graphify

Builds a queryable knowledge graph from code, docs, papers, images, and video.

Updated Jun 5, 2026
One-click install
npx skills add https://github.com/zMynxx/bifrost-with-opencode --skill graphify-zmynxx
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: graphify
Source: https://github.com/zMynxx/bifrost-with-opencode/tree/main/.opencode/skills/graphify
Command: npx skills add https://github.com/zMynxx/bifrost-with-opencode --skill graphify-zmynxx

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, and AI assistants lose that context between sessions. This Skill turns any folder into a persistent knowledge graph with community detection and an honest audit trail, so codebase questions can be answered by querying the graph instead of re-reading files. ## Core Features & Use Cases - Knowledge graph extraction: Combines deterministic AST parsing for code with LLM-based semantic extraction for docs, papers, and images, tagging every edge as EXTRACTED, INFERRED, or AMBIGUOUS. - Query, path, and explain tools: Answer natural-language questions with BFS/DFS traversal, find shortest paths between concepts, and get plain-language explanations of any node. - 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. - Incremental updates and automation: Supports --update for changed files only, --watch for auto-rebuilds, GitHub repo cloning, and cross-repo graph merging. - Use Case: Point it at a monorepo or several GitHub URLs, then ask "How does authentication flow reach the database?" and get an answer traced through the merged cross-repo graph. ## Quick Start Run /graphify on the current directory to build the knowledge graph, then ask any codebase question to query it.

Frequently Asked Questions about graphify

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

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

Run /graphify on a local path or GitHub URL. It detects files, runs AST extraction on code and LLM subagents on docs and images, then clusters the result into graph.json, an interactive HTML view, and a GRAPH_REPORT.md summary.

How do I query an existing graphify knowledge graph?▼

Use graphify query "<question>" for BFS traversal, add --dfs to trace a specific path, or use graphify path "A" "B" and graphify explain "Node". If graphify-out/graph.json already exists, questions go straight to the query step without rebuilding.

Can graphify process GitHub repositories and multiple repos at once?▼

Yes. Pass one or more https://github.com/ URLs and graphify clones each into ~/.graphify/repos, builds a graph per repo, then merges them with graphify merge-graphs into a single cross-repo graph where each node carries a repo attribute.

Does graphify support video and audio files?▼

Yes. When detection finds video or audio files, graphify transcribes them with Whisper (model selectable via --whisper-model) using a domain hint derived from the corpus, then treats the transcripts as documents during semantic extraction.

How do I update the graph incrementally after code changes?▼

Run /graphify <path> --update to re-extract only new or changed files and prune deleted ones. Code-only changes skip LLM extraction entirely; --watch mode auto-rebuilds on file changes, and a post-commit hook can be installed with graphify hook install.

What export formats does graphify support?▼

Graphify exports interactive HTML by default, plus GraphRAG-ready JSON, Obsidian vaults, agent-crawlable wikis, SVG, GraphML, and Cypher for Neo4j or FalkorDB with direct push options. It can also run as an MCP stdio server exposing query tools to other agents.