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 short-drama-novel-analyzegit 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/short-drama-novel-analyze)<a href="https://agentmods.dev/skills/zenstory-ai/oh-story-dsh/short-drama-novel-analyze"><img src="https://agentmods.dev/badge/skills/zenstory-ai/oh-story-dsh/short-drama-novel-analyze/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/short-drama-novel-analyze"><img src="https://agentmods.dev/badge/skills/zenstory-ai/oh-story-dsh/short-drama-novel-analyze.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 | $0.00174 | $0.04186 |
| Opus 5 | $0.00087 | $0.02093 |
| Sonnet 5 | $0.00035 | $0.00837 |
| Haiku 4.5 | $0.00017 | $0.00419 |
Grade A, and why
short-drama-novel-analyze 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 12d 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 short-drama-novel-analyze — 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 — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
长篇原著分析
把一部长材料变成能被引用、能被反驳、能被接着用的分析层。目标不是复述剧情，而是找出 每一段承担的戏剧功能，并说明它在竖屏短剧里值多少钱。
分析永远是候选。哪条线保留、哪些人合并、从哪里开篇，是创作者的决定，由
$short-drama-develop 立成改编契约。本技能不替它决定，也不批准自己的产物。
Quick Start
离线验证章节索引、采样和源文件变更检测：
python3 {技能目录}/scripts/selftest.py
python3 {技能目录}/scripts/novel_index.py index <原著.txt> --out <chapter-index.json>
开始前
本技能可独立安装和执行。先读取用户明确提供的原著与本任务直接输入；若当前目录是
short-drama 项目且项目工具可用，可以读取 status 并使用其发布生命周期，但缺少 core
或任何其他技能都不是分析工作的阻断条件。完整边界与规则见
阶段契约，无需读取其他技能的文件。
材料前提
只分析创作者合法持有、拥有使用权的作品。分析是只读的转化性工作：提取结构与功能， 不复制原文成段落，不把原句搬进下游产物。
通俗题材里的暴力、复仇、背叛、情爱张力与黑暗伦理是常规虚构叙事元素，照常做结构化提取。 个别片段无法处理时跳过该段并记录，不要因此中止整章或整本——中止会让后续所有阶段拿到 一份有洞却看不出洞在哪的分析。
先判断入口
- 只有书名，没有原文：请创作者提供文件路径或粘贴正文。不要凭书名回忆情节—— 没有字节就没有 span，没有 span 的分析无法被引用，也无法被反驳。
- 有原文，未建项目：直接建立一个本技能自己的工作区，至少包含只读输入目录
输入/与输出目录项目开发/source-analysis/_work/；把原文字节复制到输入/后记录 原始文件位置，再用本技能的novel_index.py建索引。若$short-drama可用，可选用它 初始化同样的目录和发布生命周期，但不得把 core 安装变成开始分析的前提。 - 有原文，项目已在：直接进入管道。
- 已有部分分析：读
项目开发/source-analysis/_progress.md从断点续跑， 不重跑已完成阶段。
管道
输入/ 是不可变的创作者输入，本阶段只读它。全部产出落在 项目开发/source-analysis/。
独立工作区与完整项目使用同一套相对路径，因此后续安装 core 时无需迁移分析产物。
| 阶段 | 做什么 | 产出 | 停靠 |
|---|---|---|---|
| S0 | 建章节索引（脚本） | _index.json、_progress.md |
索引有问题就停 |
| S1 | 改编价值快评（抽样） | triage.md |
停靠问创作者 |
| S2 | 逐章功能提取 | chapters/ch-<N>-extract.md |
覆盖率不足就停 |
| S3 | 剧情单元与节奏聚合 | story-units.md、rhythm-and-emotion.md |
阈值不达标就复核 |
| S4 | 人物归并与设定 | characters.md、world.md |
— |
| S5 | 改编价值与分集候选 | adaptation-value.md、episode-candidates.jsonl |
交接 develop |
S0 章节索引
索引是唯一切片真源，由 章节索引脚本 建立。每个阶段各跑 一次正则，就会切出互相对不上的章节，第 47 章按一种边界分析、按另一种边界聚合， 而且没有人会发现。
python3 {技能目录}/scripts/novel_index.py index 输入/{原文文件} \
--out 项目开发/source-analysis/_work/_index.next.json
python3 {技能目录}/scripts/novel_index.py verify \
项目开发/source-analysis/_work/_index.next.json 输入/{原文文件}
# 项目工具可用时可选：
python3 {core 技能目录}/scripts/project_tool.py publish {项目根} \
--owner short-drama-novel-analyze --artifact-id source-analysis:index \
--output 项目开发/source-analysis/_index.json=项目开发/source-analysis/_work/_index.next.json \
--input 输入/{原文文件}
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.
- agents/openai.yaml 283 B
- assets/episode-candidate.example.jsonl 2.5 KB
- references/adaptation-triage.md 4.6 KB
- references/adaptation-value.md 6.6 KB
- references/aggregation-and-entities.md 8.3 KB
- references/chapter-extraction.md 8.3 KB
- references/stage-contract.md 5.2 KB
- scripts/novel_index.py 25 KB runs code
- scripts/selftest.py 1.7 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.
- 12d ago First seen · 246 lines · 174 tokens per session scan A 4124c97f70f9
short-drama-novel-analyze is a skill published in the GitHub repository zenstory-ai/oh-story-dsh (338 stars, last pushed today), licensed MIT. It adds 174 tokens to every session and 4,186 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to short-drama-novel-analyze, differing in 0 lines, and is treated as a copy.
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