/next

You are helping the user decide what to read next from their Goodreads TBR list.

Analysis Steps

Use the analyze-goodreads-export skill to perform the following analysis:

1. Analyze Recent Reading Patterns

Query the last 15 books read (sorted by date_read DESC):

• Calculate average page count of recent reads
• Identify if the user has been reading mostly long books (>600 pages)
• Look for series patterns in recent reads
• Use the date_read field to determine actual reading order
• Look at my_rating field to see what books the user liked

2. Check for Series Continuity

For each series found in recent reads:

• Check if there are unread books in that series on the TBR
• Prioritize the next book in sequence (series_index), especially if the previous book had a high rating
• This is important for maintaining reading momentum!

3. Consider Reading Fatigue

Based on recent page counts:

• If average recent reads > 600 pages: Suggest shorter books (< 300 pages)
• If average recent reads < 400 pages: User might be ready for something longer
• Look for highly-rated short books as "palate cleansers"

4. Check Book Age in Library

Query books by date_added:

• Find recently added books (last 30 days) that are on TBR
• Find old books (added >1 year ago) that may have been forgotten
• Use date_added field to determine when book was added

5. Filter by Quality

Prioritize books with:

• Goodreads rating >= 3.75 (if available)
• Consider page count relative to recent reading patterns
• Balance between series continuity and variety

Output Format

Structure your response as a structured report with these categories:

# READING PATTERN SUMMARY
- Books read in last 30 days: X
- Average page count: Y pages
- Notable patterns: [e.g., "Completed The Carls series"]

# RECOMMENDATIONS BY CATEGORY

## 📚 SERIES CONTINUITY
Books that continue series you're currently reading:

- **Book Title** by Author
  Series: Series Name #X | Pages: XXX | Rating: X.X/5 | Added: [date/age]

## 🆕 RECENTLY ADDED
Books added to your TBR in the last 30 days:

- **Book Title** by Author
  Pages: XXX | Rating: X.X/5 | Added: [date]

## 💎 FORGOTTEN GEMS
Books on your TBR added over a year ago:

- **Book Title** by Author
  Pages: XXX | Rating: X.X/5 | Added: [date/years ago]

## ⚡ QUICK READS
Shorter books (< 300 pages) for reading fatigue:

- **Book Title** by Author
  Pages: XXX | Rating: X.X/5 | Added: [age]

## 🌟 HIGHLY RATED
Top-rated unread books from your TBR:

- **Book Title** by Author
  Pages: XXX | Rating: X.X/5 | Added: [age]

Important Notes

• Use date_added to determine when books were added to the library
• Calculate age from date_added (e.g., "2 days ago", "3 months ago", "2 years ago")
• Include 1-3 books per category (skip categories if no matches)
• ALWAYS check for incomplete series from recent reads first
• Balance series continuity with reading fatigue and variety
• Present data in a clean, scannable format
• Each category should help answer a different need: momentum, novelty, rediscovery, fatigue, or quality
• Only include books from the TBR list (where exclusive_shelf contains "to-read")

Implementation

Write a Python script using the goodreads_lib to:

1. Get the last 15 read books
2. Analyze patterns (page count, series, ratings)
3. Query TBR for recommendations in each category
4. Format and display results

Use the Bash tool to run your Python script.