graphify-windows

Knowledge graph generation works by extracting entities and relationships from your code, documentation, and media assets using Python-based AST parsing and LLM-driven semantic analysis. It builds a persistent, queryable graph structure that maps semantic connections across your entire corpus.

Yes, you can perform semantic search and cross-document discovery across mixed file types including source code, documentation, and media assets. The Skill constructs a navigable knowledge graph that facilitates project-wide queries and traversal for deep architectural insights.

Yes, you need Python installed, as the Skill utilizes Python-based AST parsing and LLM-driven semantic analysis to perform automated entity extraction and community detection. These dependencies are required to build the persistent, navigable knowledge graph structure.

To map hidden clusters across a codebase, this Skill applies community detection algorithms to the generated knowledge graph. It identifies thematic connections and architectural clusters across your entire project corpus by analyzing extracted entities and their relationships.

Yes, the knowledge graph provides an honest audit trail distinguishing extracted data from inferred data. This ensures transparency in the LLM-driven semantic analysis and maintains an audit-ready data structure for architectural review and compliance.