graphify-windows

Construct persistent knowledge graphs from diverse inputs using Python's graphify library.

Updated May 14, 2026

Search Tags:#code analysis #knowledge graph #document analysis #graph visualization #community detection

Ready to run

npx skills add https://github.com/ChristianVonGebhardi/autonomous-problem-solver --skill graphify-windows-christianvongebhardi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: graphify-windows
Source: https://github.com/ChristianVonGebhardi/autonomous-problem-solver/tree/main/.claude/skills/graphify
Command: npx skills add https://github.com/ChristianVonGebhardi/autonomous-problem-solver --skill graphify-windows-christianvongebhardi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires graphify, networkx, node2vec, matplotlib, seaborn, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill transforms diverse inputs like code, documents, papers, images, and videos into a navigable knowledge graph, making complex relationships and connections easily accessible.

Core Features & Use Cases

Knowledge Graph Creation: Convert any collection of files into a persistent knowledge graph with god nodes, community detection, and query tools.
Interactive Visualization: Explore the graph visually with community detection and query/path/explain tools.
Use Case: Consider a project repository. Use this Skill to visualize and analyze the relationships between code files, documentation, and external references, creating a comprehensive understanding of the project.

Quick Start

Run the graphify skill on your project's root directory with the command: /graphify .

Frequently Asked Questions about graphify-windows

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate a knowledge graph from a project directory?▼

To generate a knowledge graph from a project directory, run the graphify tool on your root folder. It transforms diverse inputs like code and documents into a persistent graph with community detection and query tools.

What is a persistent knowledge graph and how does it help with code analysis?▼

A persistent knowledge graph maps complex relationships between files, making navigation and code analysis easier. It extracts semantic connections from your repository to provide a comprehensive visual understanding of the project structure.

Can I visualize communities and query paths within my knowledge graph?▼

Yes, you can visualize communities and query paths within your knowledge graph. The tool supports interactive visualization using matplotlib and seaborn, allowing you to explore extracted relationships and run path queries.

Does graphify work with networkx and matplotlib for graph visualization?▼

Yes, graphify works with networkx and matplotlib for graph visualization. These dependencies enable community detection, node2vec embeddings, and interactive visual rendering of your extracted document and code relationships.

What types of files can I use for document analysis and knowledge graph creation?▼

You can use code, documents, papers, images, and videos for document analysis and knowledge graph creation. The tool extracts semantic connections from these diverse inputs to build a navigable map of your project.