Anthropic Skilljar Course Extractor

Step 1: Run the Extraction Script

python3 scripts/extract_course.py \
  <skilljar_url> <output_dir>

This produces:

<course-slug>/
├── README.md
├── 01-<section>/
│   ├── 01-<lesson>.md
│   └── ...
└── assets/images/   ← downloaded .webp diagrams

Step 2: Describe Diagrams

For each full-size .webp in assets/images/ (not -medium.webp):

• Use the Read tool to view it visually
• Add a detailed alt-text block and embed with ![desc](../assets/images/file.webp) in the relevant lesson markdown

Assign images to lessons based on topic — architecture diagrams go in intro lessons, code screenshots in hands-on lessons, summary slides in review lessons.

Step 3: Enrich Lesson Markdown

The script embeds bullet-point notes. Expand each lesson file:

• Add proper ## headings per concept
• Format code examples in fenced code blocks with language tags
• Convert "Key: value" lines to **Key:** value bold format
• Add tables for comparisons (e.g., message types, primitives)
• Keep the frontmatter block (Section, URL, Lesson Type)

Step 4: Enhance README

Add to the auto-generated README:

• Full course description paragraph
• Key concepts reference table
• Quick-reference code snippets
• Diagram index with one-line descriptions per image

How the Script Works (for debugging)

• Fetches page HTML; extracts window.__chatData (LLM notes embedded in every Skilljar page)
• Parses sidebar <ul class="dp-curriculum"> for section/lesson structure
• Downloads CloudFront images from d7juhi4i8fsw0.cloudfront.net/images/<slug>/
• Deduplicates lessons by URL (curriculum HTML appears twice in page source)
• Fuzzy-matches notes to lessons by title similarity

If Login Is Required

Individual lesson pages always require auth — do not fetch them. If the course landing page itself requires login (script finds 0 lessons):

1. Ask user to save the page HTML (browser → Save As → Webpage, HTML Only)
2. Modify script's fetch() to open(path).read() and pass the local file