llm-wiki

Automate building and maintaining an interlinked Markdown knowledge base.

11|Updated May 17, 2026
One-click install
npx skills add https://github.com/StarryCod/cogitum --skill llm-wiki-starrycod
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: llm-wiki
Source: https://github.com/StarryCod/cogitum/tree/main/cogitum/data/skills/research/llm-wiki
Command: npx skills add https://github.com/StarryCod/cogitum --skill llm-wiki-starrycod

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Build a persistent, interlinked Markdown knowledge base that keeps cross-references and provenance intact, reducing manual curation and drift.

Core Features & Use Cases

  • Create and maintain a compounding knowledge base that supports cross-referencing between entities, concepts, and sources.
  • Ingest diverse materials (articles, papers, transcripts) and organize them into a structured wiki accessible via Obsidian, VS Code, or any Markdown editor.
  • Use for long-term research workflows requiring synthesis, consistency, and traceability across multiple sources.

Quick Start

Ingest your first sources and start linking related pages to build a persistent, interconnected knowledge base.

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 interconnected Markdown wiki from multiple research sources?

To build an interconnected Markdown wiki, ingest diverse materials like articles and transcripts to automatically generate cross-linked pages, enforcing frontmatter metadata and provenance tagging for consistent knowledge growth.

What is the best way to maintain provenance and cross-references in a Markdown knowledge base?

Maintaining provenance in a Markdown knowledge base requires automated frontmatter metadata and provenance tagging, supplemented by periodic lint checks to ensure cross-references between entities and concepts remain intact and traceable.

Can I organize ingested research papers into schema-driven layers in Obsidian?

Yes, you can organize ingested research papers into schema-driven layers accessible via Obsidian, VS Code, or any Markdown editor, structuring content to support long-term research workflows and concept synthesis.

Does this knowledge base approach support ongoing synthesis and consistency across long-term research?

Supporting ongoing synthesis and consistency across long-term research is achieved by organizing content into schema-driven layers and running periodic lint checks to prevent manual curation drift and ensure traceability.

What are the limitations of manually curating an interlinked Markdown wiki versus automating it?

Manually curating an interlinked Markdown wiki introduces drift and broken cross-references, whereas automating ingestion, schema-driven organization, and lint checks enforces consistency and traceability across compounding sources.

How do I ingest diverse transcripts and articles into a structured Markdown knowledge base?

Ingest diverse transcripts and articles into a structured Markdown knowledge base by processing raw sources into interlinked pages with enforced frontmatter metadata, automatically cross-referencing entities and concepts for ongoing growth.