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Zhongwei Li
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commands/stats.md
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commands/stats.md
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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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