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 tance-mang/chinese-webnovel-skills --skill annotategit clone --depth 1 https://github.com/tance-mang/chinese-webnovel-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/tance-mang/chinese-webnovel-skills/annotate)<a href="https://agentmods.dev/skills/tance-mang/chinese-webnovel-skills/annotate"><img src="https://agentmods.dev/badge/skills/tance-mang/chinese-webnovel-skills/annotate/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/tance-mang/chinese-webnovel-skills/annotate"><img src="https://agentmods.dev/badge/skills/tance-mang/chinese-webnovel-skills/annotate.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 | $0.00096 | $0.01003 |
| Opus 5 | $0.00048 | $0.00502 |
| Sonnet 5 | $0.00019 | $0.00201 |
| Haiku 4.5 | $0.00010 | $0.00100 |
Grade A, and why
annotate 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 11d 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
正文标注与节奏可视化（annotate）
把一章正文"透视"出来：钩子在哪、爽点几个、高潮在第几段、修饰词够不够。让作者一眼看见自己的节奏数据。
何时用
- 想看"这章爽点够不够、钩子在哪、节奏对不对"
- 标记/检查修饰词（短平快爽文的"味儿"词）
- 给一章做发布前的可视化体检
- 和
review的区别：review是整卷/整书的宏观五维诊断；annotate是单章逐段的微观标注
工作流
第 1 步：逐段标注（输出带标签的正文）
把用户的正文按段落标注，行内插入标签：
【钩】钩子（开篇钩/章尾钩/段落悬念）【爽】爽点（逆袭/打脸/碾压等，调references/trope-library.md）【高潮】本章情绪顶点【脸】打脸桥段（标出四拍：嘲讽/沉默/碾压/围观）【伏】伏笔埋点【修】修饰词（标出已用的强化词/围观反应词，调references/xiushi-ci.md）【缓】节奏舒缓段
第 2 步：输出节奏数据表
【本章体检表】
字数：____
钩子：开篇钩 ✓/✗ 章尾钩 ✓/✗ 段落悬念 __ 个
爽点：__ 个（类型：打脸/逆袭/…）｜密度评价：达标/偏少（按平台）
高潮：在第 __ 段｜情绪强度：__/5
打脸四拍：嘲讽__ 沉默__ 碾压__ 围观__（缺哪拍标出）
修饰词：强化词__ 围观反应__ ｜密度：合适/偏少/过载(油腻)
伏笔：埋__ 个 回收__ 个
节奏曲线：紧—缓—紧 是否有呼吸感
AI 味（粗判）：比喻密度/连续同句式/句长波动 有无异常 → 要量化评分用 `aidetect`
第 3 步：给改进建议
- 爽点断档的段 → 建议补什么（指到段）
- 打脸缺了"围观"拍 → 建议加围观反应词（给 2-3 个候选，调
xiushi-ci.md） - 修饰词偏少的爽点句 → 建议在哪句加什么词
- 修饰词过载的段 → 建议删到 2-3 个（避免油腻/AI 味）
- 章尾收尾平淡 → 建议换哪种章尾钩（调
hook-library.md）
原则
- 标注要落到段/句，不空泛。
- 修饰词建议遵循"稀疏使用、峰值集中、频道有别"（见
references/xiushi-ci.md）。 - 按平台判断密度：番茄要密，起点可疏（调
references/platform-profiles.md）。 - 男频重"猛/爽/燃"的强化词与围观反应，女频重神态/情绪词。
下一步
按标注 → 用 expand 重写弱段、shuangdian 补打脸、deslop 清理过载修饰词；要 AI 味量化评分+定位 → aidetect。
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.
- 11d ago First seen · 57 lines · 96 tokens per session scan A c0ab7f6fb60c
annotate is a skill published in the GitHub repository tance-mang/chinese-webnovel-skills (55 stars, last pushed 3mo ago), licensed MIT. It adds 96 tokens to every session and 1,003 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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