?▼

A markdown wiki ingests web articles, PDFs, and transcripts into a raw layer to automatically generate summarized entity and concept pages. It organizes sources into structured, searchable knowledge without requiring external databases.

Can I maintain YAML frontmatter and cross-references in a markdown knowledge base?▼

Yes, a markdown knowledge base enforces taxonomy, adds wikilinks between pages, and maintains consistent YAML frontmatter metadata. It runs health checks to detect orphan pages, broken links, and stale content during cross-referenced updates.

Do I need an external database to organize markdown notes and research papers?▼

No, organizing markdown notes and research papers does not require an external database. This approach satisfies file system organization and cross-referenced markdown updates directly through a persistent local structure.

What is the best way to query a markdown wiki using natural language?▼

The best way to query a markdown wiki is using natural language queries to retrieve information from the structured knowledge base. The system processes queries against cross-referenced markdown pages to return relevant entity and concept data.

How do I ingest web articles and PDFs into a markdown knowledge base?▼

To ingest web articles and PDFs into a markdown knowledge base, capture sources into a raw layer. The system automatically processes these inputs to generate summarized entity and concept pages with cross-referenced markdown updates.

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