About this skill

Problem

Analyzing Tomato Novel rankings directly from pages can be blocked by font encoding, anti-crawl behavior, and missing structured fields. This skill wraps the official /api/rank/category/list endpoint into a command-line workflow for reading, filtering, and exporting ranking data.

How It Works And Boundaries

It provides matching Python and Node.js runtimes, with parameters for male/female frequency, reading/new-book rankings, genre filters, result limits, output formats, and cache refresh. Common workflows include:

-g 1140 or a Chinese genre name such as 都市 for fuzzy genre filtering
-t 1 to switch to new-book rankings
-f json or -f csv for downstream aggregation
-v to expose abstract, lastChapterTitle, and update time
--filter to match keywords in title, author, or abstract

The built-in DIT_DATA font decoding table restores book titles, author names, and abstracts returned by the API. Cross-genre mode requests subcategory endpoints in parallel, then deduplicates and merges results. Retries, local cache, and --no-cache forced refresh make snapshot analysis practical: genre heat comparison, subject keyword extraction, author leaderboard distribution, serialized/completed ratio, word-count bands, update activity, and new-book rank momentum.

It does not provide historical trends, chapter text, reader comments, ratings, or author profile details. For trend tracking, pair scheduled fetching with local storage and build your own time-series model.

Screenshots

Use Cases

A web-novel operations lead compares heat water levels in the male-frequency urban genre by exporting multiple category rankings and calculating average read_count for each title.
An editor finds emerging new titles by switching to the new-book ranking, then ranks candidates by rankPosDiff to identify books climbing quickly in the current snapshot.
A data analyst exports CSV results and computes abstract keyword frequency, author leaderboard counts, and creation status distribution for subject and author analysis.
A strategy analyst compares male versus female frequency by fetching the same genre's reading and new-book rankings to measure old-book dominance versus new-book momentum.

Best For

Web-novel data operators who need weekly cross-category heat comparisons across male and female frequency genres.
Content selection editors who need to spot rising new titles using new-book rank movement and abstract keywords.
Data analysts who export ranking JSON/CSV and aggregate author counts, word-count bands, creation status, and update activity.
Strategy researchers who compare reading versus new-book rankings to assess old-book dominance and new-book momentum.