graphify

Convert code, documents, and images into structured knowledge graphs.

Updated Jul 7, 2026

Search Tags:#code analysis #data visualization #knowledge graph #graph theory #semantic analysis

Ready to run

npx skills add https://github.com/billydeeii136/black-opps-claude-skill --skill graphify-billydeeii136

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: graphify
Source: https://github.com/billydeeii136/black-opps-claude-skill/tree/main/agent-skills/graphify
Command: npx skills add https://github.com/billydeeii136/black-opps-claude-skill --skill graphify-billydeeii136

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

The graphify Skill enables users to transform a wide range of inputs, including code, documents, images, and more, into a structured knowledge graph. This helps users discover connections and insights they might not have seen otherwise.

Core Features & Use Cases

Knowledge Graph Creation: Turns diverse inputs into interconnected knowledge graphs.
Community Detection: Identifies clusters of related information.
Multiple Outputs: Generates HTML, JSON, and audit reports for different uses.
Use Case: Suppose you have a repository of research papers and want to understand the underlying concepts and connections. Use graphify to create a knowledge graph that visualizes the relationships between different ideas and papers.

Quick Start

Run 'graphify' on your project directory to create a knowledge graph of your files.

Frequently Asked Questions about graphify

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

FAQPage Schema

How do I convert code and documents into a knowledge graph for analysis?▼

To create a knowledge graph from code, documents, or images, you can use a tool that applies graph theory and semantic analysis to structure the input data. It processes your files to detect communities and visualizes the connections for deeper understanding.

What is the best way to visualize connections in a repository of research papers?▼

Visualizing connections in research papers involves generating a structured knowledge graph that maps relationships between underlying concepts. This approach identifies clusters of related information, helping you discover insights across documents.

Do I need Python and NetworkX to generate knowledge graphs from text inputs?▼

Yes, generating knowledge graphs from text inputs using this method requires a Python environment with specific libraries like NetworkX and Matplotlib. These dependencies are necessary to process the data and render the visualizations.

Can I export my knowledge graph to Neo4j for further analysis?▼

Yes, you can export your knowledge graph to Neo4j for further analysis. The process also supports exporting the generated graph data to HTML and JSON formats, allowing for flexible integration with different platforms.

How does community detection work when mapping relationships in unstructured data?▼

Community detection in unstructured data works by applying graph theory algorithms to identify clusters of highly interconnected nodes. This semantic analysis isolates related concepts within the knowledge graph, revealing hidden structures.