Tomato Novel DeAI Review Assistant
Paste the following prompt into your AI chat to install this skill:
Install @user_170e8f72/novel-deai-tomato-pass according to https://skillhub.cn/install/skillhub.md.
About this skill
Problem
Targets .docx web novels for Tomato and similar platforms that fail chapter review, show visible AI traces, miss the 80,000-word recommendation evaluation, or underperform in traffic milestones. It focuses on policy-hard issues such as polygamous harem tropes, sexual implication, body objectification, violent threats, and vulgar titles, as well as filler words, formulaic transitions, emotional overstatement, and repetitive paragraph structures.
How It Works
The skill runs a review pipeline: back up and extract the source, scan trigger words into MUST and WARN issues, confirm a relationship-reconstruction direction, then apply general safe replacements plus book-specific mappings. De-AI processing has four stages: L1 mechanical cleanup, L2 chapter-level diagnostics, L2.5 safe automatic rewrites with before-and-after scores, and L3 template-driven manual polishing. Evaluation and traffic checks cover word count, paragraph length, dialogue ratio, first-three-chapter hooks within 300-1000 words, update cadence, validation, debut, and book-test milestones.
Boundaries
It processes text, book titles, and chapter titles only. It does not edit cover images or automatically clear copyright and political-sensitivity databases. Cover and copyright items require manual confirmation. Semantic issues such as character relationships, plot revisions, and voice consistency still require human judgment following the SOP, and the original file must remain untouched.
Use Cases
- An author receives a chapter rejection and must locate harem or vulgar trigger terms, then reframe the relationship map to pass review.
- A serial editor processes a long .docx novel and must remove filler words, clichéd metaphors, and weak dialogue tags.
- A writer preparing the 80,000-word recommendation check must verify first-chapter hooks, word counts, and pacing.
- A content operator reviews retention, clicks, and shelf-save data, then revises title, synopsis, golden chapters, and update cadence.
Best For
- A Tomato long-form author whose multi-partner setup is rejected and needs a review-safe single-partner reframe while keeping the plot.
- A web-novel editor handling .docx manuscripts who needs AI-tone cleanup and deeper tell-not-show diagnostics.
- A recommendation operator whose approved book underperforms and needs title, synopsis, golden-chapter, and update-cadence fixes.
- A freelance writer preparing for the 80,000-word evaluation who needs word-count, dialogue-ratio, paragraph-length, and hook checks.
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