What problem does it solve? Understanding a large codebase or mixed document corpus requires reading hundreds of files manually. This Skill turns any folder of code, docs, papers, images, or videos into a persistent knowledge graph with community detection, so questions about architecture, file relationships, and cross-document connections can be answered by graph traversal instead of re-reading files. ## Core Features & Use Cases - Knowledge graph construction: Extracts entities and relationships via AST parsing for code and LLM-based semantic extraction for docs, papers, and images, with an honest EXTRACTED/INFERRED/AMBIGUOUS confidence audit trail. - Query, path, and explain tools: Answers natural-language questions with BFS/DFS traversal, finds shortest paths between concepts, and explains individual nodes with source citations. - Multiple outputs and integrations: Generates interactive HTML, GraphRAG-ready JSON, GRAPH_REPORT.md, Obsidian vaults, wikis, and exports to Neo4j, FalkorDB, SVG, GraphML, or an MCP server. - Use Case: Point it at a cloned GitHub repository, then ask "How does the authentication flow reach the database?" and get a graph-traversal answer with source file citations instead of grepping through the repo. ## Quick Start Run /graphify on the current project directory to build the knowledge graph, then ask any codebase question to query it.