ck-graphify

name	ck:graphify
description	Build queryable knowledge graphs from code, docs, papers, and images. Use for codebase understanding, architecture analysis, cross-file relationship discovery, token-efficient navigation.
user-invocable	true
when_to_use	Invoke to turn code or docs into a queryable graph.
category	dev-tools
keywords	["knowledge-graph","code-analysis","tree-sitter","codebase-understanding","ast"]
argument-hint	[path] [--mcp|--report|--watch]
related	["ck:repomix","ck:scout","ck:gkg"]
maturity	beta
metadata	{"author":"safishamsi","attribution":"https://github.com/safishamsi/graphify"}

Graphify — Knowledge Graph Builder

Turn any folder of code, docs, papers, or images into a queryable knowledge graph. Uses tree-sitter AST for code (20 languages), Whisper for audio/video, and LLM subagents for documents.

When to Use

• Understanding unfamiliar codebase architecture before planning
• Discovering cross-file relationships and dependency chains
• Finding "god nodes" (most-connected concepts) in large projects
• Navigating by structure instead of grepping every file
• Preparing context-efficient codebase representation (71.5x fewer tokens vs raw files)

Installation

Note: The PyPI package is graphifyy (double-y). Other graphify* packages on PyPI are unaffiliated.

CK already bundles this skill at .claude/skills/ck-graphify/. Do not run graphify install for CK setup: upstream uses that command to install its standalone Claude skill at ~/.claude/skills/graphify/SKILL.md.

# Core install
pip install graphifyy

# With MCP server support
pip install 'graphifyy[mcp]'

# Full install (MCP + PDF + video + office + Leiden community detection)
pip install 'graphifyy[all]'

Requirements: Python 3.10+

Quick Start

# Build knowledge graph from current directory
graphify .

# Build from specific path
graphify /path/to/project

# Watch mode (auto-rebuild on file changes)
graphify . --watch

Output Artifacts

File	Purpose
graphify-out/graph.html	Interactive visualization with search + community filtering
graphify-out/GRAPH_REPORT.md	God nodes, surprising connections, suggested questions
graphify-out/graph.json	Persistent graph for queries across sessions
graphify-out/cache/	SHA256-based incremental updates (only reprocesses changed files)

MCP Server Mode

Expose the graph as an MCP server for Claude to query directly:

python -m graphify.serve graphify-out/graph.json

MCP Tools Available

Tool	Purpose
query_graph	Search for concepts and relationships
get_node	Get details of a specific node
get_neighbors	Find related concepts
shortest_path	Find connection path between two concepts

Claude Code MCP Setup

Add to .claude/.mcp.json:

{
  "mcpServers": {
    "graphify": {
      "command": "python",
      "args": ["-m", "graphify.serve", "graphify-out/graph.json"]
    }
  }
}

Three-Pass Architecture

1. AST extraction (local, no API) — tree-sitter parses code in 20 languages deterministically
2. Audio/video transcription (local) — Whisper runs on-device for media files
3. Semantic extraction (API) — LLM subagents process docs, papers, images in parallel

Supported Languages (tree-sitter)

Python, JavaScript, TypeScript, Go, Rust, Java, C, C++, Ruby, C#, Kotlin, Scala, PHP, Swift, Lua, Zig, PowerShell, Elixir, Objective-C, Julia

Confidence Tagging

Relationships in the graph are tagged by provenance:

Tag	Meaning
EXTRACTED	Directly from AST (imports, function calls, class inheritance)
INFERRED	LLM-derived with confidence score
AMBIGUOUS	Uncertain — needs human verification

Workflow Integration

Before Planning

# Build graph first, then plan with context
graphify .
# Claude reads GRAPH_REPORT.md → understands architecture → better plans

With Scout

# Graph for high-level structure, scout for specific files
graphify .                        # build graph
/ck:scout "auth module"           # find specific files

Incremental Updates

Graph rebuilds are incremental — only changed files get reprocessed. Cache at graphify-out/cache/ tracks file hashes.

Privacy

• Code: Processed locally via tree-sitter AST. No file contents leave your machine.
• Audio/Video: Transcribed locally via Whisper.
• Docs/Images: Sent to your configured model provider (Claude/OpenAI) for semantic extraction.

Limitations

• First build on large codebases can be slow (AST parsing + LLM calls)
• Semantic extraction quality depends on the underlying model
• Neo4j integration requires separate setup (pip install 'graphifyy[neo4j]')
• Leiden community detection requires pip install 'graphifyy[leiden]'

Workflow Position

Typically precedes: /ck:plan (understand architecture before planning) Related: /ck:scout (quick file search), /ck:repomix (full context dump), /ck:gkg (semantic symbol navigation)