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 TashanGKD/tashan-cursor-skills --skill article-proofreadinggit clone --depth 1 https://github.com/TashanGKD/tashan-cursor-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/tashangkd/tashan-cursor-skills/article-proofreading)<a href="https://agentmods.dev/skills/tashangkd/tashan-cursor-skills/article-proofreading"><img src="https://agentmods.dev/badge/skills/tashangkd/tashan-cursor-skills/article-proofreading/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/tashangkd/tashan-cursor-skills/article-proofreading"><img src="https://agentmods.dev/badge/skills/tashangkd/tashan-cursor-skills/article-proofreading.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 | $0.03951 |
| Opus 5 | $0.00040 | $0.01975 |
| Sonnet 5 | $0.00016 | $0.00790 |
| Haiku 4.5 | $0.00008 | $0.00395 |
Grade A, and why
article-proofreading 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 10d 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
86% identical to smart-search — 393 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 — 344 lines — stays where its author put it; the contents beside it link to each section on GitHub.
文章审稿 Skill
基于郑总历次亲手修改、明确批注的审稿标准。五轮检查，每轮独立列问题清单，最后给出优先级。
参考文档：_内部总控/AI思维碎片/写作习惯与风格手册.md
知识导航表（执行前必须理解的概念根）
| 层级 | 文档 | 需要理解的概念 |
|---|---|---|
| D0 认知根（必读） | _内部总控/AI思维碎片/写作习惯与风格手册.md |
郑总审稿标准的完整来源：AI腔定义/标题4种错误/绝对表达清单/结语要求（本 Skill 的知识根） |
| D3 规范参考 | — | 本 Skill 本身就是规范定义，无需外部规范参考 |
| D4 运行时数据 | 目标文章草稿（用户提供） | 被审稿的文章（审查对象） |
核心概念速查： ① AI腔 = 「此外」「总之」「值得注意的是」「不仅如此」「综上所述」等AI写作套话 ② 标题4种错误 = 过度承诺/空泛标题/AI腔标题/不够具体 ③ 审稿顺序不变：AI腔→标题→绝对表达→结构层次→结语，五轮独立，不合并
第一轮：AI腔检测
1.1 翻译感词汇
逐句扫描，标记所有让人感觉是从英文直译的词：
| 常见AI腔词 | 改法 |
|---|---|
| 人层 | 人类层 |
| 工具层（指代"人"所在的层次时） | 视语境调整 |
| 首先/其次/最后 作为枚举句式 | 直接说内容，不用序数铺垫 |
判断原则：读出声来感觉别扭、或是翻译腔的，就标记。
1.2 元评论过渡句（零信息量的宣告）
❌ 直接删除下列句式，不要任何替换，直接说内容：
- "核心推论只有一句话："
- "这张表说明一件事："
- "有以下几点值得关注："
- "这里有X条标准……"
- "值得注意的是，"
- "总的来说，"（在段落中间出现时）
- "不难发现，"
✅ 替换方式：删掉这句话，下一句直接用"所以，""因此，"开头，或无连接词直接开始。
为什么是AI腔：这类句子是在"宣告接下来要说什么"，本身没有任何信息量。真正有力的表达直接说出内容，不需要预热。
1.3 防守性修饰（替读者预设反驳）
❌ 检查并删除以下模式：
- "这不是X，而是Y"——当Y是在为自己的判断辩护时
- "这不是未来的预测，而是正在发生的结果"
- "很多人可能会认为……但实际上……"（不必要的假想敌）
- "这一点很重要"（让读者评判，不要自己标注）
- "不可否认……但……"
✅ 正确做法：直接陈述结论，不加防守。读者自会判断。
例外："不是X，而是Y" 用于积极的对比陈述（如"不是界面做简洁，而是设计路径的根本颠倒"）可以保留——区分在于它是断言，不是辩护。
1.4 "这里有" 套话
❌ 删除：
- "这里有一条核心原则……"
- "这里有五条判断标准……"
- "接下来我们来看……"
✅ 直接进入实质内容。
第二轮：标题质量（四种错误类型）
对每个章节/小节标题，依次用四个问题判断：
2.1 是结论，还是话题/方法描述？
检测：标题能否独立表达一个观点？还是只在描述"这节讲什么"？
❌ 错误例：产品机会的来源：三类永恒坐标 × AI 带来的成本变化
→ 说的是"我用什么框架分析"，不是"分析得出什么"
✅ 正确例：旧瓶颈正在消失，把它们做好是把错的做对
→ 表达一个判断，读者读到标题就知道该相信什么
识别错误标题的特征：
- "X 的来源：A × B"（描述分析框架）
- "X 的结构：A、B、C"（描述内容组成）
- "X 的维度/原则/方法"（描述分类方式）
2.2 是机制，还是本质/原则？
检测：标题里有"设计/使用/实现/建立/优化"等动词 → 往往是机制描述，不是结论。
❌ 错误例：为人与智能体同时设计两层界面
→ 说的是"如何做"（机制）
✅ 正确例：产品设计的第一性原理：Human-Readable + Agent-Operable
→ 说的是"这个机制揭示了什么设计原则"（本质）
改法：把"如何做"升级为"这样做意味着什么、揭示了什么"。
2.3 是定性，还是从定性推出的原则？
检测：标题只是给某个概念贴了一个标签（定性），而没有给出从这个定性推导出的行动原则。
❌ 错误例：产品的本质变了：人与其数字员工协作的介质
→ "介质"是定性（说"是什么"），信息量低
✅ 正确例：产品设计的第一性原理：Human-Readable + Agent-Operable
→ 从"介质"这个定性推出的设计原则，信息量更高
What ships with it
3 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.
- 10d ago First seen · 344 lines · 80 tokens per session scan A 1a1689e6a3a5
article-proofreading is a skill published in the GitHub repository TashanGKD/tashan-cursor-skills (20 stars, last pushed 5mo ago), licensed MIT. It adds 80 tokens to every session and 3,951 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to smart-search, differing in 393 lines, and is treated as a copy.
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