llm-wiki

Name: llm-wiki
Availability: InStock
Author: cryptopafi

Ingest sources into a persistent Markdown wiki with Obsidian-style wikilinks.

1|Updated Apr 12, 2026

Search Tags:#markdown #llm #ingestion #wiki #lint #knowledge-base #wikilinks

Ready to run

npx skills add https://github.com/cryptopafi/nexusos-skills --skill llm-wiki-cryptopafi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llm-wiki
Source: https://github.com/cryptopafi/nexusos-skills/tree/main/research/llm-wiki
Command: npx skills add https://github.com/cryptopafi/nexusos-skills --skill llm-wiki-cryptopafi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Build and maintain a persistent, interlinked markdown knowledge base that centralizes sources, notes, and queries for research workflows.

Core Features & Use Cases

Ingest sources (articles, transcripts, papers) and create cross-linked pages.
Query and synthesize compiled knowledge with consistent linting and schema adherence.
Maintain a scalable knowledge graph with Obsidian-style wikilinks and YAML frontmatter metadata.

Quick Start

Initialize a wiki workspace and start ingesting your first sources to build the interlinked knowledge graph.

Frequently Asked Questions about llm-wiki

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

FAQPage Schema

How do I build an interlinked markdown knowledge base from research papers and notes?▼

Build an interlinked markdown knowledge base by ingesting raw sources like papers and notes, then generating wiki pages with Obsidian-style wikilinks. The system applies a three-layer architecture with YAML frontmatter to maintain a persistent, cross-linked research graph.

What is the best way to maintain a persistent knowledge wiki as sources grow?▼

Maintaining a persistent knowledge wiki requires consistent linting and schema adherence. The system audits the knowledge base using a structured index and log system alongside YAML frontmatter metadata to ensure the graph remains scalable and accurately interlinked over time.

Can I use Obsidian-style wikilinks and YAML frontmatter for automated knowledge graph ingestion?▼

Yes, you can use Obsidian-style wikilinks and YAML frontmatter for automated knowledge graph ingestion. The system specifically implements this metadata structure to ingest articles and arXiv papers, creating cross-linked pages that form a structured, queryable research wiki.

How do I ingest arXiv papers and transcripts into a markdown wiki with cross-linked pages?▼

Ingest arXiv papers and transcripts into a markdown wiki by processing them through a three-layer architecture: raw sources, wiki pages, and a schema. The system automatically creates cross-linked pages using Obsidian-style wikilinks and logs the ingestion in a structured index.

Does this knowledge base linting approach require any external dependencies or components?▼

No external dependencies or components are required for this knowledge base linting approach. The system operates independently to apply schema adherence and linting rules to your markdown wiki, ensuring consistent YAML frontmatter and valid wikilinks without external integrations.

Why should I use a three-layer architecture for my markdown research wiki instead of flat notes?▼

A three-layer architecture separates raw sources, generated wiki pages, and the schema, preventing data loss and structural drift. This structured approach enables reliable linting, consistent YAML frontmatter metadata, and accurate wikilinks compared to managing flat, disconnected markdown notes.