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-dsh --skill story-short-scangit clone --depth 1 https://github.com/zenstory-ai/oh-story-dshWrote 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-dsh/story-short-scan)<a href="https://agentmods.dev/skills/zenstory-ai/oh-story-dsh/story-short-scan"><img src="https://agentmods.dev/badge/skills/zenstory-ai/oh-story-dsh/story-short-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-dsh/story-short-scan"><img src="https://agentmods.dev/badge/skills/zenstory-ai/oh-story-dsh/story-short-scan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00065 | $0.02654 |
| Opus 5.5 | $0.00026 | $0.01062 |
| Sonnet 5.5 | $0.00013 | $0.00531 |
| Haiku 4.5 | $0.00006 | $0.00265 |
Grade A, and why
story-short-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 8d 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.
This is a copy
100% identical to story-short-scan — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
story-short-scan：短篇网文扫榜
基于真实榜单样本分析短篇市场，给作者可写的情绪方向、题材候选和验证办法。
核心哲学
原则 1：短篇市场是情绪市场
扫榜从题材和简介中判断读者读完的感受、触发场景与情绪爆发点，说明这些内容如何吸引读者。仅凭题材名无法判断具体情绪体验。
原则 2：热度只按各平台自己的口径比
榜单给的收藏、赞同、在读、评分各有口径，只在同一口径内比较。排名和互动量只说明样本受到的关注，不能据此断定故事质量或情绪拉扯效果。
原则 3：短篇风口来得快去得快
报告写明样本日期、覆盖范围、结论可信度和复扫日期。题材信号可能在数周内失效，过期后只作候选参考。
扫榜流程
Phase 1：确认平台和方向
用户已给出平台或方向时直接采用，只问缺项：「想看哪个平台、男频还是女频、哪类短篇？点众可直接采集；黑岩要有黑岩后台账号、先登录；知乎盐言、番茄和七猫短篇请发榜单截图、文字或链接。手里有编辑的征稿要求也可以一起发来。」
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
| 优先级 | 模式 | 说明 | 何时用 |
|---|---|---|---|
| 1 | 脚本采集 | 点众、黑岩：抓取平台页面，产出结构化文件 | 有 Chrome 环境时（优先） |
| 2 | 作者提供 | 没有脚本的平台，或作者已有榜单 | 知乎盐言/番茄短篇/七猫短篇等 |
| 3 | 内置知识 | 用历史资料提出待验证的选题 | 无法联网、作者也没有数据时 |
命令中的 scripts/ 相对本 skill 根目录。
- 只读所选平台那一份：点众 / 黑岩 / 没有脚本的平台。其中有网址、命令、字段、故障排查、平台分析维度和作者榜单的整理格式。
- 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）。脚本自动命名文件，作者榜单按平台参考命名；某平台失败就跳过。对比时读上个日期目录的扫榜聚合.md。 - 聚合：
node scripts/aggregate-rank.js {输出目录} --out {输出目录}/扫榜聚合.md --sparse 10 --scale short。主会话只读这份聚合，需要原始简介与人设信息时再抽样：node scripts/aggregate-rank.js {输出目录} --sample {题材/标签/书名词} --n 5。 - 看聚合的「采集情况」：标了问题（书名没解出来、没采到、详情没取到）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。
- 作者发来征稿要求：原文存进输出目录的
征稿要求.md，题材、字数、截止日期当硬条件。
内置知识： 跳过聚合，加载 references/real-market-data.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。」并列出需要复扫的平台页面。
Phase 3：数据分析
有真实样本时，以聚合结果和平台参考的「分析维度」为准。趋势需对比同口径的多期样本；仅用内置知识时只给待验证候选，不填写当前数量、占比或涨跌。
- 情绪排行：按本节末的情绪表把题材、标签归入六种写作情绪，按样本数量比较。每本书根据简介判断读者读完的感受，只归一类；标签只是线索，无法判断的计「待定」。悬疑题材按简介落点分：最后揭开真相的归反转震撼，留下寒意的归细思极恐
- 题材热点：聚合的题材表和标签热词里反复出现的设定、场景
- 篇幅分布：聚合的字数分布与各题材字数中位
- 简介与书名写法：抽样简介怎么前置冲突、留悬念，书名常见词与代表作标题；可对照 real-market-data 的历史写法
- 结尾类型：HE（好结局）/BE（坏结局）/开放式，只在作者提供的内容看得出结局时统计
- 人设模型：抽样简介里反复出现的主角类型
What ships with it
9 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/manual-rank-input.md 1.9 KB
- references/platform-dianzhong.md 1.2 KB
- references/platform-heiyan.md 1.7 KB
- references/real-market-data.md 3.6 KB
- references/topic-match.md 2.7 KB
- scripts/aggregate-rank.js 31 KB runs code
- scripts/cdp-utils.js 8.4 KB runs code
- scripts/dz-browse-scraper.js 8.4 KB runs code
- scripts/heiyan-booklist-scraper.js 13 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.
- 8d ago Changed · -10 lines 9b0e18ed6bf2
- 12d ago Changed · -7 lines 21868d466a09
- 29d ago Changed · -34 lines 0c8314c6c62b
- 1mo ago Changed · +4 lines 10a656d50c02
- 1mo ago First seen · 213 lines · 65 tokens per session scan A def39be0868b
story-short-scan is a skill published in the GitHub repository zenstory-ai/oh-story-dsh (629 stars, last pushed today), licensed MIT. It adds 65 tokens to every session and 2,654 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to story-short-scan, differing in 0 lines, and is treated as a copy.
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