Agent skill

Web Novel Ranking Scanner

by zenstory-ai in zenstory-ai/oh-story-claudecode

Analyzes ranking-list data from Chinese web novel platforms to spot repeating genres, title patterns and opening hooks, then writes a market report for authors.

MITAuto-check passedWriting & Content

SKILL.md written in Chinese; this summary is our English description.

Install Web Novel Ranking Scanner

skills CLI
$ npx skills add zenstory-ai/oh-story-claudecode --skill story-long-scan -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install zenstory-ai/oh-story-claudecode story-long-scan --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/zenstory-ai/oh-story-claudecode.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/story-long-scan .claude/skills/story-long-scan && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
story-long-scan
GitHub stars
7.4k
Token cost
~1.3k tokens
SKILL.md length
258 words
Files
17 (incl. scripts, references)
Skills in repo
13
Repo updated
First seen
Licence
MIT

At a glance

Analyzes ranking-list data from Chinese web novel platforms to spot repeating genres, title patterns and opening hooks, then writes a market report for authors.

  • Works in 5 steps: :确认平台和方向 → :确定数据来源 → :数据分析 → …
  • Finding which web novel genres are trending on a given platform
  • SKILL.md covers 核心哲学, 扫榜流程, 平台特性速查 and 流程衔接, plus 2 more sections
  • Runs JavaScript scripts from its folder; calls node

What it does

The SKILL.md is written in Chinese. The agent acts as a market analyst for long-form web novels. It asks which platform you care about (Qidian, Fanqie, Jinjiang, Qimao, Ciweimao or another) and whether you have a genre in mind, then gathers data in order of preference: bundled scraper scripts for each platform, rankings you paste or link yourself, or built-in trend knowledge when there is no network. Built-in knowledge is labeled as historical and only a candidate hypothesis until live rankings are rechecked.

Scraped rankings go into a dated output folder, one per scan, and `aggregate-rank.js` combines them into a single summary covering genre shares, median heat and word count, tag keywords, common title words, overlap across lists and top example books per genre. Directions with fewer than 15 books, or 10 on small platforms, are flagged as sparse. The analysis then looks at genre distribution, new-genre signals, changes in established genres, word counts, title patterns, opening hooks and new character or plot elements.

The core rule is that one book's rank is only a clue, while a pattern repeating across lists and books counts as a signal, and platforms are judged by different measures, such as completion rate on Fanqie, subscriptions on Qidian and favorites on Jinjiang. The final report speaks to the author about market conclusions and workable genres rather than scripts or commands, and it is also saved into a topic-decision file in the output folder.

When your agent uses it

  • Finding which web novel genres are trending on a given platform
  • Comparing ranking patterns across Qidian, Fanqie and Jinjiang
  • Choosing a genre for a new long-form novel from ranking evidence

Example prompts

  • “Scan the Qidian rankings and tell me which genres are rising.”
  • “Compare the Fanqie and Jinjiang charts and say which genres suit a first novel.”
  • “Tell me what is selling in long-form web novels, starting with Fanqie.”
  • “Aggregate the ranking files I pasted and report the most common title words.”

Requirements

  • Node.js to run the scraper and aggregation scripts
  • Chrome started through /browser-cdp for platforms that need a browser session
  • Network access to the ranking pages

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. :确认平台和方向
  2. :确定数据来源
  3. :数据分析
  4. :输出扫榜报告
  5. :选题决策

What it can do on your machine

Read from SKILL.md and the folder at commit 2cf7be6. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 7 files in scripts/ (JavaScript), which the agent can run.

    Shell commands in SKILL.md call:

    • node

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Web Novel Ranking Scanner loads about 1.3k tokens when it runs, and up to ~9.6k if it reads all its reference files. Until then it costs about 27 tokens; SKILL.md has 258 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~27
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from zenstory-ai/oh-story-claudecode at commit 2cf7be6, republished under its MIT licence (© zenstory-ai). 258 words, ~1,293 tokens.

Download SKILL.mdSave it as .claude/skills/story-long-scan/SKILL.md (or your agent's skills folder). This skill also uses 16 other files; get the full folder from GitHub.
name
story-long-scan
description
长篇网文扫榜。分析起点、番茄、晋江、七猫等平台排行榜与新书数据,提炼市场趋势与热门题材。触发方式:/story-long-scan、/长篇扫榜、「长篇什么火」「起点排行」「扫七猫新书」。
version
1.0.0

story-long-scan:长篇网文扫榜

你是网络小说市场分析师。你的任务是基于榜单样本识别长篇网文市场格局,并输出可执行的题材候选、风险阈值和验证动作。

核心信念:单本排名只提供线索;跨样本重复模式才算信号。 排行榜只能证明样本存在;必须通过多榜单、多作品和近期数据判断需求强度。


核心哲学

原则 1:扫榜看模式,别只看排名

排名会波动,模式必须用重复样本验证。扫榜要提取:反复出现的题材、设定、套路、书名词和开篇卖点。单本上榜只能记为个例;同类样本达到可比数量后,才能标记为趋势候选。

原则 2:流量型平台和付费型平台看的东西不同

番茄看的是流量和完读率,起点看的是订阅和追读,晋江看的是收藏和积分。不同平台的成功标准不同,扫榜方法也不同。

原则 3:扫榜的目的是找到你能写的爆款题材

不按热度直接给结论。每个方向都要做项目可行性判断:素材储备、题材边界、篇幅承载、目标平台样本是否足够。


扫榜流程

Phase 1:确认平台和方向

问用户:「你想看哪个平台?(起点/番茄/晋江/七猫/刺猬猫/其他)有没有关注的题材方向?」

关键判断:

  • 用户已有方向 → 针对该方向做深度扫榜
  • 用户没有方向 → 做全榜概览 + 找趋势
  • 用户想跨平台比较 → 做平台对比分析

Phase 2:确定数据来源

扫榜需要真实数据支撑。 根据当前环境选择数据来源:

优先级模式说明何时用
1脚本采集直接抓取平台页面/SSR 数据,产出结构化文件优先;起点默认不需要 Chrome
2用户提供用户粘贴榜单截图/文字/链接用户已有数据时
3内置知识基于知识库趋势数据做分析无法联网、用户无数据时
脚本采集模式
  1. 只读所选平台那一份:起点 / 番茄 / 七猫 / 晋江 / 刺猬猫。里面有榜单网址、命令、字段、默认组合、故障排查和该平台的分析维度;需要浏览器态的平台先 /browser-cdp 启动 Chrome。
  2. 每次扫榜新建输出目录 扫榜/{YYYYMMDD}/(同日再扫加 -2),存本次榜单,不往旧目录追加(聚合会混进旧榜);文件名 {平台}{榜单}_{YYYYMMDD}.md。脚本已做清洗和质量标注。
  3. 聚合:node scripts/aggregate-rank.js {输出目录} --out {输出目录}/扫榜聚合.md(小平台加 --sparse 10)。主会话只读这份聚合,不整份读原始榜单:题材分布、本数占比、热度与字数中位、标签热词、书名常见词、多榜重合、每题材前 3 本代表作。
  4. 看聚合的「采集情况」:标了问题(书名没解出来、没采到、只有书单)的榜单按平台参考排查重采;重采不了就在报告里说明结论不含它。同方向不到 15 本(小平台 10 本)标「少」,等同 [数据稀疏]。
  5. 要看原始条目(开篇卖点、新人设、新桥段)时按需抽样:node scripts/aggregate-rank.js {输出目录} --sample {题材/标签/书名词} --n 5,一次一个方向。
其他数据来源

用户提供: 已有扫描结果文件 → 直接聚合;链接 → 用 WebFetch 抓取;粘贴/截图 → 整理成 # {平台} · {榜单名} 标题、每本一段 ### #{排名} {书名} 加一行 *作者 · 题材 · 连载中 · 120万字 · 3.5万收藏*(热度写「数字+口径」;有标签再加 **标签:** a、b)存进输出目录,再同样聚合。

内置知识: 加载 references/genre-trends.md,明确标注「以下分析基于历史趋势数据;未完成实时榜单校验前只能作为候选假设。」并列出需要复扫的榜单。


Phase 3:数据分析

以聚合结果为主,按所选平台参考里的「分析维度」看,再提取通用维度:

  1. 题材分布:题材表的本数、占比与热度中位
  2. 新题材信号:新书榜列、多榜重合;扫榜/ 下上一次的日期目录里有 扫榜聚合.md 就对比
  3. 经典题材变化:老牌题材的走势(上升/稳定/下降),没有上期数据就只写现状
  4. 字数:字数分布与各题材字数中位
  5. 书名模式:书名常见词 + 代表作书名
  6. 开头卖点:标签热词 + 代表作简介
  7. 新元素:对候选方向抽样原始条目,标出新的人物设定、开篇切入点、桥段套路

Phase 4:输出扫榜报告

报告写给作者:讲市场结论和能写的方向。脚本名、命令和采集状态码不进报告;某个榜没采到,就说一句「XX 榜这次没拿到(原因),结论不含它」。

报告展示给作者,同时写进输出目录的 选题决策.md:文件不存在就先按 references/topic-decision.md 的模板写文件头,再把报告作为「扫榜结论」一节写入(已存在同名一节就整节替换)。作者只扫榜不选题,结论也已落盘。

<!-- author-report -->
md
## 扫榜结论:{平台名称}

### 市场概况
- 扫榜时间:{日期}
- 核心发现:{一句话总结}

### 题材热度排行
| 排名 | 题材 | 榜上数量 | 趋势 | 代表作 |
|------|------|----------|------|--------|
| 1 | {题材} | {N本} | ↑/→/↓ | {书名} |

### 新题材信号
- {新出现或正在上升的题材,附依据}

### 经典题材动态
- {老牌题材的现状,附依据}

### 新元素提取
- 新人物设定:{新模式描述 + 代表作}
- 新开篇切入点:{新切入点描述 + 代表作}
- 新桥段/套路:{新桥段描述 + 代表作}

### 关键数据洞察
- 字数区间:上榜作品集中在 {X}-{Y} 万字
- 书名特征:{命名模式总结}
- 标签热词:{高频标签词}

### 值得关注的方向
1. {方向 + 为什么值得关注 + 可行性评估}
2. {方向 + 为什么值得关注 + 可行性评估}
3. {方向 + 为什么值得关注 + 可行性评估}

### 一句话
{犀利的总结}

Phase 5:选题决策

把扫榜结果变成能直接用的选题建议,产出 选题决策.md。完整方法(选题四步 + 可行性判断 + 输出模板)见 references/topic-decision.md。

从文件接上: 先读 {outdir}/选题决策.md 的「扫榜结论」和 扫榜聚合.md,不靠对话记忆;两者都没有就回 Phase 2。

如信息不足,向用户补齐项目条件:「目标平台、已有素材、擅长题材/写作约束、计划篇幅是什么?」

按 topic-decision.md 的选题四步产出 2-3 个推荐选题(能爆的原因 → 市场验证 → 差异化定位 → 可行性+失败风险+验证动作),写入本次扫榜输出目录 {outdir}/选题决策.md 的「推荐选题」一节,保留「扫榜结论」。

硬规则:

  • 可行性上限:背靠榜单标了 [数据稀疏]、聚合表标「少」或同方向样本 <15(小平台<10)⇒ 不许给"高",强制降到"中" + 写明先验证;内置知识模式一律给"中"。
  • 不输出项目素材无法支撑的题材;不只看热度,必须给可行性和失败风险;不忽略平台调性差异(起点男频和晋江女频审美完全不同)。

平台特性速查

平台调性核心指标主力读者适合类型
起点中文网男频为主,硬核爽文追读率、月票18-35 男性玄幻、都市、科幻、游戏
番茄小说下沉市场,免费阅读在读数、阅读榜排名大众读者脑洞、快节奏、强爽感
晋江文学城女频为主,精品路线收藏、营养液、积分16-30 女性言情、纯爱、衍生
七猫小说下沉市场,免费阅读热度、大热榜排名大众读者快节奏爽文
刺猬猫二次元、轻小说追读15-25 ACG同人、二次元、轻小说

流程衔接

流水线: 长篇 位置: 扫榜(第 1/3 步)

时机跳转到命令
找到方向story-long-analyze/story-long-analyze
直接开写story-long-write/story-long-write
更适合短篇story-short-scan/story-short-scan

参考资料

按需加载以下文件:

文件何时加载
references/topic-decision.md「选题决策」:选题四步 + 可行性判断 + 选题决策.md 模板
references/reader-profiling.md需要分析目标读者画像时
references/genre-trends.md查看题材趋势候选、切入约束和样本校验规则时
references/publishing-guide.md平台适配+推荐机制校验+数据指标+简介设计
五份平台参考(链接见「脚本采集模式」第 1 步)「确定数据来源」:只读所选平台那份——榜单网址、命令、字段、故障排查、平台分析维度
scripts/aggregate-rank.js把输出目录里的原始榜单聚合成短表(--out 落盘,--sample 抽原始条目,--json 机读),主会话只读它
scripts/cdp-utils.js 与各平台采集脚本CDP 公共工具与各平台采集脚本({平台}-rank-scraper.js),命令见所选平台参考

语言

  • 跟随用户的语言回复,用户用什么语言就用什么语言回复
  • 中文回复遵循《中文文案排版指北》

© zenstory-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 16 other files (scripts, references) in skills/story-long-scan of zenstory-ai/oh-story-claudecode.

  • SKILL.md
  • references/genre-trends.md
  • references/platform-ciweimao.md
  • references/platform-fanqie.md
  • references/platform-jjwxc.md
  • references/platform-qidian.md
  • references/platform-qimao.md
  • references/publishing-guide.md
  • references/reader-profiling.md
  • references/topic-decision.md
  • scripts/aggregate-rank.js
  • scripts/cdp-utils.js
  • scripts/ciweimao-rank-scraper.js
  • scripts/fanqie-rank-scraper.js
  • scripts/jjwxc-rank-scraper.js
  • scripts/qidian-rank-scraper.js
  • scripts/qimao-rank-scraper.js

Open the folder on GitHubat commit 2cf7be6

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders. This page covers the copy in zenstory-ai/oh-story-claudecode, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Long-Form Fiction Market ResearchNarcooo/inkos10k—~289Automated safety check: PassAGPL-3.0
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Amazon Product Search Extractorbrowser-act/skills6.1k1 repos~1.5kAutomated safety check: PassMIT
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Questions about Web Novel Ranking Scanner

What does Web Novel Ranking Scanner do?

Analyzes ranking-list data from Chinese web novel platforms to spot repeating genres, title patterns and opening hooks, then writes a market report for authors. md is written in Chinese. The agent acts as a market analyst for long-form web novels.

When should I use Web Novel Ranking Scanner?

Web Novel Ranking Scanner fits situations like: finding which web novel genres are trending on a given platform; comparing ranking patterns across Qidian, Fanqie and Jinjiang; choosing a genre for a new long-form novel from ranking evidence.

How do I install Web Novel Ranking Scanner in Claude Code?

Run `npx skills add zenstory-ai/oh-story-claudecode --skill story-long-scan -a claude-code`. Or copy the skill folder (skills/story-long-scan in zenstory-ai/oh-story-claudecode) into .claude/skills/story-long-scan in your project. Claude Code loads it when a task matches its description.

How do I install Web Novel Ranking Scanner in Codex?

Run `npx skills add zenstory-ai/oh-story-claudecode --skill story-long-scan -a codex`. Or copy the skill folder (skills/story-long-scan in zenstory-ai/oh-story-claudecode) into .agents/skills/story-long-scan in your project. Codex loads it when a task matches its description.

Can I use Web Novel Ranking Scanner in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zenstory-ai/oh-story-claudecode --skill story-long-scan -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/story-long-scan, .gemini/skills/story-long-scan, .github/skills/story-long-scan and .opencode/skills/story-long-scan in your project.

What does Web Novel Ranking Scanner need to run?

Going by SKILL.md and its folder, Web Novel Ranking Scanner needs JavaScript for the scripts in its folder and the command-line tools its instructions call (node). Our summary lists: Node.js to run the scraper and aggregation scripts; Chrome started through /browser-cdp for platforms that need a browser session; Network access to the ranking pages.

Does Web Novel Ranking Scanner access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Web Novel Ranking Scanner safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Web Novel Ranking Scanner use?

Web Novel Ranking Scanner is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Web Novel Ranking Scanner use?

About 1.3k tokens (SKILL.md is roughly 5.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 8.3k tokens, read only when the agent opens those files.

What are the alternatives to Web Novel Ranking Scanner?

Skills that share tags, products or a category with Web Novel Ranking Scanner: Web Novel Ranking Scanner (uu201/character-arc, 583 stars), Long-Form Fiction Market Research (Narcooo/inkos, 10k stars), Amazon Best Sellers Finder (browser-act/skills, 6.1k stars) and Amazon Product Search Extractor (browser-act/skills, 6.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Web Novel Ranking Scanner?

zenstory-ai (a GitHub organization) maintains it in zenstory-ai/oh-story-claudecode, which has 7,424 GitHub stars. The repository holds 13 skills in this directory. The repository was last updated on October 10, 2026.

Source: zenstory-ai/oh-story-claudecode on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.