graphify

Convert files into a knowledge graph with community detection and HTML, JSON, and Obsidian outputs.

Updated Sep 10, 2026
One-click install
npx skills add https://github.com/longqt321/Rendez --skill graphify-longqt321
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: graphify
Source: https://github.com/longqt321/Rendez/tree/main/.agent/skills/graphify
Command: npx skills add https://github.com/longqt321/Rendez --skill graphify-longqt321

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires graphifyy, networkx.

What problem does it solve? Understanding a large codebase, reading list, or mixed research corpus requires reading every file and mentally tracking connections. This Skill turns any folder of code, docs, papers, and images into a persistent, queryable knowledge graph with an honest audit trail showing which relationships were extracted versus inferred. ## Core Features & Use Cases - Knowledge Graph Extraction: Combines deterministic AST parsing for code with parallel LLM subagents for docs, papers, and images, tagging every edge as EXTRACTED, INFERRED, or AMBIGUOUS. - Community Detection & Analysis: Clusters the graph, identifies god nodes and surprising cross-community connections, and generates a plain-language GRAPH_REPORT.md. - Multiple Outputs: Produces an Obsidian vault with canvas, interactive HTML, GraphRAG-ready JSON, plus optional SVG, GraphML, Neo4j, and MCP server exports. - Use Case: Point it at an unfamiliar codebase to understand its architecture before touching anything, or drop papers, tweets, and notes into a folder and query the resulting graph weeks later without re-reading. ## Quick Start Run /graphify on the current directory to build a knowledge graph and open the generated Obsidian vault and report.

Frequently Asked Questions about graphify

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

FAQPage Schema
How do I turn a codebase into a knowledge graph?

Run /graphify on the project directory. It parses code files with AST extraction for imports and structure, then uses parallel subagents for semantic relationships, and outputs an interactive HTML graph, JSON, and an Obsidian vault.

What file types can be extracted into a knowledge graph?

The pipeline detects code files, Markdown and text documents, PDF papers, and images. Images are interpreted with vision, so screenshots, charts, diagrams, and whiteboard photos become nodes with typed relationships.

How do I update a knowledge graph when files change?

Use the --update flag for incremental re-extraction. A saved manifest tracks file state, so only new or modified files are re-processed and merged into the existing graph, saving tokens and time.

Can I export a knowledge graph to Neo4j or Gephi?

Yes. The --neo4j flag generates a Cypher file, --neo4j-push writes directly to a running Neo4j instance via bolt, and --graphml exports a GraphML file compatible with Gephi and yEd.

Why does graph extraction produce an empty graph?

An empty graph means extraction produced no nodes, usually because all files were skipped, the corpus contains only unsupported binary files, or extraction failed. Check the detection summary for skipped or unsupported files.

How are inferred relationships distinguished from extracted ones?

Every edge carries a confidence tag: EXTRACTED for relationships explicit in the source, INFERRED for reasonable deductions, and AMBIGUOUS for uncertain cases flagged for review. The audit report separates these categories.