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Rank the next 50 books on a Goodreads to-read list by season with Claude

WorkedEvidence: Described by posterPosted Last checked

Claude profiled Melina Panitsidis's Goodreads ratings and ranked the next 50 to-read books into seasonal blocks.

Beginner-friendlyNo logins, payments or coding needed. Low risk: paste the prompt and you should get something useful.

How to use this

  1. Tap Copy prompt.
  2. Open Claude.
  3. Paste it into the message box.
  4. Send it, then read the answer before acting on it.
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Prompt

Original prompt

The prompt as the person shared it in the source.

What the prompt labels mean

These are the person's own words, as shared in the source.

I have attached two CSV files:
1. My Read list.
2. My To-Be-Read list.
Create my book personality profile by analyzing my reading history, my 4-star and 5-star ratings, author patterns, and recurring genres.
Using that data, filter and rank my To-Be-Read list to build a personalized roadmap of the next 50 books I should read from that list.
Evenly organize recommendations into 4 seasonal blocks based on these descriptions:
- Autumn: Atmospheric, moody, or suspenseful books with a cozy but engaging pace.
- Winter: Immersive, complex, or cozy books (slower burns or dense world-building).
- Spring: Fresh, character-driven, or thought-provoking fiction with a steady pace.
- Summer: High-energy, immersive, or breezy books (fast-paced page-turners).
Next, generate an interactive digital bookshelf widget to display the recommended books.
The design should feature:
- A warm, premium editorial color palette.
- Minimalist style cards for each book displaying Title, Author, and color-coded genre badge.
- Interactive navigation tabs at the top to toggle between the four seasons.
- A functional "Mark as Read" click action on each card that visually updates a progress ring or tracker.

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Optional stronger version

Our rewrite with extra checks. The poster didn't test it.

Before you start, ask me 1–3 short questions about the details below and wait for my answers. Once I've answered, confirm the plan in one line and run the whole task start to finish.

Goal: A ranked, seasonal reading plan built from my own reading history.

Questions to ask me first:
- Can you export your read and to-read lists?
- How many books should the plan cover?
- How do you want to group them?

Inputs (fill in or confirm):
- Read list: [CSV EXPORT]
- To-read list: [CSV EXPORT]
- Number of books: [E.G. 50]
- Grouping: [SEASONS, MOODS OR LENGTH]

Rules:
- Pick only books already on my to-read list.
- Explain each pick in one line tied to my ratings.
- Balance the groups evenly.
- Don't invent books or authors that aren't in my files.

Checkpoint: Show my book personality profile before ranking the list.

If you get stuck: If a book has too little data to place, put it in an 'unsorted' group.

Verify before you finish: Every recommended book appears in my to-read CSV.

Output: A ranked list by group plus an interactive bookshelf with a read tracker.

Done when: I always know what to read next.

AI tool

Claude

Outcome

Melina Panitsidis's Goodreads to-read list kept growing faster than it could be read, and after years of adding books Panitsidis had forgotten what was on it and wasted time scrolling for the next read. Panitsidis exported the Goodreads library and gave Claude two CSV files, the read list and the to-read list, with a prompt asking it to build a book personality profile from reading history, 4- and 5-star ratings, author patterns and recurring genres. Claude used that profile to rank the next 50 books from the to-read list, split them evenly into autumn, winter, spring and summer blocks, and build an interactive bookshelf widget with season tabs and a 'Mark as Read' progress tracker. Panitsidis now uses the list as a reference and spends less time deciding what to read next.

Evidence: Described by posterThe person describes what happened in words, without screenshots or video.

Source

Posted
by Melina Panitsidis (The Rundown community)
Last checked
Method:
How we verify

https://app.therundown.ai/community/posts/60376fcf-5919-49c4-a580-95626c96ce56

Context: Posted to The Rundown community (date from the post's UTC timestamp). The prompt is quoted verbatim from the post.

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