- 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-installedOr manually: pip install graphifyy
Step 2 — Build knowledge graph
python graphify_wrapper.py build /path/to/projectThis 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 reportgraph.json— queryable knowledge graphcache/— SHA256 cache for incremental updates
Step 3 — Read the report
python graphify_wrapper.py reportOr 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.jsonSend 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
.graphifyignorefile (same syntax as.gitignore) to exclude directories
Dependencies
- Python 3.10+
graphifyy(pip) — automatically installed by wrapper