What problem does it solve?
This Skill eliminates the tedious manual bookkeeping that causes most personal knowledge base projects to fail, letting users focus on curating high-quality sources and asking meaningful questions while the LLM handles all wiki maintenance and organization tasks.
Core Features & Use Cases
- Wiki Initialization: Automatically sets up the complete directory structure, schema, and global registry for new personal or project-scoped knowledge bases.
- Source Ingestion: Processes raw articles, papers, notes, and other sources into structured, cross-referenced wiki pages with citations and confidence ratings.
- Knowledge Querying: Synthesizes answers to user questions by pulling insights from across the wiki, with proper citations to relevant pages.
- Wiki Health Checks: Runs linting to find contradictions, orphan pages, missing concepts, and stale content to keep the wiki accurate and useful.
A common use case is a researcher building a machine learning research wiki: they can drop papers and articles into the raw/ folder, run ingestion to have the LLM create structured wiki pages, then query the wiki for synthesized insights on specific topics like gradient descent or transformer architectures.
Quick Start
Use the llm-wiki skill to set up a new personal knowledge base for my machine learning research topic and ingest the three papers I have in my downloads folder.
