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name skill-graphify
description Turn any folder of code, docs, papers, or images into a queryable knowledge graph. Cross-platform wrapper for graphify CLI.

Skill Graphify

Turn any folder of files into a navigable knowledge graph with community detection, honest audit trail, and three outputs: interactive HTML, queryable JSON, and a plain-language report.

When to use

  • User wants to understand a codebase they're new to
  • User asks "how does X connect to Y" across many files
  • User has a folder of papers/notes/screenshots and wants structure
  • User wants a visual map of their project's architecture

Usage

Step 1 — Ensure graphify is installed

python graphify_wrapper.py ensure-installed

Or manually: pip install graphifyy

Step 2 — Build knowledge graph

python graphify_wrapper.py build /path/to/project

This runs the full pipeline: detect files → AST extraction → build graph → cluster → export.

Output goes to <project>/graphify-out/:

  • graph.html — interactive visualization (open in browser)
  • GRAPH_REPORT.md — plain-language audit report
  • graph.json — queryable knowledge graph
  • cache/ — SHA256 cache for incremental updates

Step 3 — Read the report

python graphify_wrapper.py report

Or read graphify-out/GRAPH_REPORT.md directly. Present the key findings to the user: god nodes (highly connected), surprising connections, community structure.

Step 4 — Query the graph (optional)

python graphify_wrapper.py query "how does authentication work"

Or use the CLI directly for more options:

graphify query "show the auth flow" --graph graphify-out/graph.json
graphify query "what connects X to Y?" --graph graphify-out/graph.json --dfs
graphify query "explain dependency injection" --budget 1500 --graph graphify-out/graph.json

Send results to user

After building, send graphify-out/graph.html to the user so they can explore the interactive graph. Summarize GRAPH_REPORT.md in your response.

CLI reference (graphify)

If graphify CLI is available, you can use these directly:

Command Description
graphify query "..." --graph <path> BFS traversal of the graph
graphify query "..." --dfs --graph <path> DFS — trace a specific path
graphify query "..." --budget N --graph <path> Cap output at N tokens
graphify path "Node1" "Node2" --graph <path> Shortest path between concepts
graphify explain "NodeName" --graph <path> Plain-language explanation of a node

Supported file types

  • Code: 20 languages via tree-sitter (Python, JS, TS, Go, Rust, Java, C, C++, Ruby, C#, Kotlin, Scala, PHP, Swift, Lua, Zig, PowerShell, Elixir, Objective-C, Julia)
  • Docs: Markdown, text, reStructuredText
  • Papers: PDF
  • Images: Screenshots, diagrams, whiteboard photos (requires vision-capable LLM)

Notes

  • The wrapper script (graphify_wrapper.py) handles cross-platform compatibility (Windows CMD, Linux, macOS)
  • graphify's AST extraction is deterministic and requires no LLM — it's free and fast
  • Semantic extraction (docs, images) uses LLM subagents if available, otherwise is skipped
  • Every edge is tagged EXTRACTED, INFERRED, or AMBIGUOUS — you always know what was found vs guessed
  • Incremental updates: re-running on the same folder only processes changed files (cache-based)
  • Add a .graphifyignore file (same syntax as .gitignore) to exclude directories

Dependencies

  • Python 3.10+
  • graphifyy (pip) — automatically installed by wrapper