111 lines
3.5 KiB
Markdown
111 lines
3.5 KiB
Markdown
---
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description: Analyze my reading patterns and suggest what to read next from my TBR
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---
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You are helping the user decide what to read next from their Goodreads TBR list.
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## Analysis Steps
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Use the `analyze-goodreads-export` skill to perform the following analysis:
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### 1. Analyze Recent Reading Patterns
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Query the last 15 books read (sorted by date_read DESC):
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- Calculate average page count of recent reads
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- Identify if the user has been reading mostly long books (>600 pages)
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- Look for series patterns in recent reads
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- Use the `date_read` field to determine actual reading order
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- Look at `my_rating` field to see what books the user liked
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### 2. Check for Series Continuity
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For each series found in recent reads:
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- Check if there are unread books in that series on the TBR
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- Prioritize the next book in sequence (series_index), especially if the previous book had a high rating
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- This is important for maintaining reading momentum!
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### 3. Consider Reading Fatigue
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Based on recent page counts:
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- If average recent reads > 600 pages: Suggest shorter books (< 300 pages)
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- If average recent reads < 400 pages: User might be ready for something longer
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- Look for highly-rated short books as "palate cleansers"
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### 4. Check Book Age in Library
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Query books by date_added:
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- Find recently added books (last 30 days) that are on TBR
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- Find old books (added >1 year ago) that may have been forgotten
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- Use `date_added` field to determine when book was added
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### 5. Filter by Quality
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Prioritize books with:
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- Goodreads rating >= 3.75 (if available)
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- Consider page count relative to recent reading patterns
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- Balance between series continuity and variety
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## Output Format
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Structure your response as a structured report with these categories:
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```
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# READING PATTERN SUMMARY
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- Books read in last 30 days: X
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- Average page count: Y pages
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- Notable patterns: [e.g., "Completed The Carls series"]
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# RECOMMENDATIONS BY CATEGORY
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## 📚 SERIES CONTINUITY
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Books that continue series you're currently reading:
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- **Book Title** by Author
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Series: Series Name #X | Pages: XXX | Rating: X.X/5 | Added: [date/age]
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## 🆕 RECENTLY ADDED
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Books added to your TBR in the last 30 days:
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- **Book Title** by Author
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Pages: XXX | Rating: X.X/5 | Added: [date]
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## 💎 FORGOTTEN GEMS
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Books on your TBR added over a year ago:
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- **Book Title** by Author
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Pages: XXX | Rating: X.X/5 | Added: [date/years ago]
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## ⚡ QUICK READS
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Shorter books (< 300 pages) for reading fatigue:
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- **Book Title** by Author
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Pages: XXX | Rating: X.X/5 | Added: [age]
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## 🌟 HIGHLY RATED
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Top-rated unread books from your TBR:
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- **Book Title** by Author
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Pages: XXX | Rating: X.X/5 | Added: [age]
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```
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## Important Notes
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- Use `date_added` to determine when books were added to the library
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- Calculate age from date_added (e.g., "2 days ago", "3 months ago", "2 years ago")
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- Include 1-3 books per category (skip categories if no matches)
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- ALWAYS check for incomplete series from recent reads first
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- Balance series continuity with reading fatigue and variety
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- Present data in a clean, scannable format
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- Each category should help answer a different need: momentum, novelty, rediscovery, fatigue, or quality
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- Only include books from the TBR list (where exclusive_shelf contains "to-read")
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## Implementation
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Write a Python script using the goodreads_lib to:
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1. Get the last 15 read books
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2. Analyze patterns (page count, series, ratings)
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3. Query TBR for recommendations in each category
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4. Format and display results
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Use the Bash tool to run your Python script.
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