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 reviewgit 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/review)<a href="https://agentmods.dev/skills/tance-mang/chinese-webnovel-skills/review"><img src="https://agentmods.dev/badge/skills/tance-mang/chinese-webnovel-skills/review/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/review"><img src="https://agentmods.dev/badge/skills/tance-mang/chinese-webnovel-skills/review.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 | $0.00080 | $0.01153 |
| Opus 5 | $0.00040 | $0.00576 |
| Sonnet 5 | $0.00016 | $0.00231 |
| Haiku 4.5 | $0.00008 | $0.00115 |
Grade A, and why
review 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
审稿与追读诊断（review）
不只是挑错，而是从读者留存视角诊断稿子，给出能直接改的建议。这是本插件区别于通用写作工具的核心。
何时用
- 审一章 / 一卷 / 全书
- 查剧情漏洞、伏笔断点、人设 OOC
- 诊断追读率/完读率为什么掉
- 发布前体检
六维诊断
1. 钩子诊断（留人）
- 开篇 3 句有没有立住危机/反差？
- 每章是否有有效章尾钩？标出"平淡收尾"的章（调
references/hook-library.md） - 付费节点前是否压了强钩？
2. 爽点诊断（追读）
- 爽点密度是否匹配平台？（番茄每章、起点每 3 章，调
references/platform-profiles.md） - 标出"爽点断档"的连续章节
- 爽点是否原地重复没升级？（调
references/trope-library.md升级链） - 打脸四拍是否完整（尤其"围观"拍有没有漏）
3. 伏笔诊断（防烂尾）
- 列出所有已埋伏笔 → 标记：已回收 / 待回收 / 疑似遗忘
- 标出"挖了没填的坑"和"突然冒出没铺垫的设定"
4. 人设诊断（一致性）
- 人物言行是否前后一致、有没有 OOC
- 主角是否有主观能动性（还是被金手指牵着走）
- 反派强度是否撑得起主角的胜利
5. 节奏诊断（呼吸感）
- 是否一直紧绷或一直平淡？
- 战斗/打脸后有没有舒缓，舒缓后有没有再拉紧
6. 人味/失控感诊断（像不像真人写的）
- 情绪是不是太"平均"（都合理反应，没人真的失控/偏执/在痛）？
- 冲突是不是太"干净"（误会→解释→解决，缺"越滚越脏"）？
- 句子是不是太"顺"（无长短变化、无停顿、无不完整表达）？
- 人物是不是太"正常"（没有"明知错还做"的不理性/偏执/赌）？
- 是不是太"中立"（没有偏见、私货感）？
- 一句话标准：读起来像说明书还是情绪记录？太像说明书 → 用
human技能加失控感（调references/human-texture.md）。 - 想要量化到"AI 味指数 X/10 + 扣分在哪几句" → 用
aidetect（比喻密度/连续同句式/句长波动/段落分布/情绪词重复，见references/ai-detector.md）。
输出：诊断报告
【整体评分】留人 / 追读 / 连贯 各打分 + 一句话结论
【继续率预测】前三章：冲突/悬念/人物记忆点/反差 → 读者会不会追下去（高/中/低 + 为什么）
【致命问题】最影响数据的 1–3 点（按优先级）
【钩子】哪几章收尾平淡 → 怎么改
【爽点】哪段断档 / 没升级 → 怎么补
【伏笔】待回收清单 + 疑似遗忘的坑
【人设】OOC 或强度问题
【节奏】哪里需要松/紧
【修改优先级】先改哪个见效最快
原则
- 诊断要具体到章/段，不说空话
- 每个问题都给可执行的改法，不只是指出问题
- 优先级排序：先改最影响读者留存的
下一步
按报告 → 用 expand 重写问题章、shuangdian 补爽点、hook 修钩子。
本技能是读者留存视角（钩子/爽点/追读）。要作者后台视角深查设定不矛盾（行为/因果/资源/身份/伤势/战力/时间线）→ 用
continuity；要 AI 味量化评分 → 用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.
- 12d ago First seen · 72 lines · 80 tokens per session scan A ba2497a08b10
review is a skill published in the GitHub repository tance-mang/chinese-webnovel-skills (58 stars, last pushed 3mo ago), licensed MIT. It adds 80 tokens to every session and 1,153 once invoked, about $0.0004 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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