/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.

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 did not know was connected.

Three things it does that Claude alone cannot:

1. Persistent graph: relationships are stored in graphify-out/graph.json and survive across sessions.
2. Honest audit trail: every edge is tagged EXTRACTED, INFERRED, or AMBIGUOUS.
3. Cross-document surprise: community detection finds connections between concepts in different files that you would never think to ask about directly.

Supported file types (parsed by tree-sitter AST): Python, JavaScript, TypeScript, Go, Rust, Java, C, C++, Ruby, C#, Kotlin, Scala, PHP. Plus: Markdown, plain text, PDFs, and images (via Claude vision).

Not supported: SQL, YAML, JSON, shell scripts, and other file types not listed above. If the target directory is primarily SQL (e.g. a dbt project), stop and tell the user: "graphify does not parse SQL. For dbt model dependencies, use the dbt manifest.json instead."

Usage

/graphify                                             # full pipeline on current directory
/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> --cluster-only                       # rerun clustering on existing graph
/graphify <path> --no-viz                             # skip visualization, just report + JSON
/graphify <path> --svg                                # also export graph.svg
/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> --mcp                                # start MCP stdio server for agent access
/graphify <path> --watch                              # watch folder, auto-rebuild on code changes
/graphify <path> --wiki                               # build agent-crawlable wiki
/graphify <path> --obsidian                           # write Obsidian vault to graphify-out/obsidian
/graphify <path> --obsidian-dir ~/vaults/my-project   # write vault to custom path
/graphify add <url>                                   # fetch URL, save to ./raw, update graph
/graphify add <url> --author "Name"                   # tag who wrote it
/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

Corpus-size guardrails

These run in Step 2 (detect). Do not skip them.

• total_files == 0 or all file types empty: stop. Unsupported corpus. For SQL/dbt projects, recommend dbt manifest.json instead.
• total_words < 150,000 AND total_files < 150: stop. Corpus fits in context. Tell user: "This corpus (~[N] words, [N] files) fits in a single context window. Building a graph costs more tokens than it saves. Use the Explore agent or Read tool directly instead."
• total_words > 2,000,000 OR total_files > 200: warn, show top-5 subdirectories by file count, ask user to pick a subfolder before continuing.
• Otherwise: proceed directly to Step 3.

How to invoke modes

Read only the reference file relevant to the current invocation. Do not pre-load all references.

Honesty Rules

• Never invent an edge. If unsure, use AMBIGUOUS.
• Never skip the corpus-size check warning.
• Always show token cost in the report.
• Never hide cohesion scores behind symbols: show the raw number.
• Never run HTML viz on a graph with more than 5,000 nodes without warning the user.