graphify-windows

Convert code, docs, papers, and media into a queryable knowledge graph with community detection.

Updated Apr 18, 2026
One-click install
npx skills add https://github.com/webdevarif/claude-skills --skill graphify-windows-webdevarif
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: graphify-windows
Source: https://github.com/webdevarif/claude-skills/tree/main/graphify
Command: npx skills add https://github.com/webdevarif/claude-skills --skill graphify-windows-webdevarif

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires graphifyy, networkx, and includes references (resource) components.

What problem does it solve? Understanding a large codebase or mixed document corpus requires reading hundreds of files manually. This Skill turns any folder of code, docs, papers, images, or videos into a persistent knowledge graph with community detection, so questions about architecture, file relationships, and cross-document connections can be answered by graph traversal instead of re-reading files. ## Core Features & Use Cases - Knowledge graph construction: Extracts entities and relationships via AST parsing for code and LLM-based semantic extraction for docs, papers, and images, with an honest EXTRACTED/INFERRED/AMBIGUOUS confidence audit trail. - Query, path, and explain tools: Answers natural-language questions with BFS/DFS traversal, finds shortest paths between concepts, and explains individual nodes with source citations. - Multiple outputs and integrations: Generates interactive HTML, GraphRAG-ready JSON, GRAPH_REPORT.md, Obsidian vaults, wikis, and exports to Neo4j, FalkorDB, SVG, GraphML, or an MCP server. - Use Case: Point it at a cloned GitHub repository, then ask "How does the authentication flow reach the database?" and get a graph-traversal answer with source file citations instead of grepping through the repo. ## Quick Start Run /graphify on the current project directory to build the knowledge graph, then ask any codebase question to query it.

Frequently Asked Questions about graphify-windows

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a knowledge graph from a codebase?

Run /graphify on a local path or GitHub URL. It detects files, extracts code structure via AST parsing and document semantics via LLM subagents, then clusters the graph and outputs graph.json, an interactive HTML view, and a GRAPH_REPORT.md summary.

How do I query a codebase knowledge graph with natural language?

Once graphify-out/graph.json exists, run graphify query "your question" for BFS traversal, or add --dfs to trace a specific dependency path. Use graphify path "A" "B" for shortest paths and graphify explain "Node" for single-concept explanations.

Does graphify require an API key to run?

No API key is required. Code extraction is fully structural via AST with no LLM. Semantic extraction for docs, papers, and images uses Gemini only if GEMINI_API_KEY or GOOGLE_API_KEY is set; otherwise the host agent itself performs extraction.

Can graphify export a graph to Neo4j or FalkorDB?

Yes. Use --neo4j to generate a Cypher file or --neo4j-push bolt://localhost:7687 to push directly to Neo4j. FalkorDB is supported the same way via --falkordb and --falkordb-push, using MERGE so re-runs do not create duplicates.

How does incremental update work when files change?

Run /graphify <path> --update to re-extract only new or changed files and prune deleted ones. Code-only changes skip LLM extraction entirely, and a --watch mode or git post-commit hook can trigger rebuilds automatically.

What file types can graphify process?

It handles code files across many languages, Markdown and text documents, PDF papers, images via vision extraction, and video or audio files which are first transcribed to text with Whisper before being treated as documents.