graphify

Analyze a directory of files and extract cross-file relationships into a knowledge graph.

3|Updated Nov 15, 2015

Search Tags:#knowledge_graph #code_analysis #graphify #document_relationships #audit_trail #html_output #json_output

Ready to run

npx skills add https://github.com/itsdaiego/dotfiles --skill graphify-itsdaiego

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

graphify helps you turn any folder of files into a structured, navigable knowledge graph with clustering, an audit trail, and a multi-output workflow (HTML, JSON, and a readable report).

Core Features & Use Cases

Persistent graph: stores relationships across sessions in graphify-out/graph.json.
Honest audit trail: edges are tagged EXTRACTED, INFERRED, or AMBIGUOUS for traceability.
Cross-document connectivity: discovers connections across code, docs, papers, and images to reveal hidden relationships.
Outputs: HTML visualization, GraphRAG-ready JSON, and a plain-language GRAPH_REPORT.md.

Quick Start

Run graphify on a directory to generate a navigable knowledge graph and a comprehensive report.

Frequently Asked Questions about graphify

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

FAQPage Schema

How do I build a knowledge graph from a directory of code and documents?▼

You can build a knowledge graph by analyzing a directory of code, docs, and media to extract cross-file relationships. The process outputs an HTML visualization, GraphRAG-ready JSON, and a readable report for exploration.

What is the best way to visualize cross-file relationships in a codebase?▼

Visualizing cross-file relationships involves extracting connections across code, docs, and media into an HTML output. This reveals hidden relationships and provides a navigable format for exploring the corpus.

How does an audit trail work for inferred relationships in a knowledge graph?▼

An audit trail for inferred relationships works by annotating graph edges as EXTRACTED, INFERRED, or AMBIGUOUS. This preserves provenance and ensures reproducibility by clearly identifying the origin of each connection.

Can I generate GraphRAG-ready JSON from a folder of mixed file types?▼

Yes, you can generate GraphRAG-ready JSON from a folder of mixed code, docs, and media. The analysis discovers cross-document connectivity and outputs structured JSON alongside an HTML visualization and a markdown report.

Does graphify require any external dependencies to analyze code and documents?▼

No, graphify does not require any external dependencies to analyze code and documents. It operates independently to extract cross-file relationships and generate its HTML, JSON, and markdown report outputs.

Why are some edges in my generated graph marked as AMBIGUOUS?▼

Edges in a generated graph are marked as AMBIGUOUS to indicate that the relationship between files could not be definitively extracted or inferred. This tagging preserves provenance and enables reproducibility during cross-reference discovery.