graphify

Convert code, documents, images, and videos into persistent knowledge graphs.

Updated May 10, 2026
One-click install
npx skills add https://github.com/AhmadGehad/lfms --skill graphify-ahmadgehad
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: graphify
Source: https://github.com/AhmadGehad/lfms/tree/main/.config/kilo/skills/graphify
Command: npx skills add https://github.com/AhmadGehad/lfms --skill graphify-ahmadgehad

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill turns any input (code, docs, papers, images, videos) into a persistent knowledge graph, enabling users to explore and analyze complex relationships and information.

Core Features & Use Cases

  • Knowledge Graph Creation: Convert diverse input types into a structured knowledge graph with nodes and edges.
  • Community Detection: Identify and analyze communities of related nodes and concepts.
  • Query & Analysis: Use queries to explore the graph and generate insights.
  • Use Case: Imagine you have a large codebase and you want to understand the relationships between different components. Use this Skill to generate a knowledge graph and query it to find out which functions call each other or which classes are related.

Quick Start

Use the graphify skill to analyze the codebase at the path '/path/to/codebase'.

Frequently Asked Questions about graphify

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

FAQPage Schema
How do I visualize codebase relationships as a knowledge graph?

To visualize codebase relationships as a knowledge graph, convert your source code into structured nodes and edges. This Skill transforms code inputs into a persistent graph, enabling you to explore which functions call each other and how classes are related through querying.

What is knowledge graph creation from diverse documents and media?

Knowledge graph creation from diverse documents and media is the process of structuring varied inputs like text, images, and videos into interconnected nodes and edges. It enables persistent exploration and analysis of complex relationships across different information formats.

Do I need Python installed to build a knowledge graph from documents?

Yes, you need Python installed to build a knowledge graph from documents. This Skill relies on the graphifyy Python library to process inputs, construct the persistent graph structure, and perform community detection and querying.

How do I analyze large codebases for component dependencies?

You can analyze large codebases for component dependencies by converting the codebase into a knowledge graph. Once graphed, use queries to explore the structure and identify which functions call each other or how classes are related.

Can I detect communities of related concepts in a knowledge graph?

Yes, you can detect communities of related concepts in a knowledge graph. This Skill supports community detection features that identify and analyze clusters of related nodes within your structured code or document data.

What's the best way to organize unstructured documents into a queryable format?

The best way to organize unstructured documents into a queryable format is transforming them into a persistent knowledge graph. This converts text and media into structured nodes and edges, allowing you to query relationships and generate insights.