What problem does it solve? Understanding a large codebase, reading list, or mixed research corpus requires reading every file and mentally tracking connections. This Skill turns any folder of code, docs, papers, and images into a persistent, queryable knowledge graph with an honest audit trail showing which relationships were extracted versus inferred. ## Core Features & Use Cases - Knowledge Graph Extraction: Combines deterministic AST parsing for code with parallel LLM subagents for docs, papers, and images, tagging every edge as EXTRACTED, INFERRED, or AMBIGUOUS. - Community Detection & Analysis: Clusters the graph, identifies god nodes and surprising cross-community connections, and generates a plain-language GRAPH_REPORT.md. - Multiple Outputs: Produces an Obsidian vault with canvas, interactive HTML, GraphRAG-ready JSON, plus optional SVG, GraphML, Neo4j, and MCP server exports. - Use Case: Point it at an unfamiliar codebase to understand its architecture before touching anything, or drop papers, tweets, and notes into a folder and query the resulting graph weeks later without re-reading. ## Quick Start Run /graphify on the current directory to build a knowledge graph and open the generated Obsidian vault and report.