| 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:
- Repository URL - The full GitHub URL provided by the user
- Repository name - Extract from URL (e.g.,
dmccreary/geometry-course)
- GitHub Pages URL - Derive from repo:
https://{username}.github.io/{repo-name}
- Project title - Check for a clear title in README.md or use repo name
- Description - Extract from README.md (first paragraph or project description)
- 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:
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: `concepts`, `chapters`, `microsims`, `stories`,
`glossaryTerms`, `faqs`, `quizQuestions`, `references`, `diagrams`,
`equations`, `words`, `links`, `equivalentPages`, `developmentStage`.
- Fallback only if
book-metrics.json is absent (older repos that have
not run the metrics tool):
- Concept count:
find docs -name learning-graph.csv | wc -l and subtract 1 for header
- Chapter count: Count directories in
docs/chapters/
- Glossary term count: number of level 4 headers in
docs/glossary.md
- FAQ count: Count level 3 or level 4 headers in
docs/faq.md
- 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/
docs/learning-graph/book-metadata.json (or the older metadata.json) may
also contain useful identity metadata: title, author, date, and license.
Use the gh CLI or direct GitHub API to fetch repository information:
gh repo view {owner}/{repo} --json description
gh repo clone {owner}/{repo} /tmp/{repo} -- --depth 1
Step 2: Obtain or Generate Thumbnail Image
Check for existing thumbnail options in priority order:
- Existing social card image - Check
docs/img/ for social-card.png or similar
- README banner image - Parse README.md for header images
- Custom image provided by user - User may specify an image path
- 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)
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')
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

# After

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)**

Brief 1-2 sentence description of the project, its purpose, and target audience.
· XX Concepts · XX Chapters · XX MicroSims · XX Stories · XX Quizzes · XXK Words · XXX Glossary Terms · XX FAQs · XX Appendices
· <span class="completion completion-4" title="Almost Complete (4/5)"></span>
· [:octicons-mark-github-16: Repository](https://github.com/username/repo-name)
Entry Guidelines
See references/entry-format.md for examples of different entry styles, including entries with and without glossary terms, and with varying levels of detail in the description and metrics.
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
- Verify image displays correctly: Run
mkdocs serve and check the case studies page
- Check image file size: Confirm thumbnail is under 70KB
- Remove backup files (after verification):
rm docs/case-studies/img/*.backup
- Clean up temporary clone (if created):
rm -rf /tmp/{repo-name}
Example Usage
Local Filesystem Example:
User request: "Add a case study for ../xapi-course"
Remote GitHub Repository Example:
User request: "Add a case study for https://github.com/dmccreary/systems-thinking"
Process:
- For lone repo, extract: title="Systems Thinking in the Age of AI", description from README
- Find existing image or generate thumbnail
- Compress to
docs/case-studies/img/systems-thinking.jpg (~31KB)
- Generate entry:
- Insert new entry alphabetically into
docs/case-studies/index.md:
- Suggest to the user that they verify the entry with the following URL with
mkdocs serve running:
http://127.0.0.1:8000/intelligent-textbooks/case-studies/
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