What problem does it solve? Understanding a large codebase or mixed corpus of documents 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 project content can be answered by querying the graph instead of re-reading everything. ## 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 audit trail. - 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 visualization, 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, a git post-commit hook, and cross-repo graph merging. - Use Case: Point it at a monorepo or a GitHub URL, then ask "How does the auth flow reach the database?" and get an answer traced through the graph with source citations. ## Quick Start Run /graphify on the current directory to build the knowledge graph, then ask any question about the codebase to query it.
