Learning System

Purpose

Transform collected learning resources (URLs + notes) into structured Ship-Learn-Next learning paths.

Usage

Call this skill with a markdown file containing learning resources:

process learning from AI/Inbox.md

The file can contain:

• YouTube URLs
• Web article URLs
• PDF file paths
• Your own notes and thoughts about topics
• Any mix of the above

Workflow

Phase 1: Parse & Extract

1. Read the inbox file

   • Parse markdown content
   • Identify URLs (YouTube, web articles)
   • Extract user notes (text content, bullet points)
   • Report summary: "Found X YouTube videos, Y articles, Z paragraphs of notes"

2. Extract content from URLs

   • For YouTube URLs: Call youtube-transcript skill
   • For web article URLs: Call article-extractor skill
   • For PDF paths: Read and extract text directly
   • Process in parallel where possible
   • Report failures gracefully: "Extracted X/Y sources (Z failed)"
   • Continue processing even if some extractions fail

Phase 2: Analyze & Cluster

3. Unified analysis

   • Analyze ALL content together:
     • Extracted transcripts and articles
     • User's original notes
     • Context from markdown structure (headings, sections)

4. Identify learning clusters

   • Look for topic patterns and themes
   • Group related sources together
   • Consider user's notes as strong signals for clustering
   • Create proposed clusters with:
     • Suggested cluster name
     • List of URLs belonging to cluster
     • List of user notes related to cluster
     • Rationale for grouping

Phase 3: Interactive Approval

5. Present clusters one by one

   • For each cluster, show:

     Cluster: "[Suggested Name]"
     
     Sources:
     - YouTube: "[Title]" (transcript available)
     - Article: "[Title]" (from [domain])
     - Your note: "[first 100 chars...]"
     
     Rationale: [Why these are grouped together]
     
     Options:
     1. Approve as-is
     2. Rename cluster
     3. Split into multiple topics
     4. Merge with another cluster
     5. Exclude some sources
     

6. Handle user feedback

   • Listen for natural language responses:

     • "Approve" / "looks good" → Accept cluster
     • "Rename to [X]" → Change cluster name
     • "Split - [reason]" → Divide cluster
     • "Merge with cluster N" → Combine clusters
     • "Remove [source]" → Exclude specific item

   • Apply changes immediately and confirm

   • Continue to next cluster

   • Keep track of all decisions

7. Final confirmation

   • Show summary of all approved clusters
   • Confirm: "Ready to create N learning paths in 3_Resources/?"
   • Wait for explicit approval

Phase 4: Create Learning Paths

8. Check for existing paths

   • For each cluster, check if 3_Resources/[topic-name]/ exists
   • If exists: This is a MERGE operation (add to existing path)
   • If new: This is a CREATE operation

9. Create folder structure

   • For NEW paths:

     3_Resources/[topic-name]/
     ├── README.md              # Ship-Learn-Next plan
     ├── sources/               # Extracted content
     │   ├── [source-1].md
     │   └── [source-2].md
     └── notes/                 # User's notes
         └── [note].md
     

   • For MERGE operations:

     • Add new source files to sources/
     • Add new notes to notes/
     • Update README.md with new content

10. Generate Ship-Learn-Next plan

    • Use the template from ship-learn-next-template.md

    • Fill in:

      • Title: Cluster name
      • Overview: Brief description of learning path
      • Ship section: 3-5 practical projects to build
      • Learn section: Links to all sources and notes
      • Next section: Advanced topics and next steps
      • Topics tags: Extracted keywords
      • Status: "seedling" for new paths

    • For MERGE: Update existing sections intelligently:

      • Add new items to Ship/Learn sections
      • Update "Last Updated" date
      • Preserve existing progress tracking

11. Save extracted content

    • For each source, create markdown file in sources/:

      ---
      source: [original URL]
      type: [youtube/article/pdf]
      title: [extracted title]
      extracted: [date]
      ---
      
      # [Title]
      
      [Extracted content]
      

    • For user notes, create file in notes/:

      ---
      from: [original inbox file]
      extracted: [date]
      ---
      
      [User's note content]
      

12. Handle multi-topic sources

    • If a source belongs to multiple clusters:
      • Save full content in PRIMARY cluster (user specified during approval)
      • Add reference link in SECONDARY clusters:

        ## Related Resources
        See also: [[../[primary-topic]/sources/[file]|[Title]]]
        

Phase 5: Cleanup & Tracking

13. Update inbox file

    • Move processed items to "## Processed (DATE)" section
    • Mark with [x] checkboxes
    • Add path reference: → [topic-name]
    • Example:

      ## Processed (2025-11-22)
      - [x] https://youtube.com/watch?v=abc → kubernetes-networking
      - [x] My notes about service mesh → kubernetes-networking
      

    • Preserve unprocessed items in original location

14. Update resources index

    • Add new learning paths to 3_Resources/index.md
    • Format:

      - [[kubernetes-networking/README|Kubernetes Networking]] 🌱 seedling
      

    • For merged paths: Update "Last Updated" timestamp

15. Generate summary report

    ✅ Processing Complete!
    
    Created 2 new learning paths:
    - 3_Resources/kubernetes-networking/ (3 sources, 1 note)
    - 3_Resources/rust-async/ (2 sources, 2 notes)
    
    Merged into 1 existing path:
    - 3_Resources/llm-agents/ (+2 sources)
    
    Processed: 7/8 sources
    Failed: 1 (paywalled article - saved URL for manual review)
    
    Next steps:
    - Review learning plans in 3_Resources/
    - Start with "Ship" sections for hands-on learning
    - Update progress as you learn
    

Error Handling

Extraction Failures

• If YouTube transcript unavailable:

  • Report in summary
  • Save URL in learning path with note: "⚠️ Transcript unavailable - watch manually"
  • Continue processing

• If article blocked/paywalled:

  • Report in summary
  • Save URL with note: "⚠️ Manual extraction needed"
  • Continue processing

• If PDF unreadable:

  • Report in summary
  • Save file path with note: "⚠️ Text extraction failed"
  • Continue processing

Clustering Issues

• If sources too diverse (no clear clusters):

  • Report: "Sources are too diverse for automatic clustering"
  • Offer: "Create individual learning paths for each source?" or "Group all into 'Mixed Topics' for manual organization?"
  • Let user decide

• If only 1-2 sources:

  • Still create learning path but note it's minimal
  • Suggest: "This is a small learning path - consider collecting more resources before studying"

Ambiguous Classifications

• If source could fit multiple topics equally:
  • Present during approval: "This resource covers both X and Y equally - which should be primary?"
  • Wait for user decision
  • Apply primary/secondary reference strategy

Existing Path Collisions

• ALWAYS merge into existing paths
• Report: "Merged X new sources into existing [topic-name]/"
• Show what was added in summary

Best Practices

1. Process regularly: Run weekly or when you have 5+ resources collected
2. Add context: Include your thoughts in the inbox - helps clustering
3. Review plans: The generated Ship-Learn-Next plans are starting points - refine them
4. Update progress: Check off items as you learn, update completion %
5. Merge related paths: If you later realize two paths should be one, manually merge folders
6. Archive completed: Move finished learning paths to 4_Archives/resources/

Technical Notes

• Depends on: youtube-transcript and article-extractor skills
• Creates folders with kebab-case naming (e.g., kubernetes-networking)
• Uses Obsidian wikilinks for cross-references
• Compatible with Dataview queries for tracking
• Preserves frontmatter for metadata management
• Status progression: seedling 🌱 → sapling 🌿 → evergreen 🌳 (update manually as path matures)

Example Session

User: process learning from AI/Inbox.md