Skill: flobo3/skill-graphify (version 1.0.0)

--- 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 ```bash python graphify_wrapper.py ensure-installed ``` Or manually: `pip install graphifyy` ### Step 2 — Build knowledge graph ```bash 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 ```bash 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) ```bash python graphify_wrapper.py query "how does authentication work" ``` Or use the CLI directly for more options: ```bash 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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)