# Graphify > Build and query a knowledge graph over a repo, docs folder, papers, or notes corpus using the graphify CLI. Use when the user asks to graphify a directory, map a codebase or research corpus into a knowledge graph, find connections between concepts or files, explain how entities relate, or export a graph as an Obsidian vault, HTML visualization, GraphML, or Neo4j. - Skill: `jylhis/graphify` (Agent Skill) - Install (CLI): `npx skillmds@latest add jylhis/graphify` - Raw SKILL.md: https://api.skillmd.com/api/skills/jylhis/graphify/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Web & Frontend - Author: Jylhis (https://skillmd.com/u/jylhis) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/jylhis/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](https://github.com/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: ```sh 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 ```sh graphify # full pipeline: extract, build, cluster, report graphify --mode deep # aggressive cross-document inference (slower, costlier) graphify --update # re-extract changed files only, merge into graph graphify --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 ```sh graphify query "" # answer by traversing the graph graphify path "" "" # shortest path between two concepts graphify explain "" # everything the graph knows about one node graphify add # 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 ```sh graphify --svg # embeddable diagram graphify --graphml # Gephi / yEd graphify --neo4j # Cypher export or direct push graphify --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 --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.