safishamsi

safishamsi / graphify

Public

AI coding assistant skill (Claude Code, Codex, OpenCode, Cursor, Gemini CLI, OpenClaw, Factory Droid, Trae). Turn any folder of code, docs, papers, or images into a queryable knowledge graph

safishamsi.github.iopenpax.ai claude-code codex graphrag knowledge-graph openclaw
18,332
1,863
100% credibility
Found Apr 06, 2026 at 1,685 stars 8x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Graphify builds interactive knowledge graphs from folders containing code, documents, papers, images, and other files to help understand projects faster.

How It Works

1
📰 Hear about graphify

You learn about a helpful tool that turns messy folders of code, notes, papers, and pictures into a clear map of ideas and connections.

2
🔧 Set it up easily

Follow simple steps to add it to your AI helper, so it's ready whenever you need it.

3
📁 Choose your folder

Pick the project folder full of your files – code, documents, images, anything.

4
✨ Give the command

In your AI chat, just type a short phrase like /graphify and name the folder – it starts exploring right away.

5
⏳ Watch it build the map

It reads every file, spots links between ideas, groups them smartly, and creates a shareable picture.

6
📈 Discover hidden insights

Open the colorful clickable map, read the plain-English report on key hubs, surprises, and smart questions to ask next.

7
🔄 Stay up to date

Turn on auto-refresh so the map updates as you add files or make changes, keeping everything current.

🎉 Unlock your project's secrets

Now you grasp the big picture instantly, chat smarter with your AI, and tackle complex work with confidence.

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Star Growth

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AI-Generated Review

What is graphify?

graphify turns folders of code, papers, images, or notes into queryable knowledge graphs via a free Claude code GitHub skill. Type `/graphify .` in Claude Code to get an interactive HTML viz, JSON for persistent queries, and a report with god nodes, surprising connections, and suggested questions. Multimodal: AST parses code structure deterministically, Claude vision extracts concepts from PDFs/screenshots, all clustered with community detection and tagged EXTRACTED/INFERRED/AMBIGUOUS.

Why is it gaining traction?

Delivers 71x token savings on mixed 50+ file corpora vs raw reads, plus Claude github skills like git hooks for auto-rebuilds on commit and `--watch` for live syncs. Hooks into Claude Code install for PreToolUse prompts that prioritize graphs over grep, making claude github integration seamless—no more token-wasting full scans. Honest audit trails build trust, unlike opaque grabify alternatives.

Who should use this?

Devs onboarding to large Python/JS/Go codebases via Claude Code, AI researchers mixing repos with arXiv PDFs and diagrams, teams doing claude github code review on multimodal projects. Ideal for claude code install in note-heavy workflows like Karpathy's `/raw` folders.

Verdict

Early but clever (252 stars, 1.0% credibility)—pip install graphifyy, graphify install, and test on your codebase. Strong for Claude Code users wanting graph RAG; watch for polish on edge cases before heavy production lift.

(198 words)

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