Case Study Generator

Case Study Generator

Overview

This skill automates the creation of case study entries for the intelligent textbooks case studies page. Given a GitHub repository URL, it extracts project information, generates or processes a thumbnail image compressed to ~70KB, and creates a properly formatted markdown entry for docs/​case-studies/​index.md.

Workflow

Step 1: Gather Repository Information

Extract the following from the GitHub repository:

1. Repository URL - The full GitHub URL provided by the user
2. Repository name - Extract from URL (e.g., dmccreary/​geometry-course)
3. GitHub Pages URL - Derive from repo: https://{username}.github.io/​{repo-name}
4. Project title - Check for a clear title in README.md or use repo name
5. Description - Extract from README.md (first paragraph or project description)
6. Metrics (if available):
   • File count: find docs -type f -name "*.md" | wc -l
   • Word count: find docs -type f -name "*.md" -exec cat {} \; | wc -w
   • MicroSim count: Count directories in docs/​sims/
   • Glossary term count: Parse docs/​glossary.md if exists

Use the gh CLI or direct GitHub API to fetch repository information:

# Get repo description
gh repo view {owner}/​{repo} --json description

# Clone repo temporarily for analysis
gh repo clone {owner}/​{repo} /​tmp/​{repo} -- --depth 1

Step 2: Obtain or Generate Thumbnail Image

Check for existing thumbnail options in priority order:

1. Existing social card image - Check docs/​img/ for social-card.png or similar
2. README banner image - Parse README.md for header images
3. Custom image provided by user - User may specify an image path
4. Generate with AI - If no image exists, suggest generating one with an AI image tool

Place the source image in docs/​case-studies/​img/ with a filename matching the repo name:

• Use kebab-case: geometry-course.jpg, deep-learning-course.jpg

Step 3: Compress Thumbnail Image

Compress the thumbnail to approximately 70KB for fast page loading.

For Initial Compression

Run the thumbnail compression script:

python3 src/​compress-thumbnails.py docs/​case-studies/​img 70

This script:

• Targets 70KB file size
• Maintains minimum 400px width
• Preserves aspect ratio
• Creates .backup files for safety

For PNG to JPEG Conversion (if needed)

If PNG compression cannot achieve 70KB target, convert to JPEG:

python3 src/​convert-png-to-jpg.py docs/​case-studies/​img 70

This script:

• Converts PNG to JPEG format
• Fills transparent areas with white background
• Achieves better compression for photographic images
• Removes original PNG after successful conversion

Manual Single-Image Compression

For compressing a single new image without affecting others:

from PIL import Image, ImageOps

def compress_single_image(input_path, output_path, target_kb=70, min_width=400):
    """Compress a single image to target size."""
    img = Image.open(input_path)
    img = ImageOps.exif_transpose(img)

    # Convert to RGB for JPEG
    if img.mode in ('RGBA', 'LA', 'P'):
        background = Image.new('RGB', img.size, (255, 255, 255))
        if img.mode == 'P':
            img = img.convert('RGBA')
        if img.mode in ('RGBA', 'LA'):
            background.paste(img, mask=img.split()[-1])
            img = background
    elif img.mode != 'RGB':
        img = img.convert('RGB')

    # Calculate resize factor
    orig_w, orig_h = img.size
    min_factor = min_width / orig_w if orig_w > min_width else 1.0

    for factor in [0.5, 0.4, 0.35, 0.3, 0.25, 0.2, 0.15]:
        if factor < min_factor:
            continue
        new_w = int(orig_w * factor)
        new_h = int(orig_h * factor)
        resized = img.resize((new_w, new_h), Image.Resampling.LANCZOS)

        for quality in [85, 80, 75, 70, 65, 60]:
            resized.save(output_path, "JPEG", quality=quality, optimize=True)
            if os.path.getsize(output_path) / 1024 <= target_kb:
                return True
    return False

Step 4: Update index.md Image References

After converting PNG to JPEG, update docs/​case-studies/​index.md to use the new .jpg extension:

# Before
![Project Name](./​img/​project-name.png)

# After
![Project Name](./​img/​project-name.jpg)

Step 5: Generate Case Study Entry

Create the markdown entry using the format in references/​entry-format.md.

Entry Format

- **[Project Title](https://username.github.io/​repo-name)**

    ![Alt Text](./​img/​repo-name.jpg)

    Brief 1-2 sentence description of the project, its purpose, and target audience.

    [:octicons-mark-github-16: Repository](https://github.com/​username/​repo-name) · XX Files · XXK Words · X MicroSims

Entry Guidelines

1. Title: Use the full descriptive project title, not the repo name
2. Image alt text: Match the project title or use a descriptive phrase
3. Description:
   • Keep to 1-2 sentences
   • Mention target audience (high school, college, etc.)
   • Highlight key features (MicroSims, learning graph, etc.)
4. Metrics line: Include available metrics separated by ·
   • Repository link with GitHub icon
   • File count (if significant)
   • Word count (rounded to K)
   • Glossary term count
   • MicroSim count
   • Development stage (if early: "Early Stage", "Active Development")

Step 6: Insert Entry in Alphabetical Order

Insert the new entry into docs/​case-studies/​index.md in alphabetical order by project title. The entries are inside a <div class="grid cards grid-3-col" markdown> block.

Step 7: Verify and Clean Up

1. Verify image displays correctly: Run mkdocs serve and check the case studies page
2. Check image file size: Confirm thumbnail is under 70KB
3. Remove backup files (after verification):

   rm docs/​case-studies/​img/​*.backup
   

4. Clean up temporary clone (if created):

   rm -rf /​tmp/​{repo-name}
   

Example Usage

User request: "Add a case study for https://github.com/dmccreary/systems-thinking"

Process:

1. Clone repo, extract: title="Systems Thinking in the Age of AI", description from README
2. Find existing image or generate thumbnail
3. Compress to docs/​case-studies/​img/​systems-thinking.jpg (~31KB)
4. Generate entry:

- **[Systems Thinking in the Age of AI](https://dmccreary.github.io/​systems-thinking)**

    ![Systems Thinking](./​img/​systems-thinking.jpg)

    Interactive resources for teaching systems thinking from high school to executive level. Multiple course descriptions.

    [:octicons-mark-github-16: Repository](https://github.com/​dmccreary/​systems-thinking) · MicroSims included

1. Insert alphabetically after "STEM Robots" entry
2. Verify with mkdocs serve

Resources

Compression Scripts

The following scripts are located in the project's src/ directory:

• src/​compress-thumbnails.py - Compresses images to target KB size with configurable minimum width
• src/​convert-png-to-jpg.py - Converts PNG files to JPEG format for better compression

Reference Files

• references/​entry-format.md - Template and examples for case study entries