llm-wiki

To build a persistent markdown knowledge base, the skill ingests raw sources like papers and transcripts, then synthesizes them into structured, interlinked entity and concept pages. It cross-references new information with existing notes to maintain consistency across research sessions.

Maintaining an interlinked wiki requires an agent-driven approach that automatically summarizes, files, and cross-references new information. This agent-driven curation flags contradictions and highlights open questions, preventing the insight loss common in traditional RAG systems.

A self-compounding knowledge base works by automatically cross-referencing newly ingested raw sources with existing markdown notes. Unlike standard RAG alternatives, it evolves into a deepening domain model by actively maintaining interlinked entity pages and concept relationships.

Yes, you can use local or cloud-based storage for persistent note-taking. The markdown wiki requires file system access to manage markdown files and integrate with your storage, ensuring your interlinked knowledge base persists across sessions.

Yes, file system access is required to manage a markdown research wiki. The skill needs this access to create, update, and interlink entity and concept markdown pages within your local or cloud-based storage directories.

An agent-driven wiki relies heavily on file system access for markdown management and consistent local or cloud storage integration. Its effectiveness in building a deepening domain model depends on the continuous ingestion of raw sources to properly synthesize and cross-reference structured pages.