Graphify

Graphify

Turn a folder of mixed files (code, markdown, PDFs, images) into a persistent, queryable knowledge graph: entities and edges, clustered into communities, exported as an Obsidian vault and interactive HTML. Adapted from the skill shipped by Graphify-Labs/graphify (MIT).

What the graph adds over reading files directly: relationships persist across sessions in graphify-out/graph.json, every edge carries an audit tag (EXTRACTED from source, INFERRED by the model, or AMBIGUOUS), and community detection surfaces cross-document connections that grepping misses.

Prerequisites

Check the install before anything else:

graphify --version || pip install graphifyy

Code parsing is local AST work (tree-sitter, no LLM cost). Semantic extraction from docs, PDFs, and images calls an LLM, so large corpora cost real tokens; warn the user before deep runs on big folders.

Build a graph

graphify <path>              # full pipeline: extract, build, cluster, report
graphify <path> --mode deep  # aggressive cross-document inference (slower, costlier)
graphify <path> --update     # re-extract changed files only, merge into graph
graphify <path> --watch      # auto-rebuild on change (code-only, no LLM cost)

Scope before running: if the corpus exceeds roughly 200 files or 2M words, ask the user to pick a subfolder instead of forcing the whole tree through. Skip obviously sensitive files (secrets, credentials) from the corpus.

Outputs land in graphify-out/: graph.json (persistent store), an Obsidian vault, graph.html (interactive view, practical below ~5000 nodes), and a markdown report. Community labels in the report are meant to be reviewed and renamed by a human; offer to relabel them with the user rather than trusting auto-generated names.

Query the graph

graphify query "<question>"      # answer by traversing the graph
graphify path "<A>" "<B>"        # shortest path between two concepts
graphify explain "<entity>"      # everything the graph knows about one node
graphify add <url>               # fetch a URL into the corpus and update

When relaying answers: cite the source locations the graph gives, keep the EXTRACTED / INFERRED / AMBIGUOUS distinction visible for load-bearing claims, and say so plainly when the graph has no coverage instead of filling gaps from your own knowledge.

Exports

graphify <path> --svg        # embeddable diagram
graphify <path> --graphml    # Gephi / yEd
graphify <path> --neo4j      # Cypher export or direct push
graphify <path> --mcp        # serve the graph over MCP

The Obsidian vault export pairs with the obsidian-markdown skill; a graphify run over an llm-wiki corpus is a quick way to visualize hubs and orphan pages.

Gotchas

• Upstream's graphify install drops its own copy of this skill into ~/.claude/skills/graphify/. If that copy is present, they overlap; prefer one and tell the user which.
• --update beats full rebuilds once a graph exists; full runs redo the LLM extraction.
• Rerunning clustering alone is cheap: graphify <path> --cluster-only.

Verification

After a build: graphify-out/graph.json exists and is non-empty, the report lists communities with sensible labels, and a spot-check graphify explain on a known entity returns edges pointing at real source locations.