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BarrettLowe
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graphify

Convert code, documents, papers, and images into structured knowledge graphs with community detection.

Updated Mar 7, 2016
Search Tags:#data visualization#knowledge graph#text analysis#semantic analysis#community detection
One-click install
npx skills add https://github.com/BarrettLowe/dotfiles --skill graphify-barrettlowe
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: graphify
Source: https://github.com/BarrettLowe/dotfiles/tree/main/claude/skills/graphify
Command: npx skills add https://github.com/BarrettLowe/dotfiles --skill graphify-barrettlowe

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill converts diverse inputs such as code, documents, papers, and images into a structured knowledge graph with community detection and various outputs like HTML, JSON, and an audit report.

Core Features & Use Cases

  • Knowledge Graph Creation: Transform files into a graph of interconnected concepts.
  • Community Detection: Identify clusters of related information.
  • Output Generation: Generate interactive HTML, JSON, and a detailed report.
  • Use Case: Imagine you have a large collection of research papers. Use this Skill to automatically create a knowledge graph that visualizes connections between ideas and communities of related work.

Quick Start

Run the 'graphify' command in your terminal to initiate the knowledge graph creation process.

Frequently Asked Questions about graphify

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

FAQPage Schema
How do I create a knowledge graph from research papers and documents?▼

To create a knowledge graph from research papers and documents, you can convert diverse inputs like code, documents, and images into a structured graph of interconnected concepts using automated community detection and interactive HTML visualizations.

Can I use Python networkx and igraph for text analysis and semantic clustering?▼

Yes, you can use Python networkx and igraph libraries for semantic analysis and clustering. This Skill utilizes these specified graph analysis dependencies to process text, apply community detection, and output structured visualizations.

What is the best way to generate interactive data visualizations from code and text analysis?▼

The best way to generate interactive data visualizations from code and text analysis is to transform inputs into a structured knowledge graph and export the results as interactive HTML, JSON files, and detailed audit reports.

What file formats and outputs can I generate from a knowledge graph?▼

You can generate interactive HTML visualizations, structured JSON data, and detailed audit reports from your knowledge graph. These outputs map the interconnected concepts and clustered communities detected during the semantic analysis.

Do I need a specific Python environment to run graphify for community detection?▼

Yes, you need a Python environment with specified graph analysis and visualization libraries installed, including networkx, python-igraph, graphifyy, and matplotlib, to execute the community detection and HTML export processes.

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