Initial commit
This commit is contained in:
Zhongwei Li
100
commands/next.md
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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 Calibre library TBR list.
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## Analysis Steps
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Perform the following analysis using the Calibre skill:
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### 1. Analyze Recent Reading Patterns
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Query the last 15 books marked as read (sorted by *dateread 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 `*dateread` field to determine actual reading order
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- Look at `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 timestamp (when added to library):
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- Find recently added books (last 30 days) that are unread
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- Find old books (added >1 year ago) that may have been forgotten
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- Use `b.timestamp` 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 (use #dateread:">=30daysago")
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- Average page count: Y pages
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- Notable patterns: [e.g., "Completed Mistborn Era 2 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/5 | Added: [date/age]
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## 🆕 RECENTLY ADDED
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Books added to your library in the last 30 days:
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- **Book Title** by Author
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Pages: XXX | Rating: X/5 | Added: [date]
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## 💎 FORGOTTEN GEMS
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Books added over a year ago that you may have forgotten:
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- **Book Title** by Author
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Pages: XXX | Rating: 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/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/5 | Added: [age]
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```
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## Important Notes
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- Use `b.timestamp` to determine when books were added to the library
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- Calculate age from timestamp (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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- **IMPORTANT**: All queries should exclude archived books from recommendations
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110
commands/random.md
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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 Calibre library.
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## Selection Process
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Use the Calibre skill to select a random book:
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### 1. Determine Selection Pool
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By default, select from TBR list, but check if user specified:
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- **TBR** (default): `#read:No and #archived:No`
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- **All unread**: `#read:No`
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- **Entire library**: no filter
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- **Specific criteria**: by author, series, genre, rating, page count, etc.
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### 2. Query the Pool
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Query all books matching the criteria:
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```bash
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--fields='title,authors,series,series_index,*goodreads,*pages,timestamp'
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--search='[appropriate search based on user criteria]'
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--for-machine
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```
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### 3. Random Selection
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From the results:
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- Count total books in pool
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- Select one at random
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- Present it with full details
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### 4. Context About the Selection
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Provide helpful context:
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- When it was added to library (from `timestamp`)
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- Why it might be interesting to read now
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- How it fits current reading patterns
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- Series context if applicable
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## Output Format
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Present the random selection:
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```
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# 🎲 RANDOM BOOK SELECTION
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**[Book Title]** by [Author]
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## Details
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- **Series**: [Series Name #X] or "Standalone"
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- **Pages**: XXX pages
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- **Rating**: X.X / 5 (Goodreads)
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- **Added to Library**: [date or "X months/years ago"]
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## Why Read This Now?
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[Provide 2-3 reasons why this might be a good choice, such as:]
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- Fits your recent reading pattern of [genre/length/style]
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- Next book in [Series Name] series
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- Highly rated on Goodreads
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- Been in your TBR for [time], might be a good time to revisit
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- Quick read at XXX pages if you're between longer books
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- This author is similar to [recent author] you enjoyed
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## Pool Information
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Selected randomly from **X books** in your [TBR/unread books/library/specified criteria]
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---
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Not feeling it? Run /books:random again for another suggestion!
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Or try /books:vibes to find books with similar themes to one you enjoyed.
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```
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## Advanced Options
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If the user specifies additional criteria, combine them:
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**Examples:**
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- "Random book under 300 pages": Add `#pages:"<300"`
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- "Random unread Sanderson": Add `authors:"Sanderson"`
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- "Random highly rated book": Add `#goodreads:">4"`
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- "Random from series": Add `not series:""`
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- "Random standalone": Add `series:""`
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- "Random book added this year": Add `timestamp:">YYYY-01-01"`
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## Implementation Notes
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- Use Python or similar to select random index from JSON array results
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- Handle edge cases (empty pool, pool of 1)
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- If pool is small (< 10 books), mention all options
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- Exclude archived books by default unless user specifically asks
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- Make selection truly random - don't bias toward highest rated or recently added
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- If user runs command multiple times, try to avoid repeating recent suggestions
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## Example Python Snippet for Randomization
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```python
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import json
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import random
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import sys
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data = json.load(sys.stdin)
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if data:
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book = random.choice(data)
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print(json.dumps(book, indent=2))
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```
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You can pipe calibredb output through this to get random selection.
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7
commands/series.md
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7
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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Use the `books:find-incomplete-series` skill to find incomplete series. DO NOT call
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calibredb yourself, use the skill! It provides a python script to help
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you find unfinished series.
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141
commands/stats.md
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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 Calibre library.
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## Analysis to Perform
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Use the Calibre 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 `#dateread:">=YYYY-01-01"` where YYYY is current year)
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- Books read last 30 days (use `#dateread:">=30daysago"`)
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- Books read last 90 days (use `#dateread:">=90daysago"`)
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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 `rating` field)
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- Average Goodreads rating of books read (`*goodreads` 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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- Number of complete series finished
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- Books read that are part of series vs standalone
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- Most read series
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### 6. To-Be-Read Statistics
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Query TBR list (`#read:No and #archived:No`):
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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 timestamp)
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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] by XX% 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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## Query Tips
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- Use `#dateread` field with date ranges for time-based queries
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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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- Exclude archived books from all queries
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- Handle missing data gracefully (some books may not have all custom fields set)
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## Implementation Notes
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**Bash/Python Pitfalls:**
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- Multi-line bash for loops are tricky - use Python with heredoc instead for complex iteration
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- When looping through months to count books, use Python's subprocess module rather than bash for loops
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- When processing JSON data from calibredb, be careful with missing fields - always use `.get()` with defaults
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- Keep Python data processing scripts simple - avoid complex inline data structures that can have KeyError issues
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- Better to do multiple simple queries than one complex Python script with hard-coded data
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231
commands/vibes.md
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commands/vibes.md
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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 in their TBR that have similar vibes/themes to a book they specify.
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## Analysis Process
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Use the Calibre skill to find books with similar vibes:
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### 1. Get the Reference Book
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First, query for the book the user mentions (or ask them to specify one):
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```bash
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--fields='title,authors,series,tags,comments,*goodreads,*pages'
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--search='title:"[book title]"'
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```
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Get all available information:
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- Author(s)
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- Series (if part of one)
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- Tags (if any)
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- Comments/description
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- Goodreads rating
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- Page count
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### 2. Identify Similar Books
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Query TBR for books with similar characteristics:
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**Same Author**
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```bash
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--search='authors:"[author]" and #read:No and #archived:No'
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```
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**Same Series**
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```bash
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--search='series:"[series]" and #read:No and #archived:No'
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```
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**Similar Tags/Genres**
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If the reference book has tags, search for books with those tags:
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```bash
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--search='tags:"[tag]" and #read:No and #archived:No'
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```
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**Similar Page Count** (±100 pages)
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```bash
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--search='#pages:">[low]" and #pages:"<[high]" and #read:No and #archived:No'
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```
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**Similar Rating Range**
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```bash
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--search='#goodreads:">[rating-0.5]" and #goodreads:"<[rating+0.5]" and #read:No and #archived:No'
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```
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### 3. Use Available Information
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Even without formal tags, extract information from:
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- Book titles (often contain genre hints: "The [Fantasy] of...")
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- Series names (often thematic)
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- Author names (can search for co-authors, related authors)
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- Comments field (may contain descriptions with genre/theme keywords)
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### 4. Search for Community Recommendations
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Use WebSearch to find what other readers recommend as similar books:
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**Search for Reddit recommendations:**
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```
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"books like [book title]" site:reddit.com
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```
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**Search for Goodreads lists:**
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```
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"similar to [book title]" site:goodreads.com
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```
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**General recommendation searches:**
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```
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"if you liked [book title]" recommendations
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"books similar to [book title]"
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"read alikes [book title]"
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```
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Extract book titles and authors from search results, then:
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- Check if any are in the user's TBR (prioritize these)
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- Note popular recommendations even if not in TBR (user might want to add them)
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### 5. Similarity Scoring
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For each book found, explain WHY it's similar:
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- Same author
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- Same/related series
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- Shared tags/genres
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- Similar length (good for pacing match)
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||||
- Similar rating (quality match)
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- Similar themes (if detectable from metadata)
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- **Recommended by readers** (found in search results)
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## Output Format
|
||||
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||||
```
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# 📖 BOOKS WITH SIMILAR VIBES
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Finding books similar to: **[Reference Book]** by [Author]
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## About the Reference Book
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- **Series**: [Series Name] or "Standalone"
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- **Length**: XXX pages
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- **Rating**: X.X / 5
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- **Key Themes**: [Based on tags, series, title, or "Limited metadata available"]
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## Similar Books in Your TBR
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### By Same Author
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[If any found:]
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- **[Title]** ([XXX pages], rated X.X/5, added [time ago])
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Why similar: Same author, similar [length/rating/style]
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||||
### In Related Series
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[If any found:]
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- **[Title]** by [Author]
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Series: [Series Name]
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||||
Why similar: [Connection to original series]
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||||
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||||
### Similar Themes/Genres
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[If any found based on tags or metadata:]
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||||
- **[Title]** by [Author] ([XXX pages], rated X.X/5)
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Why similar: [Shared tags/themes/genre indicators]
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||||
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### Similar Length & Quality
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[Books with similar page count and rating:]
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- **[Title]** by [Author] ([XXX pages], rated X.X/5)
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Why similar: Similar reading commitment and quality
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||||
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||||
### Recommended by the Community
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||||
[Books found via web search that are in your TBR:]
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||||
- **[Title]** by [Author] ([XXX pages], rated X.X/5)
|
||||
✓ In your TBR
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||||
Why similar: Frequently mentioned on [Reddit/Goodreads/etc.] as similar to [reference book]
|
||||
|
||||
## 🎯 Top Recommendations from Your TBR
|
||||
|
||||
Based on the similarity analysis, here are the strongest matches:
|
||||
|
||||
1. **[Title]** by [Author]
|
||||
Best match because: [Specific reasons]
|
||||
|
||||
2. **[Title]** by [Author]
|
||||
Good match because: [Specific reasons]
|
||||
|
||||
3. **[Title]** by [Author]
|
||||
Worth considering: [Specific reasons]
|
||||
|
||||
## 📚 Popular Recommendations Not in Your Library
|
||||
|
||||
Based on web search, these books are frequently recommended as similar but aren't in your TBR yet:
|
||||
|
||||
1. **[Title]** by [Author]
|
||||
Why recommended: [Based on search results - Reddit threads, Goodreads lists, etc.]
|
||||
Rating: X.X / 5 on Goodreads (if found)
|
||||
|
||||
2. **[Title]** by [Author]
|
||||
Why recommended: [Reason from search results]
|
||||
|
||||
3. **[Title]** by [Author]
|
||||
Why recommended: [Reason from search results]
|
||||
|
||||
---
|
||||
💡 Tips:
|
||||
- Want to add any of these to your library? Use Calibre to import them
|
||||
- If no similar books were found in your TBR, try /books:random for a surprise pick
|
||||
- Use /books:next for personalized recommendations based on reading patterns
|
||||
```
|
||||
|
||||
## Handling Limited Metadata
|
||||
|
||||
If the reference book or TBR books have limited metadata:
|
||||
- Focus on author and series connections (most reliable)
|
||||
- Use page count for "similar reading experience"
|
||||
- Use ratings for quality matching
|
||||
- Look for patterns in titles
|
||||
- Be transparent about limitations: "Based on available metadata..."
|
||||
|
||||
## User Interaction
|
||||
|
||||
If the user just says "/books:vibes" without specifying a book:
|
||||
1. Ask them which book they want to find similar books to
|
||||
2. Suggest they could use a recent read they enjoyed
|
||||
3. Or ask them to name a book they loved
|
||||
|
||||
If they specify a book not in the library:
|
||||
1. Ask if they want to find books similar to that book's:
|
||||
- Author
|
||||
- Genre/themes (ask them to describe)
|
||||
- Length/style
|
||||
|
||||
## Query Strategy
|
||||
|
||||
1. **Start broad**: Query all potential matches from library
|
||||
2. **Search the web**: Use WebSearch to find community recommendations (Reddit, Goodreads, book blogs)
|
||||
3. **Cross-reference**: Match web recommendations against your TBR
|
||||
4. **Present both**: Show matches in TBR first, then popular recommendations not in library
|
||||
5. **Score by similarity**: Books matching multiple criteria rank higher
|
||||
6. **Prioritize quality**: Weight highly-rated books (>4.0) higher
|
||||
7. **Prioritize community picks**: Books mentioned by other readers are strong matches
|
||||
8. **Limit results**: Show top 5-10 from each category (in TBR and not in TBR)
|
||||
9. **Explain reasoning**: Always say WHY books are similar
|
||||
10. **Exclude archived**: Always add `#archived:No` unless user specifies otherwise
|
||||
11. **Include context**: Note which platform recommended the book (Reddit, Goodreads, etc.)
|
||||
|
||||
## Example Scenarios
|
||||
|
||||
**User**: "Books like Mistborn"
|
||||
- Library query: Author (Sanderson), Series (Mistborn), Tags (fantasy, magic)
|
||||
- Web search: "books like Mistborn" Reddit/Goodreads
|
||||
- Find in TBR: Other Sanderson books, high fantasy, magic systems
|
||||
- Suggest from web: Stormlight Archive, Wheel of Time, etc.
|
||||
|
||||
**User**: "Something like this cozy mystery I just finished"
|
||||
- Library query: By title/author, look for tags like "mystery", "cozy"
|
||||
- Web search: "[title] read alikes" "similar cozy mysteries"
|
||||
- Find in TBR: Similar page count, similar authors, mystery tags
|
||||
- Suggest from web: Popular cozy mystery series recommendations
|
||||
|
||||
**User**: "Books with similar vibes to Name of the Wind"
|
||||
- Library query: Author (Rothfuss), Tags (fantasy, coming-of-age)
|
||||
- Web search: "books similar to Name of the Wind" site:reddit.com
|
||||
- Find in TBR: Epic fantasy, similar length, highly-rated
|
||||
- Suggest from web: Popular epic fantasy series often recommended together
|
||||
Reference in New Issue
Block a user