oh-story-claudecode is a skill package for planning, analyzing, and writing Chinese web fiction, covering both short and long novels. It is intended for writers using Claude Code, Codex, OpenClaw, and other supported AI-agent environments, with workflows for market research, story breakdowns, drafting, editing, and cover creation. The catalogue entries are the package's skills, commands, agents, and plugin.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add zenstory-ai/oh-story-claudecode --skill story-long-scangit clone --depth 1 https://github.com/zenstory-ai/oh-story-claudecodeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zenstory-ai/oh-story-claudecode/story-long-scan)<a href="https://agentmods.dev/skills/zenstory-ai/oh-story-claudecode/story-long-scan"><img src="https://agentmods.dev/badge/skills/zenstory-ai/oh-story-claudecode/story-long-scan/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zenstory-ai/oh-story-claudecode/story-long-scan"><img src="https://agentmods.dev/badge/skills/zenstory-ai/oh-story-claudecode/story-long-scan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket warn
- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00073 | $0.02631 |
| Opus 5.5 | $0.00029 | $0.01052 |
| Sonnet 5.5 | $0.00015 | $0.00526 |
| Haiku 4.5 | $0.00007 | $0.00263 |
Grade A, and why
story-long-scan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
2 near-identical copies found in the catalogue:
- story-long-scan — 100% identical, 0 lines differ
- story-long-scan — 92% identical, 321 lines differ
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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。
核心信念：单本排名提供线索，近期多榜单、多作品的重复模式才支持趋势判断。
扫榜原则
原则 1：扫榜看模式，别只看排名
提取样本中重复的题材、设定、套路、书名词和简介卖点，判断能否支持趋势候选。
原则 2：各平台的热度口径不同
番茄看在读，起点看付费榜和总推荐，晋江看收藏、营养液和积分，七猫看热度。完读率、追读、订阅转化是作者后台的数据，采集脚本拿不到，作者没提供就不下结论。
原则 3：扫榜的目的是找到你能写的爆款题材
每个方向都结合素材储备、题材边界、计划篇幅和目标平台样本判断可行性，再给选题建议。
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。
- 每次扫榜在工作目录新建
扫榜/{YYYYMMDD}/（同日再扫加-2），下文{outdir}指它。不往旧目录追加，聚合会混进旧榜。 - 只读所选平台参考：起点 / 番茄 / 七猫 / 晋江 / 刺猬猫。按 Phase 1 的频道和「看什么」运行默认命令；需要 Chrome 的平台先
/browser-cdp启动。 - 聚合：
node scripts/aggregate-rank.js {outdir} --out {outdir}/扫榜聚合.md。主会话先只读聚合：题材分布、去重本数、热度与字数中位、标签、书名词、多榜重合和代表作。 - 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、详情没取到）的榜单，按平台参考的「采不到时」排查后重采；重采不了，报告里说明结论不含它。
- 分析新人物设定、新切入设定和新桥段时按需抽样：
node scripts/aggregate-rank.js {outdir} --sample {题材/标签/书名词} --n 5，一次一个方向。命中条数含多榜重复，样本本数按同平台书名+作者去重。
作者提供
已有榜单文件就放进 {outdir} 聚合。作者给链接时，请他把页面内容复制过来或截图（番茄、起点有反爬，直接抓页面常拿不到）。把文字或截图整理成：首行 # {平台} · {榜单名}，平台名只用 起点 / 番茄 / 晋江 / 七猫 / 刺猬猫，才能和脚本数据合并；每本一段 ### #{排名} {书名}，下一行 *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 {outdir} 后同样聚合。
内置知识
读 references/genre-trends.md，报告开头写明「以下分析基于历史题材候选，没有实时榜单校验，只能作为待验证的假设」，并列出需要复扫的榜单。
Phase 3：数据分析
以聚合结果为主，先按所选平台参考的「分析维度」看，再看通用维度：
- 题材分布：本数、占比与热度中位。番茄按题材等额采样（聚合会注明），本数是采样上限，题材冷热看热度中位和平台 P75。
- 新题材信号：新书榜、书库新书样本与多榜重合；同时采到新书榜和其他榜时，聚合才显示「新书榜」列。上次日期目录有
扫榜聚合.md时，按相同平台、榜单与筛选范围对比。 - 头部老书：月票、畅销、阅读榜上字数几百万、连载多年的书，代表这个题材的存量读者，不代表现在开新书能起来；判断新方向以新书榜、书库新书为主。
- 经典题材变化：老牌题材上升、稳定还是下降；没有上期数据就只写现状。
- 字数：字数分布与各题材字数中位。
- 书名模式：书名常见词 + 代表作书名。
- 简介卖点：标签热词 + 代表作简介。数据只有简介（聚合截到 30 字，采集截到 100 字），开篇怎么写留给拆文验证。
- 新元素：对候选方向抽样原始条目，标出新的人物设定、切入设定、桥段套路。
What ships with it
14 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/genre-trends.md 3.8 KB
- references/platform-ciweimao.md 1.7 KB
- references/platform-fanqie.md 2.8 KB
- references/platform-jjwxc.md 1.9 KB
- references/platform-qidian.md 3.2 KB
- references/platform-qimao.md 2.5 KB
- references/topic-decision.md 4.9 KB
- scripts/aggregate-rank.js 31 KB runs code
- scripts/cdp-utils.js 8.4 KB runs code
- scripts/ciweimao-rank-scraper.js 9.3 KB runs code
- scripts/fanqie-rank-scraper.js 26 KB runs code
- scripts/jjwxc-rank-scraper.js 16 KB runs code
- scripts/qidian-rank-scraper.js 48 KB runs code
- scripts/qimao-rank-scraper.js 35 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago Changed 9c30fbc326aa
- 10d ago Changed · -23 lines cd415ae7d5e3
- 12d ago Changed · -5 lines 5235b62812f5
- 17d ago Changed · -5 lines · +12 tokens per session ef2c3b3ae448
- 1mo ago Changed · -152 lines 88d46967faba
- 1mo ago Changed · +3 lines 5b56a6c0b330
- 1mo ago First seen · 343 lines · 61 tokens per session scan A aedbfc25b66d
story-long-scan is a skill published in the GitHub repository zenstory-ai/oh-story-claudecode (8,063 stars, last pushed yesterday), licensed MIT. It adds 73 tokens to every session and 2,631 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
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A Chinese fiction editing tool focused on making narrative prose sound like it was written by a person rather than generated by AI.
novel-review
A review coordinator for long novels. It breaks a manuscript review into focused checks by specialist roles, combines their reports, and stores the results in a review directory.
novel-sync
A Chinese-language workflow for updating a novel's continuity notes after a chapter is written or changed. It records events, character changes, unresolved clues, and notable lines in the project's reference files.
novel-plan
A planning workflow for designing a new long-form novel before writing it. It helps settle the premise, setting, character, progression, ending, outline, and writing approach, then tests the setting for logical problems.
bestseller-novel-outline
A planning workflow for designing a very long commercial novel before chapters are written. It creates the world, characters, overall plot, volume outlines, planted clues, and chapter paths for later writing tools.
novel-chapter-write
A long-form novel writing workflow that turns an approved chapter plan into chapter prose. It supports drafting, targeted revision, and final approval while keeping style and story details consistent across chapters.