The method

How a Map gets built.

Three signals, scanned in parallel. One editorial pass to write the verdict. Read it like a developmental editor would read a manuscript report — because that's the standard we're trying to meet.

01 · Supply — what's already winning

Every Amazon bestseller in your subgenre. Their tropes, blurbs, cover language, review counts. The whole competitive landscape, read in full and structured for you.

We scrape the top of every relevant Amazon list monthly, then parse each blurb and cover for the tropes, archetypes, settings, and language patterns that are actually moving units right now. Then we cross-reference review counts, time-on-list, and price band so a hot trope on a $0.99 promo doesn't outrank a steady seller at $4.99.

What you see in the Map: ranked tropes by lane (hot / warming / soft / oversupplied), the top 3-5 comp titles in your specific cell, the patterns repeating in winning blurbs and covers.

02 · Demand — what readers ask for

Reddit threads, Goodreads reviews, BookTok captions. The "I want a book that…" voice in the reader's own words. We mine it, score it, surface the gaps.

Bestseller data tells you what worked; reader voice tells you what's missing. We track every "looking for a book where…" thread across r/RomanceBooks and siblings, every 4-and-5-star Goodreads review that articulates what the reader loved, and the BookTok captions that drove the latest reading-list spikes. Same readers, but their wishlists run ahead of the bestseller list.

That delta — where readers are asking for X but the shelves are full of Y — is the most useful signal we ship. It's also the hardest to game.

03 · Vocabulary — the words readers use

The Goodreads shelves real readers create. Your blurb keywords, ad targeting, metadata. Words that find your readers, not words your editor invented.

If your blurb says "enemies-to-lovers" but readers are shelving the books they love as "I hated him then I didn't", your discovery is broken. We map the actual shelf-name and tag vocabulary readers use in your lane, then suggest the phrases to drop into your blurb, your Amazon keywords, and your ad targeting so the right people can find you.

04 · The verdict

Each signal gets its own pass. Then one editorial step compares them, looks for the agreements and the contradictions, and writes the Map — a structured document a developmental editor would recognise, not a wall of bullet points.

You get a complete pre-launch blueprint — around twenty sections covering a go/no-go opportunity score, the ranked trope stack with demand-vs-supply, FMC and MMC archetypes, the open market gap, comp titles three ways, cover direction, title and character-name ideas, the book-length sweet spot, reader vocabulary, the categories you can rank in, market economics, risk flags, and a step-by-step action plan. Same skeleton every time; the verdict changes.

Who it's for

  • Indie romance authors deciding what to write next — or whether to shelve a half-drafted project.
  • Series-planners working out the spin-off or the next lane.
  • Writers coming back after a break who need to know what's moved in the last six months.
  • Anyone who'd rather spend the week writing than scrolling Goodreads.

Who it's not for

  • People looking for a ghost-writer or an AI to draft the manuscript. We don't do that.
  • Authors who've already locked their next book — a Map is most useful before the outline.
  • Anyone outside romance and its adjacent lanes. The model is tuned for this market specifically; we'd rather say no than ship something thin.
Tropesmith is a research tool, not a writing tool. The taste, the prose, the book — that's still you. We just keep the field notes.

Build my Map

Our data pipeline, plainly

Four signal layers, weighted by how loudly readers actually speak.

A Map isn't a guess about what's selling. It's a triangulation across four independent reader-voice channels — each scraped, structured, and ranked by signal strength. Here's what feeds the engine right now.

Layer 1 · Virality
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BookTok plays observed
Weighted highest 82 hashtags, 8,013 viral videos
Layer 2 · Explicit demand
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structured reader asks
Reddit + Goodreads + BookTok Parsed by Claude into trope + heat + setting
Layer 3 · Reader praise
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Goodreads shelf signals
11,843 reviews mined What individual readers shelve and recommend
Layer 4 · Supply scan
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bestsellers analyzed
34,219 book-trope mappings To find the gap between demand and what's published
Live BookTok signals feeding the engine
#darkromance 127M plays #bridgerton 61M plays #romancetok 45M plays #icebreaker 38M plays #romancebooks 35M plays #acotar 14M plays #darkromancebooks 10M plays #workplaceromance 7M plays #romantasy 7M plays
How the weighting works

Each demand signal is scored on strength (how many readers, how recently, how unambiguously). BookTok virality wins on volume — a video with millions of plays compresses thousands of reader endorsements into one data point. Reddit asks score next: lower volume, but each post is an explicit reader request. Goodreads adds depth per book. Amazon tells us where the market is over-supplied — that's the gap your Map points you toward.

Pipeline runs continuously. Numbers reflect the live corpus as of .

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