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 /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 | BFS traversal of the graph |

| graphify query "..." --dfs --graph | DFS — trace a specific path |

| graphify query "..." --budget N --graph | Cap output at N tokens |

| graphify path "Node1" "Node2" --graph | Shortest path between concepts |

| graphify explain "NodeName" --graph | 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