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This commit is contained in:
Zhongwei Li
110
commands/next.md
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110
commands/next.md
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---
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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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85
commands/random.md
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85
commands/random.md
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---
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description: Pick a random book from TBR or library
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---
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You are helping the user pick a random book from their Goodreads library.
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## Task
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Use the `analyze-goodreads-skill` skill to pick a random book from their TBR list.
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## Implementation
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Write a Python script using goodreads_lib:
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```python
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#!/usr/bin/env python3
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import sys
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import random
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sys.path.insert(0, '__SKILL_DIR__/scripts')
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from goodreads_lib import GoodreadsLibrary
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lib = GoodreadsLibrary()
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# Get TBR books
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tbr = lib.get_tbr_books()
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if not tbr:
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print("No books in your TBR!")
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sys.exit(0)
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# Pick random book
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book = random.choice(tbr)
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# Display
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print("\n# 🎲 RANDOM TBR PICK\n")
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print(f"**{book.title}**")
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print(f"by {book.author}\n")
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if book.series and book.series_index:
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print(f"📚 Series: {book.series} #{book.series_index}")
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if book.num_pages:
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print(f"📖 Pages: {book.num_pages}")
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if book.average_rating:
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print(f"⭐ Goodreads Rating: {book.average_rating:.2f}/5")
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if book.date_added:
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from datetime import datetime
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days_ago = (datetime.now() - book.date_added).days
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if days_ago < 30:
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print(f"🆕 Added: {days_ago} days ago")
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elif days_ago < 365:
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months = days_ago // 30
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print(f"📅 Added: {months} months ago")
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else:
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years = days_ago // 365
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print(f"📅 Added: {years} years ago")
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print(f"\nTotal TBR books: {len(tbr)}")
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```
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## Output Format
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```
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# 🎲 RANDOM TBR PICK
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**Book Title**
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by Author Name
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📚 Series: Series Name #X
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📖 Pages: XXX
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⭐ Goodreads Rating: X.XX/5
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📅 Added: X months/years ago
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Total TBR books: XX
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```
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## Important Notes
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- Pick from books where `is_tbr` is True
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- Display relevant metadata (series, pages, rating, date added)
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- Handle missing data gracefully
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- Use the Bash tool to run your Python script
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- Replace `__SKILL_DIR__` with the actual skill directory path
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80
commands/series.md
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80
commands/series.md
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---
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description: List unfinished series and the next book to read in each
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---
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You are helping the user find incomplete series in their Goodreads library.
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## Task
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Use the `analyze-goodreads-export` skill to find series where:
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- The user has read at least one book in the series
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- There are still unread books in the series
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- Display the next book to read in each series
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## Implementation
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Write a Python script using goodreads_lib:
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```python
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#!/usr/bin/env python3
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import sys
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sys.path.insert(0, '__SKILL_DIR__/scripts')
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from goodreads_lib import GoodreadsLibrary
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lib = GoodreadsLibrary()
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# Get incomplete series
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incomplete = lib.get_incomplete_series()
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# Sort by series name
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incomplete_sorted = sorted(incomplete.items(), key=lambda x: x[0])
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# Display results
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print("\n# UNFINISHED SERIES\n")
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print(f"You have {len(incomplete_sorted)} incomplete series:\n")
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for i, (series_name, info) in enumerate(incomplete_sorted, 1):
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read = info['read_count']
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total = info['total_count']
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next_book = info['next_book']
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print(f"{i}. **{series_name}** ({read}/{total} books read)")
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if next_book:
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title = next_book.title
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author = next_book.author
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index = next_book.series_index or '?'
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pages = next_book.num_pages or '?'
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rating = f"{next_book.average_rating:.2f}" if next_book.average_rating else "N/A"
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print(f" Next: **{title}** by {author}")
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print(f" Book #{index} | {pages} pages | Goodreads: {rating}/5\n")
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else:
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print(f" Next: Unable to determine\n")
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```
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## Output Format
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```
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# UNFINISHED SERIES
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You have X incomplete series:
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1. **Series Name** (X/Y books read)
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Next: **Book Title** by Author
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Book #X | XXX pages | Goodreads: X.XX/5
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2. **Another Series** (X/Y books read)
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Next: **Book Title** by Author
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Book #X | XXX pages | Goodreads: X.XX/5
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```
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## Important Notes
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- Series information is parsed from book titles (e.g., "Title (Series, #1)")
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- Books are sorted by series_index within each series
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- Only show series where at least one book has been read
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- The "next" book is the first unread book in series order
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- Handle missing data gracefully (pages, ratings may be missing)
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- Use the Bash tool to run your Python script
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- Replace `__SKILL_DIR__` with the actual skill directory path
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141
commands/stats.md
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141
commands/stats.md
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---
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description: Show reading statistics (books per year/month, pages read, average rating, genre breakdown)
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---
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You are helping the user analyze their reading statistics from their Goodreads library.
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## Analysis to Perform
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Use the `analyze-goodreads-export` skill to gather and analyze the following statistics:
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### 1. Reading Velocity
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Query books read in different time periods:
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- Books read this year (use `date_read.year == current_year`)
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- Books read last 30 days
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- Books read last 90 days
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- Break down by month for current year
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Calculate:
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- Books per month average (current year)
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- Pages per month average
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- Current reading pace vs yearly average
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### 2. Page Statistics
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Query all read books with page counts:
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- Total pages read this year
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- Total pages read all time
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- Average pages per book
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- Longest book read
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- Shortest book read
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### 3. Rating Analysis
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Query all read books with ratings:
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- Average rating given (your `my_rating` field)
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- Average Goodreads rating of books read (`average_rating` field)
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- Most common rating you give
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- Distribution of ratings (how many 5-star, 4-star, etc.)
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### 4. Author Statistics
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Query all read books:
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- Most read authors (count by author name)
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- Total unique authors read
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### 5. Series Statistics
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Query all read books with series information:
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- Books read that are part of series vs standalone
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- Most read series
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- Number of complete series finished
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### 6. To-Be-Read Statistics
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Query TBR list (where `is_tbr` is True):
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- Total books in TBR
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- Total pages in TBR
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- Average Goodreads rating of TBR
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- Oldest book in TBR (by date_added)
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- Books added to TBR in last 30 days
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## Output Format
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Present statistics in a clean, organized report:
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```
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# READING STATISTICS
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## 📊 Reading Velocity
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- **This Year**: X books (Y pages)
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- **Last 30 Days**: X books (Y pages)
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- **Average Pace**: X books/month, Y pages/month
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### Monthly Breakdown (YYYY)
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Jan: X books | Feb: X books | Mar: X books | etc.
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## 📖 Page Statistics
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- **Total Pages Read (All Time)**: X,XXX pages
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- **Total Pages Read (This Year)**: X,XXX pages
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- **Average Book Length**: XXX pages
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- **Longest Book**: [Title] by [Author] (XXX pages)
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- **Shortest Book**: [Title] by [Author] (XXX pages)
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## ⭐ Rating Analysis
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- **Your Average Rating**: X.X / 5
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- **Goodreads Average of Books Read**: X.X / 5
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- **Most Common Rating**: X stars
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### Rating Distribution
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★★★★★: XX books (XX%)
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★★★★☆: XX books (XX%)
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★★★☆☆: XX books (XX%)
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★★☆☆☆: XX books (XX%)
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★☆☆☆☆: XX books (XX%)
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## ✍️ Author Statistics
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- **Total Authors Read**: XX unique authors
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- **Most Read Authors**:
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1. [Author Name]: X books
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2. [Author Name]: X books
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3. [Author Name]: X books
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## 📚 Series Statistics
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- **Books in Series**: XX books (XX% of total)
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- **Standalone Books**: XX books (XX% of total)
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- **Most Read Series**:
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1. [Series Name]: X books
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2. [Series Name]: X books
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## 📋 To-Be-Read Statistics
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- **Total TBR Books**: XXX books (X,XXX pages)
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- **Average TBR Rating**: X.X / 5
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- **Added Recently**: XX books in last 30 days
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- **Oldest Unread**: [Title] (added X years/months ago)
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## 🎯 Reading Insights
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[Provide 2-3 interesting insights, such as:]
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- You're on track to read XX books this year
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- Your reading pace has [increased/decreased] compared to last year
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- You tend to rate books higher/lower than Goodreads average
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- You're reading more/fewer series books than standalone
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```
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## Implementation
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Write a Python script using goodreads_lib to:
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1. Query all read books
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2. Calculate statistics for each category
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3. Format and display results with proper formatting
|
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Use the Bash tool to run your Python script.
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## Important Notes
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- Use `date_read` field to determine if/when book was read
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- Calculate percentages and averages from the data
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- Present large numbers with thousand separators for readability
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- Compare current year to all-time averages where interesting
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- Handle missing data gracefully (some books may not have all fields)
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- Round floating point values to 2 decimal places for readability
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125
commands/vibes.md
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125
commands/vibes.md
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@@ -0,0 +1,125 @@
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---
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description: Find similar books in your library based on genre, author, or themes
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---
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You are helping the user find books similar to one they specify.
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## Task
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When the user asks to find books with similar "vibes" or similar to a specific book:
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1. Ask them what book they want to find similar books to (if not already specified)
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2. Use the `analyze-goodreads-export` skill to search for that book in their library
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3. Find similar books based on:
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- Same author
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- Books on the same custom shelves (genre indicators)
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- Similar series (if applicable)
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- Similar page count
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- Similar ratings
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||||
|
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## Implementation
|
||||
|
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Write a Python script using goodreads_lib:
|
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|
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```python
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#!/usr/bin/env python3
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import sys
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sys.path.insert(0, '__SKILL_DIR__/scripts')
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from goodreads_lib import GoodreadsLibrary
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lib = GoodreadsLibrary()
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# Get the reference book (user should specify)
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search_term = "BOOK_TITLE" # Replace with user's input
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# Find the book
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matches = [b for b in lib.books if search_term.lower() in b.title.lower()]
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if not matches:
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print(f"Could not find '{search_term}' in your library")
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sys.exit(1)
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ref_book = matches[0]
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print(f"\n# BOOKS SIMILAR TO: {ref_book.title}\n")
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# Find similar books
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similar = []
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# 1. Same author
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if ref_book.author:
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same_author = [b for b in lib.books
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if b.author == ref_book.author
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and b.book_id != ref_book.book_id
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and not b.is_read]
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if same_author:
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print(f"## 📚 More by {ref_book.author}\n")
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for book in same_author[:3]:
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pages = f"{book.num_pages} pages" if book.num_pages else "? pages"
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rating = f"{book.average_rating:.2f}/5" if book.average_rating else "N/A"
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print(f"- **{book.title}** | {pages} | ⭐ {rating}")
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print()
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# 2. Same shelves (genre indicators)
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if ref_book.bookshelves:
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shelves = ref_book.bookshelves.split(',')
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for shelf in shelves[:2]: # Check first 2 shelves
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||||
shelf = shelf.strip()
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||||
if shelf:
|
||||
same_shelf = [b for b in lib.books
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||||
if shelf in b.bookshelves
|
||||
and b.book_id != ref_book.book_id
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and not b.is_read]
|
||||
if same_shelf:
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print(f"## 🏷️ Similar (from '{shelf}' shelf)\n")
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||||
for book in same_shelf[:3]:
|
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pages = f"{book.num_pages} pages" if book.num_pages else "? pages"
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rating = f"{book.average_rating:.2f}/5" if book.average_rating else "N/A"
|
||||
print(f"- **{book.title}** by {book.author} | {pages} | ⭐ {rating}")
|
||||
print()
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||||
|
||||
# 3. Similar series (if in a series)
|
||||
if ref_book.series:
|
||||
series_books = lib.get_series_books(ref_book.series)
|
||||
unread_in_series = [b for b in series_books if not b.is_read]
|
||||
if unread_in_series:
|
||||
print(f"## 📖 More in {ref_book.series}\n")
|
||||
for book in unread_in_series[:3]:
|
||||
idx = f"#{book.series_index}" if book.series_index else ""
|
||||
pages = f"{book.num_pages} pages" if book.num_pages else "? pages"
|
||||
rating = f"{book.average_rating:.2f}/5" if book.average_rating else "N/A"
|
||||
print(f"- **{book.title}** {idx} | {pages} | ⭐ {rating}")
|
||||
print()
|
||||
```
|
||||
|
||||
## Output Format
|
||||
|
||||
```
|
||||
# BOOKS SIMILAR TO: Reference Book Title
|
||||
|
||||
## 📚 More by Author Name
|
||||
|
||||
- **Book Title** | XXX pages | ⭐ X.XX/5
|
||||
- **Book Title** | XXX pages | ⭐ X.XX/5
|
||||
|
||||
## 🏷️ Similar (from 'genre' shelf)
|
||||
|
||||
- **Book Title** by Author | XXX pages | ⭐ X.XX/5
|
||||
- **Book Title** by Author | XXX pages | ⭐ X.XX/5
|
||||
|
||||
## 📖 More in Series Name
|
||||
|
||||
- **Book Title** #X | XXX pages | ⭐ X.XX/5
|
||||
```
|
||||
|
||||
## Important Notes
|
||||
|
||||
- Ask the user which book they want to find similar books to
|
||||
- Search is case-insensitive for the title
|
||||
- Prioritize unread books in results
|
||||
- Custom shelves can indicate genres (e.g., "mental-health", "favorites")
|
||||
- Limit results to 3 per category for readability
|
||||
- Handle missing data gracefully
|
||||
- Use the Bash tool to run your Python script
|
||||
- Replace `__SKILL_DIR__` with the actual skill directory path
|
||||
- Replace `BOOK_TITLE` with the user's search term
|
||||
Reference in New Issue
Block a user