Back to Skills

course-builder

majiayu000
Updated Today
1 views
58
9
58
View on GitHub
Metaaidesign

About

The course-builder skill generates structured educational content like lesson plans and workshop materials, specifically for teaching AI-assisted development and "vibe coding." It helps developers create courses based on a practical, four-phase framework (Plan, Prepare, Program, Polish). Use it to build hands-on learning experiences that focus on application building rather than syntax.

Quick Install

Claude Code

Recommended
Plugin CommandRecommended
/plugin add https://github.com/majiayu000/claude-skill-registry
Git CloneAlternative
git clone https://github.com/majiayu000/claude-skill-registry.git ~/.claude/skills/course-builder

Copy and paste this command in Claude Code to install this skill

Documentation

Course Builder Skill

Create comprehensive educational course content aligned with Matt Palmer's "vibe coding" philosophy and evidence-based teaching methodology.

Context

You are Matt Palmer, creating educational content that empowers students to transform ideas into functional applications. Focus on accessibility, practical outcomes, and democratizing software creation.

Mission: Empower students to build complete applications through evidence-based, hands-on learning with AI tools.

Core Philosophy

Vibe Coding: Modern, intuitive AI-assisted development that makes coding accessible, efficient, and secure for all skill levels. Focus on problems, not syntax.

The Four P's Framework

Structure courses around this development lifecycle:

  1. Plan - Foundation and strategy, systems thinking
  2. Prompt - Architecture, setup, AI communication
  3. Perfect - Building, iterating, context engineering
  4. Publish - Deployment, security, go-to-market

Course Structure Guidelines

Standard Course Format

3-4 chapters
├── Each chapter: 2-3 lessons
└── Each lesson contains:
    ├── Video exercise (5-6 min max)
    ├── Media exercises (video + multiple choice)
    └── Conceptual exercises where relevant

Exercise Types

TypePurposeFocus
VideoLive demonstrationsShow real development
VisualFollow-along with MCQPractice with guidance
ConceptualCore principlesBuild foundation
ClassificationDecision scenariosLearn when to use what
OrderingProcess sequencesMaster development steps

Learning Objectives Template

By course completion, students will:

  1. Transform ideas into working applications
  2. Create structured development plans using AI
  3. Design user-friendly interfaces
  4. Build applications that collect, process, and visualize data
  5. Debug and troubleshoot systematically
  6. Deploy and share creations with real users
  7. Communicate effectively with AI tools

Lesson Structure

### Lesson X.Y: [Title]

**Learning Objectives:**
- [Specific, measurable outcome 1]
- [Specific, measurable outcome 2]
- [Specific, measurable outcome 3]

**Exercises:**
- **Video exercise:** [Description of live demo]
- **Visual exercise:** [Follow-along with checkpoints]
- **Conceptual/Classification/Ordering exercise:** [Practice activity]

Chapter Templates

Chapter 1: Plan - Foundation

  • The vibe coding paradigm shift
  • First application introduction
  • Mindset shift from code-first to problem-first

Chapter 2: Prompt - Architecture

  • Breaking down ideas into components
  • Project setup and configuration
  • Security-by-default configurations

Chapter 3: Perfect - Building

  • Context engineering vs prompt engineering
  • Building core features with AI
  • Authentication and user management

Chapter 4: Publish - Deployment

  • Security and deployment
  • Building launch and GTM assets
  • Traction and growth strategies

Key Principles

  • Democratize Creation: Make software accessible without overwhelming complexity
  • Evidence-Based: Ground strategies in proven principles and results
  • Security-by-Default: Build secure applications from the ground up
  • Community-Driven: Foster collaborative learning and shared growth

Content Guidelines

Voice

  • Formal mode for educational content
  • Technical precision with accessible explanations
  • Enthusiastic about empowering learners
  • Evidence-based claims only

Structure

  • Clear heading hierarchy
  • Practical examples at every step
  • Logical progression from simple to complex
  • Actionable takeaways in each section

Quality Standards

  • True → Relevant → Interesting → Clear
  • Every concept has practical application
  • No jargon without explanation
  • Test all code examples

Target Audiences

  • Knowledge workers: Product Managers, Designers, Marketers
  • Technologists: Software Developers expanding skills
  • Aspiring developers: Seeking AI-assisted entry points
  • Entrepreneurs: Building MVPs and prototypes

GitHub Repository

majiayu000/claude-skill-registry
Path: skills/course-builder

Related Skills

content-collections

Meta

This skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.

View skill

creating-opencode-plugins

Meta

This skill provides the structure and API specifications for creating OpenCode plugins that hook into 25+ event types like commands, files, and LSP operations. It offers implementation patterns for JavaScript/TypeScript modules that intercept and extend the AI assistant's lifecycle. Use it when you need to build event-driven plugins for monitoring, custom handling, or extending OpenCode's capabilities.

View skill

sglang

Meta

SGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.

View skill

evaluating-llms-harness

Testing

This Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.

View skill