What problem does it solve? Understanding a large codebase or mixed document corpus requires reading hundreds of files manually, and AI assistants lose that context between sessions. This Skill turns any folder into a persistent knowledge graph with community detection and an honest audit trail, so codebase questions can be answered by querying the graph instead of re-reading files. ## Core Features & Use Cases - Knowledge graph extraction: Combines deterministic AST parsing for code with LLM-based semantic extraction for docs, papers, and images, tagging every edge as EXTRACTED, INFERRED, or AMBIGUOUS. - Query, path, and explain tools: Answer natural-language questions with BFS/DFS traversal, find shortest paths between concepts, and get plain-language explanations of any node. - 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. - Incremental updates and automation: Supports --update for changed files only, --watch for auto-rebuilds, GitHub repo cloning, and cross-repo graph merging. - Use Case: Point it at a monorepo or several GitHub URLs, then ask "How does authentication flow reach the database?" and get an answer traced through the merged cross-repo graph. ## Quick Start Run /graphify on the current directory to build the knowledge graph, then ask any codebase question to query it.