A small mixed-input corpus: Python source files, a markdown paper with arXiv citations, and one image. Tests graphify on different file types in a single run.

Corpus (5 files)

raw/
├── analyze.py          — graph analysis module (god_nodes, surprising_connections)
├── build.py            — graph builder (build_from_json, NetworkX wrapper)
├── cluster.py          — Leiden community detection (cluster, score_all)
├── attention_notes.md  — Transformer paper notes (Vaswani et al., 2017) with arXiv citation

Note: the original benchmark included attention_arabic.png (an Arabic-language figure from the Attention paper). PNG files are not stored in this repo. To reproduce with the image, save any diagram from the Attention Is All You Need paper as raw/attention_arabic.png.

How to run

pip install graphifyy

graphify install                        # Claude Code
graphify install --platform codex       # Codex
graphify install --platform opencode    # OpenCode
graphify install --platform claw        # OpenClaw

Then open your AI coding assistant in this directory and type:

/graphify ./raw

What to expect

~20 nodes, ~19 edges from AST alone (3 Python modules)
3 communities: Graph Analysis, Clustering and Scoring, Graph Building
God nodes: analyze.py, cluster.py, build.py
attention_notes.md classified as paper (arXiv heuristic fires on 1706.03762)
If you include the image: 1 extra node describing the figure content via vision
Token reduction: 5.4x

Actual output is in this folder: GRAPH_REPORT.md and graph.json. Full eval: review.md.