Tomato Novel Label Combination Optimizer
Paste the following prompt into your AI chat to install this skill:
Please install @user_1f46f626/fanqie-label-optimizer according to https://skillhub.cn/install/skillhub.md.
About this skill
Problem to Solve
When publishing on Tomato Novel, tag selection often lacks verifiable guidance. Similar categories and subjects can behave very differently depending on how tags are combined. New authors may mix broad tags, trending tags, and conflicting signals, which can weaken platform recommendations or shrink the traffic pool. This skill narrows the decision to category plus subject, aiming to produce a tested tag combination rather than simply expanding keywords.
How It Works
The skill runs locally, without network access, an API key, or content collection. The workflow is:
- Confirm the novel category: male-oriented or female-oriented
- Confirm the core subject, such as suspense, romance, xuanhuan, or sci-fi
- Run the recommend command to get recommended tags and tags to avoid
- Add custom tags on top of the base combination, then merge the final set
It returns two kinds of output: recommended combinations suited to the category and subject, and risky tags that tested releases associated with traffic shrinkage. The “not recommended” list is not a platform ban; it marks combinations that performed poorly in real releases.
Boundaries
The data reflects summer 2026 real-release tests, making it useful for early tag planning on Tomato Novel. Final tags should still be adjusted for plot details, character hooks, and opening chapters. Platform algorithms can change, so for cross-category or heavily mixed subjects, use the tested output as a baseline and validate with small release experiments.
Use Cases
- A first-time Tomato Novel author needs a recommended tag set for male suspense and tags to avoid.
- After a test release, an author checks whether current tags fall into poorly performing combinations.
- An editor asks for tag-selection rationale, so recommendations and risky tags are organized by category and subject.
- An author keeps custom selling-point tags while merging a platform-friendly baseline without conflicting tags.
Best For
- First-time Tomato Novel authors who need a tested starting tag set before launch.
- Web novel writers who want to keep custom tags while merging a baseline combination.
- Editors or content operators who need category-based tag recommendations and avoidance notes.
- Local-workflow users who want no network calls or collection of creative data.
Related Skills
A long-form web novel assistant with outline locking, chapter word-count control, state updates, and consistency checks.
A step-by-step WeChat long-form writing coach that turns a proven article structure into title, layered problems, quotable insight, objection handling, and solution logic with verifiable examples.
Generates rising Douyin hotspot lists, rank changes, trend signals, and topic ideas for content planning.
Rewrite self-introductions by scenario, audience, and duration, with memory hooks, structure feedback, and speakable practice versions.