case-study-generator

name	case-study-generator
description	This skill generates case study entries for intelligent textbook projects from GitHub repositories. Use this skill when adding a new case study to the case studies index page, when the user provides a GitHub repo URL and wants to create a case study card entry. The skill handles repo analysis, thumbnail generation/compression, and markdown entry formatting.

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 — read the canonical metrics file FIRST, do not re-count:

   • Preferred source: docs/learning-graph/book-metrics.json (produced by the book-metrics tool, validated against book-metrics.schema.json). Its metrics object is the single source of truth shared with the README and LinkedIn skills, so the case-study card shows identical numbers:

     # After cloning the repo for analysis:
     python3 -c "import json; m=json.load(open('docs/learning-graph/book-metrics.json'))['metrics']; \
     

print(m['concepts'], m['chapters'], m['microsims'], m['glossaryTerms'], m['faqs'], m['words'])" ```

 Available keys include `concepts`, `chapters`, `microsims`, `stories`,
 `glossaryTerms`, `faqs`, `quizQuestions`, `references`, `diagrams`,
 `equations`, `words`, `links`, `equivalentPages`, `developmentStage`.

• Fallback only if book-metrics.json is absent:
  • 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.

    XXX Concepts · XX Chapters · XX MicroSims · XXK Words · XX Glossary Terms · XX FAQs
    · <span class="completion completion-5" title="Complete (5/5)"></span>
    · [:octicons-mark-github-16: Repository](https://github.com/username/repo-name)

CRITICAL — blank lines between entries: The <div class="grid cards grid-3-col" markdown> block requires a blank line between every list item. If blank lines are missing, MkDocs Material renders all cards as a single-column list instead of a 3-column grid. Always ensure there is an empty line after the · [:octicons...] repo line before the next - **[ entry.

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: Use values from docs/learning-graph/book-metrics.json (canonical source). Format metrics separated by · on one line, then the completion span and repo link each on their own · continuation lines:
   • Concepts, Chapters, MicroSims, Stories (omit any with count 0)
   • Word count rounded to K (e.g. 157K Words)
   • Glossary Terms, FAQs, Quiz Questions, References (omit any with count 0)
   • Completion span: <span class="completion completion-X" title="..."></span>
   • Repository link with GitHub octicon icon
5. Never use "Files" count — it is not a meaningful metric in the new format

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.

    200 Concepts · 15 Chapters · 13 MicroSims · 120K Words · 41 Glossary Terms
    · <span class="completion completion-2" title="Early Development (2/5)"></span>
    · [:octicons-mark-github-16: Repository](https://github.com/dmccreary/systems-thinking)

5. Insert alphabetically after "STEM Robots" entry — ensure a blank line before and after the new entry
6. Verify with mkdocs serve that the page still shows 3 columns

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