graphify

Generate a knowledge graph from a folder of files with semantic analysis and community detection.

Updated Feb 10, 2026

Search Tags:#knowledge graph #file analysis #semantic analysis #graph visualization #community detection

Ready to run

npx skills add https://github.com/ammonfife/sakima.co --skill graphify-ammonfife

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: graphify
Source: https://github.com/ammonfife/sakima.co/tree/main/.claude/skills/graphify
Command: npx skills add https://github.com/ammonfife/sakima.co --skill graphify-ammonfife

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps users create a knowledge graph from a folder of files, uncovering hidden connections and organizing information in a structured format.

Core Features & Use Cases

Knowledge Graph Creation: Turn files into a navigable knowledge graph.
Community Detection: Identify clusters of related concepts.
Output Suite: Generates interactive HTML, JSON, SVG, GraphML, Cypher, and an audit report.
Use Case: For someone analyzing a codebase or research corpus, this Skill can help visualize relationships and connections between different files and concepts.

Quick Start

Run the graphify skill on your current directory to create a knowledge graph from all files within.

Frequently Asked Questions about graphify

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

FAQPage Schema

How do I generate a knowledge graph from a folder of files?▼

Community detection in file analysis works by identifying clusters of related concepts across a directory. It parses various file types like code and documents to uncover hidden connections, organizing information into structured clusters within the generated knowledge graph.

Can I create a knowledge graph that includes images and code files?▼

Yes, you can create a knowledge graph that includes images and code files. The analysis supports various file types including code, documents, and images, allowing you to visualize relationships and connections across a diverse research corpus or codebase.

What output formats are available when building a knowledge graph from documents?▼

Available output formats when building a knowledge graph from documents include interactive HTML, JSON, SVG, GraphML, Cypher, and an audit report. These outputs provide multiple ways to visualize and interact with the detected semantic communities.

Do I need Python dependencies to perform semantic analysis on a directory?▼

Yes, you need Python dependencies to perform semantic analysis on a directory. The parsing and analyzing processes require specific Python libraries to extract concepts, detect communities, and generate the final knowledge graph outputs.