uv-tutorial-generator

Generate structured tutorials with objectives, diagrams, runnable examples, and exercises.

1|Updated Feb 6, 2026

Search Tags:#documentation #onboarding #tutorial #developer education #code learning #example generation

Ready to run

npx skills add https://github.com/uv-xiao/pkbllm --skill uv-tutorial-generator

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: uv-tutorial-generator
Source: https://github.com/uv-xiao/pkbllm/tree/main/human/exercises/tutorial-generator
Command: npx skills add https://github.com/uv-xiao/pkbllm --skill uv-tutorial-generator

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and assets (resource) and references (resource) components.

What problem does it solve?

This Skill automates the creation of structured, runnable tutorials from codebase analysis and hands-on learning sessions, transforming complex technical information into teachable material.

Core Features & Use Cases

Structured Tutorial Generation: Creates tutorials with objectives, diagrams, code tours, runnable examples, and exercises.
Source Grounding: Links directly to code locations (file:line) for clarity.
Runnable Examples: Ensures tutorials include executable code snippets with expected outputs.
Checkable Exercises: Provides tasks with clear acceptance criteria for self-assessment.
Use Case: Turn your understanding of a new ML library's architecture into a comprehensive tutorial for new team members, complete with setup instructions, code walkthroughs, and practice exercises.

Quick Start

Initialize a new tutorial for the topic "vllm-internals" by running the init_tutorial.py script.

Frequently Asked Questions about uv-tutorial-generator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate structured tutorials from a codebase?▼

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.

What is source grounding in developer education tutorials?▼

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.

Do I need Python to create runnable code examples for onboarding?▼

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.

Can I generate checkable exercises with clear acceptance criteria from code?▼

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.

What is the best way to turn ML library architecture into team onboarding material?▼

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.

Are there limitations when creating code tours from repository analysis?▼

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.