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graphify

Create knowledge graphs from code, documents, and images with community detection.

3|Updated Jun 12, 2026
Search Tags:#data visualization#knowledge graph#information retrieval#codebase analysis#community detection
One-click install
npx skills add https://github.com/whimzyLive/nightshift-ai --skill graphify-whimzylive
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: graphify
Source: https://github.com/whimzyLive/nightshift-ai/tree/main/plugins/sdlc/skills/graphify
Command: npx skills add https://github.com/whimzyLive/nightshift-ai --skill graphify-whimzylive

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill transforms input from various sources, like code, documents, papers, and images, into a knowledge graph, revealing the connections and clusters of concepts within them.

Core Features & Use Cases

  • Knowledge Graph Creation: Build interconnected knowledge graphs from a variety of sources.
  • Community Detection: Identify clusters of related concepts within the graph.
  • Data Output: Generate HTML, JSON, and plain-language audit reports from the graph.
  • Use Case: For a new codebase, create a graph to visualize and understand the architecture and connections before diving into coding.

Quick Start

To create a knowledge graph from the current directory, use: /graphify

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 a codebase to visualize architecture?▼

To create a knowledge graph from a codebase, you can process the directory using this tool to extract concepts and generate interactive HTML and JSON outputs. This visualizes architecture and interconnectedness before coding.

What is community detection and how does it cluster concepts in documents?▼

Community detection is a graph processing technique that identifies clusters of related concepts within a dataset. Applied to documents or code, it groups interconnected ideas to reveal structural relationships.

Do I need a specific Python environment to generate knowledge graphs from images?▼

Yes, generating knowledge graphs from images requires a Python environment and specific graph processing libraries. These dependencies handle the extraction and community detection logic.

What output formats are generated when turning documents into a knowledge graph?▼

Turning documents into a knowledge graph generates interactive HTML for visualization, JSON for data integration, and plain-language audit reports. These outputs detail the discovered clusters and concept relationships.

Can I analyze multiple diverse inputs like code and papers in a single knowledge graph?▼

You can analyze diverse inputs like code, documents, and images together in a single knowledge graph. The tool processes these varied sources to map and cluster interconnected concepts across the entire input set.

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