Transform codebases into persistent knowledge graphs via AST extraction.
npx skills add https://github.com/alangeb/tau --skill graphify-alangeb
Please help me install this Agent Skill. Skill: graphify Source: https://github.com/alangeb/tau/tree/main/src/skills/graphify Command: npx skills add https://github.com/alangeb/tau --skill graphify-alangeb
This skill solves the difficulty of understanding complex, large-scale codebases by transforming them into navigable, queryable knowledge graphs that highlight architecture, dependencies, and community cohesion.
Run the graphify skill on the current directory to build a complete knowledge graph of the codebase.
High-intent search queries and answers about installing and using this skill.
To generate a dependency graph from a large codebase, automated AST extraction detects file relationships and builds a persistent knowledge graph. This enables structural analysis, dependency mapping, and semantic querying across the entire project.
A persistent knowledge graph for codebase architecture is a navigable, queryable structure representing file interactions and community cohesion. It enables semantic querying to ask natural language questions about code logic and design patterns without manual tracing.
Yes, you need Python 3.10+ and the graphifyy package to build a codebase knowledge graph. These dependencies are required to execute the full pipeline of detection, AST extraction, and graph construction.
You analyze code logic by running semantic queries against the generated knowledge graph. This allows you to ask natural language questions about design patterns, file interactions, and module relationships without manually tracing imports.
The best way to understand a new project's architecture is generating a visual and textual report from a knowledge graph. It automatically detects key modules, file relationships, and potential bottlenecks, eliminating manual import tracing.
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