uv-tutorial-generator

Create runnable tutorials from a codebase by running the initialization Python script against your repository. The process analyzes source code locations and generates teachable material with objectives, diagrams, executable examples, and exercises.

Source grounding in developer education tutorials links teachable material directly to specific code locations using file and line references. This ensures learners can navigate to the exact source code being explained within the generated tutorial structure.

Yes, you need Python to create runnable code examples for onboarding. The tutorial generation process requires running Python initialization scripts to set up the directory structure and generate the executable code snippets and checkable exercises.

Yes, you can generate checkable exercises with clear acceptance criteria directly from code. The tutorial structure includes self-assessment tasks with expected outputs and acceptance criteria derived from the analyzed repository artifacts.

The best way to turn ML library architecture into onboarding material is to automate tutorial generation from repo analysis. This transforms technical information into structured lessons with setup instructions, code walkthroughs, and practice exercises.

Limitations when creating code tours include dependency on specific Python initialization scripts and strict adherence to tutorial structure conventions. The generated output must be organized under a designated exercises and tutorials directory path.