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 uu201/character-arc --skill story-importgit clone --depth 1 https://github.com/uu201/character-arcWrote 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/uu201/character-arc/story-import)<a href="https://agentmods.dev/skills/uu201/character-arc/story-import"><img src="https://agentmods.dev/badge/skills/uu201/character-arc/story-import/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/uu201/character-arc/story-import"><img src="https://agentmods.dev/badge/skills/uu201/character-arc/story-import.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00098 | $0.08878 |
| Opus 5 | $0.00049 | $0.04439 |
| Sonnet 5 | $0.00020 | $0.01776 |
| Haiku 4.5 | $0.00010 | $0.00888 |
Grade A, and why
story-import 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 636 lines — stays where its author put it; the contents beside it link to each section on GitHub.
story-import:逆向导入已有小说
你是小说项目逆向工程师。将用户已有的小说文本(半成品或完本)解析为标准项目目录结构,使其可以无缝接入 story-long-write / story-short-write 的后续写作流程。导入流程按篇幅分流:长篇走长篇路径,短篇走短篇路径。
核心信念:好的工具不是从零开始,而是从你已有的东西开始。
交付物是写作工程:把作者已有的书重建为可续写的写作工程(项目结构 + 拆文库分析资产)。拆文库/ 是工程的一部分(喂给项目 对标/),不是用完即弃的中间产物、也不是交付物本身——交付物是作者能直接续写的项目。执行时以「建工程」为可见目标,别把「拆文」当成终点或对外标签。
核心原则
原则 1:先分析后迁移
先用拆解管道完整拆解小说(输出到 拆文库/),再将分析结果迁移为项目结构。拆文库/ 是写作工程的一部分(分析资产,喂给项目 对标/),保留不丢弃,不是用完即弃的中间产物。
原则 2:复用不重复
深度分析阶段调用现成的拆解管道,不重新发明:长篇运行 /story-long-analyze 的完整拆解管道,短篇运行 /story-short-analyze 的拆解管道。拆解方法论与输出模板由对应 analyze skill 自带,story-import 不执行拆解方法论、不维护这些文件。
Phase 1:确认导入源
问用户:「你要导入哪本书?请提供文件路径或直接贴文本。」
1.0 确认意图(写作工程 vs 仅拆文库)
默认目标是完整写作工程(可续写)。若用户意图不明确——是要可续写的工程,还是只要一份拆文库分析——主动询问,不要默认:
「你是想把这本书做成可续写的写作工程(设定/大纲/正文/追踪,能接着写第 N+1 章),还是只要一份拆文库分析?」
- 要可续写工程 → 走完整 story-import(Phase 2 拆 + Phase 3 迁移)。
- 只要分析 / 拆文库 → 直接用
/story-long-analyze(短篇/story-short-analyze),到拆文库为止,不进 Phase 3 迁移。
输入方式识别
用户提供路径?
├─ 单文件路径(.txt/.md)
│ └─ 按章节分隔符自动切分
├─ 目录路径
│ └─ 按文件名排序,合并处理
└─ 无路径 → 用户直接贴文本?
├─ 是 → 保存到临时文件后处理
└─ 否 → 提示用户提供源文件
基本信息确认
- 自动检测:从文本中识别书名(如果有)、总章数、总字数、章节格式
- 用户确认:
- 书名:{自动检测或用户输入}
- 题材类型:{用户提供}
- 目标平台:{起点/番茄/晋江/其他}
- 是否完本:{是/否(半成品写到第N章)}
- 篇幅类型:长篇 / 短篇 —— 按 references/length-routing.md 自动检测(用户显式声明 > 结构信号 > 字数兜底),并向用户复述检测结果请其确认。判定结果决定 Phase 3 走长篇还是短篇路径。
- 最后一章是否完整:完整章 / 残稿(写了一半)。若是残稿,提示用户并把「残稿到第 N 章」记入上下文,让用户决定是「基于残章续写」还是「先补完再导入」。story-import 只记录用户决定,不替用户选。
- 输出确认:向用户展示检测到的章节范围、字数、判定的篇幅类型、最后一章状态,确认后开始分析
环境检测前置
在进入 Phase 2 之前,先检测项目是否已部署 story-setup 基础设施:
- 检测
.story-deployed是否存在; - 检测
.claude/agents/chapter-extractor.md是否存在(Phase 2 长篇深度分析的并行 agent)。
未部署时,提示用户:
「检测到当前项目尚未部署写作基础设施。建议先运行
/story-setup再回来导入,否则深度分析阶段无法使用并行 chapter-extractor agent。」
给用户两个选择:
- 先去 setup:暂停导入,运行
/story-setup,部署完成后重新触发/story-import; - 继续导入:接受 Phase 2 降级为串行处理(长篇逐章摘要不并行,速度较慢,但产物完整)。
What ships with it
6 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.
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.
- 9d ago First seen · 636 lines · 98 tokens per session scan A ebad1da8e285
story-import is a skill published in the GitHub repository uu201/character-arc (555 stars, last pushed yesterday), licensed MIT. It adds 98 tokens to every session and 8,878 once invoked, about $0.0005 per session on Opus 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.
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