This analyzes web fiction rankings from Chinese platforms like Qidian, Fanqie, and Jinjiang to spot market trends and hot genres. It either scrapes live data using browser-cdp, takes user-provided screenshots, or falls back to historical knowledge. The output is structured reports showing genre distribution, word count patterns, title conventions, and emerging tropes with actual book examples. It's designed for authors trying to figure out what's working right now before committing to a long-form project. The coolest part is how it distinguishes between platforms: Qidian optimizes for subscription reads, Fanqie for completion rates, Jinjiang for collections and reader donations. Each needs different success metrics.

skills.sh installs
11.9K
GitHub stars
5.1K
last updated
Aug 21, 2026

Install

$ npx -y skills add worldwonderer/oh-story-claudecode --skill story-long-scan --agent claude-code

Installs into .claude/skills of the current project.

Files

story-long-scan:长篇网文扫榜

你负责从长篇榜单的重复样本中识别题材候选,结合作者条件给出可行性、失败风险与验证动作。单本排名只提供线索,近期多榜单、多作品的重复模式才支持趋势判断;各平台热度只按自身公开口径比(番茄看在读,起点看付费榜与总推荐,晋江看收藏、营养液与积分,七猫看热度),完读率、追读、订阅转化等作者后台数据未提供时不下结论。


扫榜流程

Phase 1:确认平台和方向

沿用作者已给的条件;缺的一次问齐,给选项:

  • 平台:起点 / 番茄 / 晋江 / 七猫 / 刺猬猫 / 多个平台对比
  • 频道(番茄、七猫才分):男频 / 女频 / 都看
  • 看什么:新书里在冒头的(多采新书样本) / 成熟大盘(只采默认榜单)
  • 有没有关注的题材方向(没有就做全榜概览)

Phase 2:确定数据来源

优先级模式说明何时用
1脚本采集直接抓取平台页面数据,产出结构化榜单文件优先;起点排行榜不需要 Chrome
2作者提供作者粘贴榜单文字、截图,或给已有的榜单文件作者已有数据,或脚本采不到时
3内置知识只用历史题材候选做分析无法联网、作者也没有数据时
脚本采集模式

命令里的 scripts/ 相对本 skill 根目录。

Sponsors

Details

Repository
github.com/worldwonderer/oh-story-claudecode
First seen
May 16, 2026

More from worldwonderer/oh-story-claudecode

All 13

Recommended

1juliusbrussee avatarcavemanjuliusbrussee/cavemanUltra-compressed communication mode. Cuts output tokens 65% (measured) by speaking like caveman while keeping full technical accuracy. Supports intensity levels618.3Kskills.sh installs
2mattpocock avatargrill-memattpocock/skillsA relentless interview to sharpen a plan or design.1.5Mskills.sh installs
3shadcn avatarimproveshadcn/improveSurvey any codebase as a senior advisor and produce prioritized, self-contained implementation plans for OTHER models/agents to execute. Strictly read-only on s40Kskills.sh installs
4obra avatarsystematic-debuggingobra/superpowersUse when encountering any bug, test failure, or unexpected behavior, before proposing fixes326.7Kskills.sh installs
5forrestchang avatarkarpathy-guidelinesforrestchang/andrej-karpathy-skillsBehavioral guidelines to reduce common LLM coding mistakes through explicit assumptions, simplicity, and verifiable success criteria.17.9Kskills.sh installs
6vercel-labs avatarfind-skillsvercel-labs/skillsHelps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express int4.3Mskills.sh installs