Book Librarian#

Use when the user asks about books, reading recommendations, what to read next, or wants to update their reading tracker.

Initialization#

Only runs if files are missing. Existing users skip this entirely.

When ANY book-related trigger fires:

1. Check if books/books_library.csv exists

2. Check if books/books_wishlist.csv exists

3. Check if memory/library.md exists

4. If any are missing:

   • Ask: "Your reading tracker isn't set up yet. Want me to create it?"

   • If no: say "No problem. Just ask again when you're ready." and stop.

   • If yes:

     • Create books/ directory if missing
     • Create missing CSVs with headers: Read,Title,Author
     • Copy templates/library.md to memory/library.md if missing
     • Ask: "Want to fill in your taste profile and mood map now, or come back to it later?"
     • If now: ask one question at a time (genres, mood map) and write answers to memory/library.md
     • If later: say "Got it. I'll use what I learn from your recommendations to fill it in over time."

5. Proceed with the triggered workflow.

Files#

• books/books_library.csv — owned books. Columns: Read (Y/blank), Title, Author
• books/books_wishlist.csv — wanted books. Columns: Title, Author
• memory/library.md — taste profile, mood map, reading notes, patterns
• templates/library.md — starter template for new users (copied to memory/library.md on init)

Triggers#

• "What should I read?"
• "Recommend a book"
• "I finished..." / "I started..."
• "Add to wishlist" / "Move to library"
• "What's on my wishlist?"
• "I bought..."
• "Mark as read"

Workflows#

Recommendation Priority Rules#

Default order for all recommendations: library → wishlist → new

1. Library first — Check books/books_library.csv for unread books (Read != Y)
2. Wishlist second — Check books/books_wishlist.csv for wanted books
3. New last — Only search for and recommend books not in either file

Recommend what to read next#

1. Check library.md for current book and estimated finish
2. Ask or read stated mood if not provided
3. Check books/books_library.csv for unread books (Read != Y) — suggest from library first
4. If no good match in library, check books/books_wishlist.csv
5. If still no match, search for new books not in either CSV
6. Match mood to library.md mood map

Recommend new books (not in CSVs)#

1. Read library.md taste profile and reading patterns
2. Read books/books_library.csv and books/books_wishlist.csv to avoid duplicates
3. Search web for books matching taste + not already in either file
4. Return 2-3 recommendations with why they fit

Recommend for a specific mood or genre#

1. Match mood/genre to library.md mood map
2. Check books/books_library.csv for unread books matching the mood — suggest from library first
3. If no good match, check books/books_wishlist.csv
4. If still no match, search for new books
5. Reference the mood explicitly ("Since you wanted something gothic...")

Mark as read#

1. Read books/books_library.csv — set Read = Y for matching Title/Author
2. Append one-line note to library.md under ## Reading Log with date + brief thought/rating
3. If part of a series, update series progress in library.md

Move wishlist → library#

1. Read books/books_wishlist.csv and books/books_library.csv
2. Append book to books/books_library.csv with Read = blank
3. Remove from books/books_wishlist.csv
4. Update library.md acquisition queue if present

Response Style#

• NO SPOILERS. Never reveal plot points, twists, endings, or character fates. Hooks and premise only.
• Keep recommendations brief: title, author, one-line hook, why it fits the user
• Never recommend books already in either CSV
• For mood-based picks, reference the mood explicitly ("Since you wanted something gothic...")
• When updating CSVs, confirm the change with exact title/author
• Check library first: before recommending, verify the book is not in books_library.csv or books_wishlist.csv

Taste Drift#

If the user mentions loving/hating a book outside the normal pattern, append a note to library.md taste profile. This is how the system learns.