graphify

To turn a directory of documents into a knowledge graph, this Skill processes your current directory using Python to extract structures and generate a navigable graph. It applies semantic analysis to map relationships across diverse files.

Community detection is a graph analysis technique that identifies clusters of related concepts within your files. It helps file analysis by grouping interconnected ideas, making it easier to explore large codebases or research corpora.

You can generate a knowledge graph from diverse inputs including code, documents, and images. The Skill uses file detection and structural extraction to process these varied formats into a unified graph representation.

Creating a knowledge graph yields interactive HTML, JSON, and a plain-language audit report. These outputs provide visual navigation, machine-readable structural data, and a human-readable summary of the detected communities.

Python is required as the underlying mechanism for graph analysis and codebase understanding. The Skill utilizes Python libraries to perform structural extraction, semantic analysis, and graph visualization on your input directory.

The best way to analyze relationships across a research corpus is converting the documents into a knowledge graph with community detection. This approach reveals structural connections and clusters of related academic ideas efficiently.