SkillFoxx

Type a name, a task or a service

graphify

The /graphify skill builds a knowledge graph of a project's code, docs and PDFs so the agent queries the graph instead of grepping

SkillMedium risk

graphify-labs/graphifyAuthor: Graphify Labs

Install

uv tool install graphifyy
graphify install

Alternatives to uv are pipx install graphifyy or pip install graphifyy. Add --project to install the skill into the current repository.

This is third-party code. Review the repository files before installing.

What it does

The /graphify command walks a folder or repository and builds a graph of links between files, functions, schemas and documents. Code is parsed locally with tree-sitter without a model, while docs, PDFs, images and video get a semantic pass from the agent's model. Every edge is tagged as extracted from source or inferred, so you can tell what to trust. Outputs are interactive HTML, GraphRAG-ready JSON and a GRAPH_REPORT.md, and the agent gets query, path and explain commands plus an optional MCP server over the graph.

Who it is for. For developers and teams using agents in large or unfamiliar codebases.

Good fit when

  • You need to understand the architecture of an unfamiliar repository quickly
  • The agent burns many tokens searching through files
  • You want one map linking code with docs, SQL schemas and PDFs

Not a fit when

  • A small project the agent can read in full anyway
  • Documents cannot be sent to a model but document analysis is what you need

Example request

/graphify . then show how the auth module connects to the database

Limitations

Requires Python and the graphifyy package from PyPI (double y; other graphify packages are unrelated). Code parsing is local, while docs, images and video go through the agent's model or a separate API key. The graph must be refreshed after pulls; git hooks help with that.

How to disable. Remove the skill from the agent's skills directory and the package with uv tool uninstall graphifyy or pip uninstall graphifyy. Delete the graphify-out folder in the project manually if needed.

Security check

  • Reads all project files and writes results to graphify-out
  • Docs and media are sent to a model for analysis
  • Installs git hooks on a separate command

README in short

The README covers a two-step install: the graphifyy package via uv, pipx or pip and registering the skill with graphify install, optionally per project. It describes team mode with git hooks that rebuild the graph on commit and branch switch, and an MCP server with query_graph, get_neighbors and shortest_path tools. Supports Claude Code, Cursor, Codex, Gemini CLI and more. Apache-2.0 licensed.

SKILL.md

---
name: graphify
description: "Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools."
---

# /graphify

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

## Usage

```
/graphify                                             # full pipeline on current directory (HTML viz; add --obsidian for a vault)
/graphify <path>                                      # full pipeline on specific path
/graphify https://github.com/<owner>/<repo>           # clone repo then run full pipeline on it
/graphify https://github.com/<owner>/<repo> --branch <branch>  # clone a specific branch
/graphify <url1> <url2> ...                           # clone multiple repos, build each, merge into one cross-repo graph
/graphify <path> --mode deep                          # thorough extraction, richer INFERRED edges
/graphify <path> --update                             # incremental - re-extract only new/changed files
/graphify <path> --directed                            # build directed graph (preserves edge direction: source→target)
/graphify <path> --whisper-model medium                # use a larger Whisper model for better transcription accuracy
/graphify <path> --cluster-only                       # rerun clustering on existing graph
/graphify <path> --no-viz                             # skip visualization, just report + JSON
/graphify <path> --html                               # (HTML is generated by default - this flag is a no-op)
/graphify <path> --svg                                # also export graph.svg (embeds in Notion, GitHub)
/graphify <path> --graphml                            # export graph.graphml (Gephi, yEd)
/graphify <path> --neo4j                              # generate graphify-out/cypher.txt for Neo4j
/graphify <path> --neo4j-push bolt://localhost:7687   # push directly to Neo4j
/graphify <path> --falkordb                           # generate graphify-out/cypher.txt for FalkorDB
/graphify <path> --falkordb-push falkordb://localhost:6379   # push directly to FalkorDB

FAQ

Does code leave the machine?

No, code is parsed locally via AST. Only docs, PDFs and media you include go to a model.

Can a team share one graph?

Yes, graphify-out is meant to be committed, and the built-in MCP server can serve the graph over HTTP.

Editors’ pick

A skills library that gives coding agents a development process: brainstorming, planning, TDD, subagents and code review

PluginMedium risk286.6K
Editors’ pick

Small composable skills for engineering with agents: plan grilling, TDD, bug diagnosis, code review and architecture

SkillLow risk262K

Reference MCP servers

Model Context Protocol servers

Editors’ pick

Official reference MCP servers: Filesystem, Fetch, Git, Memory, Sequential Thinking, Time and Everything

MCP serverMedium risk90.3K
Editors’ pick

Up-to-date, version-specific library docs and code examples in your agent's context, via MCP or a CLI plus skill

MCP serverMedium risk62K