What problem does it solve? Traditional RAG rediscovers knowledge from scratch on every query, losing cross-references and synthesis work. This Skill maintains a persistent, compounding markdown wiki where sources are ingested once, cross-referenced, and kept consistent over time. ## Core Features & Use Cases - Source Ingestion: Capture URLs, PDFs, and pasted text into an immutable raw/ layer, then create or update entity, concept, and comparison pages with wikilinks and taxonomy-controlled tags. - Knowledge Querying: Answer questions by reading the index and relevant pages, then file valuable syntheses back into the wiki as query or comparison pages. - Wiki Linting: Audit for orphan pages, broken wikilinks, index gaps, stale content, contradictions, and tag taxonomy violations. - Use Case: A researcher tracking AI/ML developments ingests arxiv papers and articles weekly; the wiki accumulates interlinked entity and concept pages, browsable in Obsidian with graph view and Dataview queries. ## Quick Start Create a new LLM wiki at ~/wiki for AI research and ingest this article URL into it.