/graphify

name: graphify
description: any input (code, docs, papers, images) → knowledge graph → clustered communities → HTML + JSON + audit report. Use when the user wants to build, inspect, or query a cross-file knowledge graph from code, docs, papers, images, or videos with upstream graphify.
trigger: /graphify

/graphify

Turn any folder of files into a navigable knowledge graph with community detection, an honest audit trail, and three outputs: interactive HTML, GraphRAG-ready JSON, and a plain-language GRAPH_REPORT.md.

Upstream

• Upstream project: `https://github.com/safishamsi/graphify`
• Official PyPI package: `graphifyy` (double `y`)
• Upstream license: MIT
• This repo ships guidance for using the upstream tool. It does not vendor the Python package itself.

Read [`UPSTREAM.md`](./UPSTREAM.md) before changing compatibility, install, or license claims.

Requirements

Required tools:

• Python 3.10+
• Official `graphifyy` package installed for the `python` interpreter this skill will use

Check:

Get-Command graphify -ErrorAction SilentlyContinue
python -c "import graphify, sys; print(sys.executable)" 2>$null

Install options:

uv tool install graphifyy
pipx install graphifyy
python -m pip install graphifyy

Rules:

• Do not assume `graphifyy` is installed.
• Do not auto-install without user approval.
• Show the install command before running it.
• If `graphify` exists on PATH but `python -c "import graphify"` fails, stop and tell the user the skill cannot use that interpreter yet.

Manual CLI Notes

• In PowerShell, run `graphify .`, not `/graphify .`.
• To install upstream Codex integration after the package is present: `graphify codex install`
• Manual CLI use and skill-guided use are separate: the package must exist first in both cases.

Usage

/graphify                                                # full pipeline on current directory → Obsidian vault
/graphify <path>                                         # full pipeline on specific path
/graphify <path> --mode deep                             # thorough extraction, richer INFERRED edges
/graphify <path> --update                                # incremental - re-extract only new/changed files
/graphify <path> --directed                              # build directed graph (preserves edge direction: source→target)
/graphify <path> --whisper-model medium                  # use a larger Whisper model for better transcription accuracy
/graphify <path> --cluster-only                          # rerun clustering on existing graph
/graphify <path> --no-viz                                # skip visualization, just report + JSON
/graphify <path> --html                                  # (HTML is generated by default - this flag is a no-op)
/graphify <path> --svg                                   # also export graph.svg (embeds in Notion, GitHub)
/graphify <path> --graphml                               # export graph.graphml (Gephi, yEd)
/graphify <path> --neo4j                                 # generate graphify-out/cypher.txt for Neo4j
/graphify <path> --neo4j-push bolt://localhost:7687      # push directly to Neo4j
/graphify <path> --wiki                                  # build agent-crawlable wiki (index.md + one article per community)
/graphify <path> --obsidian                              # generate Obsidian vault
/graphify <path> --obsidian --obsidian-dir ~/my/vault    # write vault to custom path (e.g. existing vault)
/graphify <path> --mcp                                   # start MCP stdio server for agent access
/graphify <path> --watch                                 # watch folder, auto-rebuild on code changes (no LLM needed)
/graphify add <url>                                      # fetch URL, save to ./raw, update graph
/graphify add <url> --author "Name"                      # tag who wrote it
/graphify add <url> --contributor "Name"                 # tag who added it to the corpus
/graphify query "<question>"                             # BFS traversal - broad context
/graphify query "<question>" --dfs                       # DFS - trace a specific path
/graphify query "<question>" --budget 1500               # cap answer at N tokens
/graphify path "AuthModule" "Database"                   # shortest path between two concepts
/graphify explain "SwinTransformer"                      # plain-language explanation of a node

What graphify is for

graphify is built around Andrej Karpathy's /raw folder workflow: drop anything into a folder - papers, tweets, screenshots, code, notes - and get a structured knowledge graph that shows you what you didn't know was connected.

Three things it does that your AI assistant alone cannot: 1. **Persistent graph** - relationships are stored in `graphify-out/graph.json` and survive across sessions. Ask questions weeks later without re-reading everything. 2. **Honest audit trail** - every edge is tagged EXTRACTED, INFERRED, or AMBIGUOUS. You know what was found vs invented. 3. **Cross-document surprise** - community detection finds connections between concepts in different files that you would never think to ask about directly.

Use it for:

• A codebase you're new to (understand architecture before touching anything)
• A reading list (papers + tweets + notes → one navigable graph)
• A research corpus (citation graph + concept graph in one)
• Your personal /raw folder (drop everything in, let it grow, query it)

What You Must Do When Invoked

If no path was given, use `.` (current directory). Do not ask the user for a path.

Follow these steps in order. Do not skip steps.

Step 1 - Ensure graphify is installed

# Check CLI presence and Python importability
Get-Command graphify -ErrorAction SilentlyContinue
python -c "import graphify, sys; open('.graphify_python', 'w').write(sys.executable)" 2>$null

If the import succeeds, print nothing and move straight to Step 2.

If it fails:

1. Stop. Do not continu