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.

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 ./raw

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 .

Manual install (curl)

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-update graph whenever files change
/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

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.

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	Type	Reduction	Eval
Karpathy repos + 5 papers + 4 images	Mixed	71.5x	`worked/karpathy-repos/review.md`
httpx (Python HTTP client)	Code	small corpus¹	`worked/httpx/review.md`
Code + paper + Arabic image	Multi-type	small corpus¹	`worked/mixed-corpus/review.md`

¹ Small corpora fit in one context window - graph value is structural clarity, not compression.

Tech stack

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

Contributing

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.