graphify

graphify

A Claude Code skill. Type /graphify in Claude Code - it reads your files, builds a knowledge graph, and gives you back structure you didn't know was there.

Fully multimodal. Drop in code, PDFs, markdown, screenshots, diagrams, whiteboard photos, even images in other languages - graphify uses Claude vision to extract concepts and relationships from all of it and connects them into one graph.

Andrej Karpathy keeps a /raw folder where he drops papers, tweets, screenshots, and notes. graphify is the answer to that problem - 71.5x fewer tokens per query vs reading the raw files, persistent across sessions, honest about what it found vs guessed.

/graphify .                        # works on any folder - your codebase, notes, papers, anything

graphify-out/
├── graph.html       interactive graph - click nodes, search, filter by community
├── obsidian/        open as Obsidian vault
├── wiki/            Wikipedia-style articles for agent navigation (--wiki)
├── GRAPH_REPORT.md  god nodes, surprising connections, suggested questions
├── graph.json       persistent graph - query weeks later without re-reading
└── cache/           SHA256 cache - re-runs only process changed files

Install

Requires: Claude Code and Python 3.10+

pip install graphifyy && graphify install

The PyPI package is temporarily named graphifyy while the graphify name is being reclaimed. The CLI and skill command are still graphify.

Then open Claude Code in any directory and type:

/graphify .

mkdir -p ~/.claude/skills/graphify
curl -fsSL https://raw.githubusercontent.com/safishamsi/graphify/v1/skills/graphify/skill.md \
  > ~/.claude/skills/graphify/SKILL.md

Add to ~/.claude/CLAUDE.md:

- **graphify** (`~/.claude/skills/graphify/SKILL.md`) - any input to knowledge graph. Trigger: `/graphify`
When the user types `/graphify`, invoke the Skill tool with `skill: "graphify"` before doing anything else.

Usage

/graphify                          # run on current directory
/graphify ./raw                    # run on a specific folder
/graphify ./raw --mode deep        # more aggressive INFERRED edge extraction
/graphify ./raw --update           # re-extract only changed files, merge into existing graph

/graphify add https://arxiv.org/abs/1706.03762        # fetch a paper, save, update graph
/graphify add https://x.com/karpathy/status/...       # fetch a tweet

/graphify query "what connects attention to the optimizer?"
/graphify path "DigestAuth" "Response"
/graphify explain "SwinTransformer"

/graphify ./raw --watch            # auto-sync graph as files change (code: instant, docs: notifies you)
/graphify ./raw --wiki             # build agent-crawlable wiki (index.md + article per community)
/graphify ./raw --svg              # export graph.svg
/graphify ./raw --graphml          # export graph.graphml (Gephi, yEd)
/graphify ./raw --neo4j            # generate cypher.txt for Neo4j
/graphify ./raw --mcp              # start MCP stdio server

graphify hook install              # post-commit git hook - rebuilds graph on every commit automatically

Works with any mix of file types:

Type	Extensions	Extraction
Code	.py .ts .js .go .rs .java .c .cpp .rb .cs .kt .scala .php	AST via tree-sitter + call-graph pass
Docs	.md .txt .rst	Concepts + relationships via Claude
Papers	.pdf	Citation mining + concept extraction
Images	.png .jpg .webp .gif	Claude vision - screenshots, diagrams, any language

What you get

God nodes - highest-degree concepts (what everything connects through)

Surprising connections - ranked by composite score. Code-paper edges rank higher than code-code. Each result includes a plain-English why.

Suggested questions - 4-5 questions the graph is uniquely positioned to answer

Token benchmark - printed automatically after every run. On a mixed corpus (Karpathy repos + papers + images): 71.5x fewer tokens per query vs reading raw files.

Auto-sync (--watch) - run in a background terminal and the graph updates itself as your codebase changes. Code file saves trigger an instant rebuild (AST only, no LLM). Doc/image changes notify you to run --update for the LLM re-pass. Useful for agentic workflows where multiple agents are writing code in parallel - the graph stays current between waves automatically.

Git commit hook (graphify hook install) - installs a post-commit hook that rebuilds the graph after every commit. No background process needed. Triggers once per commit, works with any editor, safe to add alongside existing hooks.

Wiki (--wiki) - Wikipedia-style markdown articles per community and god node, with an index.md entry point. Point any agent at index.md and it can navigate the knowledge base by reading files instead of parsing JSON.

Every edge is tagged EXTRACTED, INFERRED, or AMBIGUOUS - you always know what was found vs guessed.

Worked examples

Corpus	Files	Reduction	Output
Karpathy repos + 5 papers + 4 images	52	71.5x	worked/karpathy-repos/
graphify source + Transformer paper	4	5.4x	worked/mixed-corpus/
httpx (synthetic Python library)	6	~1x	worked/httpx/

Token reduction scales with corpus size. 6 files fits in a context window anyway, so graph value there is structural clarity, not compression. At 52 files (code + papers + images) you get 71x+. Each worked/ folder has the raw input files and the actual output (GRAPH_REPORT.md, graph.json) so you can run it yourself and verify the numbers.

Tech stack

NetworkX + Leiden (graspologic) + tree-sitter + Claude + vis.js. No Neo4j required, no server, runs entirely locally.

Worked examples are the most trust-building contribution. Run /graphify on a real corpus, save output to worked/{slug}/, write an honest review.md evaluating what the graph got right and wrong, submit a PR.

Extraction bugs - open an issue with the input file, the cache entry (graphify-out/cache/), and what was missed or invented.

See ARCHITECTURE.md for module responsibilities and how to add a language.