graphify-windows

To build a knowledge graph from a codebase, run /graphify <path> on your target directory. This process extracts semantic data from code, documents, and research papers, transforming unstructured content into a persistent graph for in-depth exploration and connection discovery.

Community detection in document analysis identifies and explores cross-document connections and communities within a knowledge graph. It groups related concepts across structured and unstructured data sources, facilitating complex information analysis and connection discovery.

Yes, creating graph visualizations from research papers requires Python and the networkx library, along with matplotlib and graphviz. These dependencies are essential for data extraction, semantic analysis, and rendering the interactive knowledge graphs.

Yes, you can extract knowledge graphs from unstructured PDF documents using the pdfplumber dependency. The Skill processes diverse content types, including images and papers, to facilitate complex information analysis and connection discovery.

The best way to explore cross-document connections is by constructing a persistent knowledge graph from your project's codebase and dependencies. Using built-in query tools, you can extract insights and discover relationships across the generated semantic graph.

Limitations when applying community detection to large codebases depend on the networkx graph capacity and local processing constraints. While the Skill handles semantic analysis for complex connection discovery, extremely large directories may impact rendering performance.