notebook-development

name	notebook-development
description	Helps create and structure Jupyter notebooks for teaching labs. Use this skill when creating lab notebooks, adding explanations for students, structuring educational content, or writing code cells with proper documentation.

Notebook Development Skill (Teaching Labs)

Overview

This skill helps create well-structured Jupyter notebooks for teaching Northwestern MS AI students about Azure services and agentic AI.

Target Audience

• Who: Graduate students in MS AI program
• Background: Smart industry professionals, strong coding skills
• Tone: Professional, educational, explains why not just how
• Level: Intermediate to advanced Python, new to Azure/AI Foundry

Notebook Structure Template

1. Title & Overview (Markdown)
   - Lab title
   - Learning objectives
   - Prerequisites
   - Architecture diagram

2. Setup & Prerequisites (Code + Markdown)
   - Import checks
   - Tool verification
   - Environment setup

3. Core Concepts (Markdown)
   - Explain the theory
   - Architecture diagrams
   - Key terminology tables

4. Step-by-Step Implementation (Code + Markdown)
   - One concept per section
   - Code cells with comments
   - Explanatory markdown between code

5. Testing & Verification (Code)
   - Test the implementation
   - Show expected outputs

6. Summary (Markdown)
   - Key takeaways table
   - Next steps
   - Additional resources

Writing Style Guidelines

Markdown Cells

## Step X: Clear Action Title

**Why this matters:** Brief explanation of the concept's importance.

### Key Concepts

| Concept | Description |
|---------|-------------|
| Term 1  | Clear definition |
| Term 2  | Clear definition |

### Architecture

\`\`\`
┌─────────────┐     ┌─────────────┐
│  Component  │────▶│  Component  │
└─────────────┘     └─────────────┘
\`\`\`

Now let's implement this:

Code Cells

# Clear section comment explaining what this code does
# Include inline comments for non-obvious logic

def example_function(data: list[float]) -> dict:
    """
    Brief description of what the function does.
    
    Args:
        data: List of numbers to process
        
    Returns:
        Dictionary with computed statistics
    """
    # Validate input
    if not data:
        raise ValueError("Data cannot be empty")
    
    # Compute statistics
    result = {
        "count": len(data),
        "mean": sum(data) / len(data),
    }
    
    return result

# Test the function
sample_data = [10, 20, 30, 40, 50]
result = example_function(sample_data)
print(f"✅ Analysis complete: {result}")

ASCII Art Diagrams

Use consistent styles for architecture diagrams:

Flow Diagram

Input ───▶ Process ───▶ Output

Component Diagram

┌─────────────────┐     ┌─────────────────┐
│   Component A   │────▶│   Component B   │
│   (description) │◀────│   (description) │
└─────────────────┘     └─────────────────┘

Layered Architecture

┌─────────────────────────────────────────┐
│            Presentation Layer            │
├─────────────────────────────────────────┤
│             Business Logic              │
├─────────────────────────────────────────┤
│              Data Layer                 │
└─────────────────────────────────────────┘

Emoji Conventions

Use emojis consistently:

Emoji	Meaning
✅	Success/Complete
❌	Error/Failure
⚠️	Warning/Caution
📝	Note/Documentation
🔧	Configuration/Setup
🎉	Celebration/Milestone
📡	Network/API
🔒	Security
💡	Tip/Insight

Tables for Key Concepts

| Feature | Description | Example |
|---------|-------------|---------|
| Feature 1 | Clear description | `code example` |
| Feature 2 | Clear description | `code example` |

Code Output Formatting

# Print structured output
print("=" * 50)
print("📊 Results Summary")
print("=" * 50)
print(f"  • Count: {result['count']}")
print(f"  • Mean:  {result['mean']:.2f}")
print("=" * 50)

Prerequisites Check Pattern

import subprocess
import shutil

def check_tool(name: str, command: list) -> bool:
    """Check if a tool is installed."""
    try:
        result = subprocess.run(command, capture_output=True, text=True, timeout=10)
        version = result.stdout.strip().split('\n')[0]
        print(f"✅ {name}: {version}")
        return True
    except (subprocess.SubprocessError, FileNotFoundError):
        print(f"❌ {name}: Not installed")
        return False

print("Checking required tools...\n")
tools = [
    ("Python", ["python", "--version"]),
    ("Azure CLI", ["az", "--version"]),
]
all_ok = all(check_tool(name, cmd) for name, cmd in tools)

if all_ok:
    print("\n🎉 All tools installed!")
else:
    print("\n⚠️ Some tools missing. See installation instructions above.")

Summary Section Template

## 🎯 Summary

### What You Built
Brief description of the complete implementation.

### Key Takeaways

| Concept | Implementation |
|---------|----------------|
| Concept 1 | How it was implemented |
| Concept 2 | How it was implemented |

### Next Steps
- Link to next lab
- Advanced topics to explore
- Additional resources

## 📚 Resources
- [Official Documentation](url)
- [Tutorial](url)