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 Anarcadia/chuanban-chaishu-skills --skill chai-fen-sao-miaogit clone --depth 1 https://github.com/Anarcadia/chuanban-chaishu-skillsWrote 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/anarcadia/chuanban-chaishu-skills/chai-fen-sao-miao)<a href="https://agentmods.dev/skills/anarcadia/chuanban-chaishu-skills/chai-fen-sao-miao"><img src="https://agentmods.dev/badge/skills/anarcadia/chuanban-chaishu-skills/chai-fen-sao-miao/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/anarcadia/chuanban-chaishu-skills/chai-fen-sao-miao"><img src="https://agentmods.dev/badge/skills/anarcadia/chuanban-chaishu-skills/chai-fen-sao-miao.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 | $0.00052 | $0.00686 |
| Opus 5 | $0.00026 | $0.00343 |
| Sonnet 5 | $0.00010 | $0.00137 |
| Haiku 4.5 | $0.00005 | $0.00069 |
Grade A, and why
chai-fen-sao-miao 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.
What it actually says
拆分扫描
小说拆分扫描工具。对给定的小说文件进行结构扫描和章节拆分。
使用方式
用户提供小说文件路径，执行以下流程：
- 扫描阶段：调用 Python 脚本扫描小说结构
- 报告阶段：展示扫描结果，询问用户拆分粒度
- 拆分阶段：按用户指定的层级执行拆分
- 完成报告：展示拆分结果
执行步骤
步骤1：扫描结构
执行命令：
python3 {WORKSPACE}/novel_splitter.py --scan "文件路径"
步骤2：展示报告并询问用户
根据扫描结果，向用户展示检测到的章节层级，询问：
- 希望按哪个层级拆分？（卷/章/节/话等）
步骤3：执行拆分
执行命令：
python3 {WORKSPACE}/novel_splitter.py --split [层级] "文件路径"
步骤4：报告完成
告知用户：
- 输出目录位置
- 生成的文件数量
- 是否有异常情况
输入参数
用户输入 - 小说文件路径（支持 txt、epub、md 格式）
输出目录结构
Factory/拆书/{书名}/
├── {书名}.txt # 源文件（拆分后自动移入）
├── 拆分/
│ ├── 001_第一章_xxx.md
│ ├── 002_第二章_xxx.md
│ └── ...
├── 概括/ # 后续概括生成
├── 人物和设定/ # 后续分析生成
└── 全书概括.md # 后续概括生成
注意事项
- 如果未检测到标准章节结构，提示用户可能需要手动指定或检查文件
- 无法识别的内容会合并到前一个已识别章节
- 支持的拆分层级：卷、部、篇、章、集、回、话、节
- 项目目录命名：统一使用
{书名}/格式（不带书名号），与源文件名保持一致 - 源文件处理：拆分完成后，源文件自动移动到项目目录下，保持与目录同名
- 防嵌套机制：
- 脚本会自动检测并避免创建嵌套目录结构
- 如果检测到已存在的嵌套目录（如
书名/《书名》/），会自动合并到父目录 - 支持检测和清理带书名号的嵌套目录（《》、「」等）
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 · 78 lines · 52 tokens per session scan A e3ced739ac62
chai-fen-sao-miao is a skill published in the GitHub repository Anarcadia/chuanban-chaishu-skills (44 stars, last pushed 5mo ago), licensed MIT. It adds 52 tokens to every session and 686 once invoked, about $0.0003 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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