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 LeonChaoX/qinyan-academic-skills --skill qinyan-paper-polishgit clone --depth 1 https://github.com/LeonChaoX/qinyan-academic-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/leonchaox/qinyan-academic-skills/qinyan-paper-polish)<a href="https://agentmods.dev/skills/leonchaox/qinyan-academic-skills/qinyan-paper-polish"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/qinyan-paper-polish/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/leonchaox/qinyan-academic-skills/qinyan-paper-polish"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/qinyan-paper-polish.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 | $0.00145 | $0.01625 |
| Opus 5 | $0.00072 | $0.00813 |
| Sonnet 5 | $0.00029 | $0.00325 |
| Haiku 4.5 | $0.00015 | $0.00162 |
Grade A, and why
qinyan-paper-polish 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
沁言学术 - 论文润色优化
概述
本技能按照科学专业的学术规范，对中文论文、英文论文、专业报告等学术文本进行润色优化。提升语言表达质量、逻辑连贯性和学术规范性，同时保持作者的原始观点和研究内容不变。
技能路由： 本技能处理通用语言润色。用户明确指定 Nature 系列期刊，或需要章节级逻辑重构、证据边界校准、去模板化 AI 文风和投稿级忠实度核验时，使用 qinyan-nature-polishing。
学术诚信声明：
- 润色仅限于语言表达层面的优化，不改变原文的核心观点、数据和结论
- 不会添加原文中不存在的研究内容或数据
- 不会替代作者的原创性工作
- 润色结果应由作者本人审核确认
润色原则
核心原则
- 忠实原意：准确保留作者的原始观点、逻辑和论述
- 学术规范：符合学术写作的通用规范和领域惯例
- 清晰精炼：消除冗余表达，提升行文简洁度
- 逻辑连贯：确保段落之间、句子之间的逻辑衔接流畅
- 术语准确：使用准确的专业术语，避免口语化表达
不做的事
- 不改变研究结论或夸大研究成果
- 不添加未经证实的论述
- 不删除关键信息或数据
- 不改变文章的整体结构（除非用户明确要求）
润色维度
1. 语言表达优化
- 消除语法错误和拼写错误
- 修正标点符号使用不当
- 改善句式结构，避免过长或过短的句子
- 消除重复表述和冗余词语
- 统一人称和时态使用
2. 学术用语规范
- 将口语化表达替换为学术用语
- 使用领域标准术语，保持术语一致性
- 适当使用学术衔接词和过渡短语
- 避免绝对化表述（如"证明了"改为"表明"/"揭示了"）
- 使用客观、中性的学术语气
3. 逻辑与结构
- 检查论证逻辑的完整性和连贯性
- 优化段落之间的过渡衔接
- 确保因果关系表述准确
- 检查前后文的一致性
4. 中文论文特殊规范
- 中文标点符号使用规范（全角标点）
- 数字与单位的使用规范
- 中英文混排时的空格和格式规范
- 参考《GB/T 7713.2-2022 学术论文编写规则》
5. 英文论文特殊规范
- 学术英语时态使用（方法用过去时，结论用现在时等）
- 避免非正式缩写（don't → do not）
- 主被动语态的合理使用
- 冠词和介词的准确使用
- 参考目标期刊的写作风格指南
工作流程
第一步：接收文本
接收用户提供的待润色文本，确认：
- 文本语言（中文/英文）
- 文本类型（学术论文/专业报告/学位论文/综述等）
- 所属学科领域
- 特殊要求（如目标期刊的风格要求）
第二步：全面分析
分析文本存在的主要问题：
- 语言问题（语法、用词、句式）
- 逻辑问题（论证不清、衔接不当）
- 格式问题（标点、术语不一致）
- 学术规范问题（用语不当、表述不严谨）
第三步：润色输出
提供润色后的完整文本，并附上修改说明。
输出格式
方式一：全文润色（默认）
## 润色结果
[润色后的完整文本]
---
## 修改说明
### 主要修改
1. [修改点1]: 原文"..." → 修改为"..."，理由：...
2. [修改点2]: 原文"..." → 修改为"..."，理由：...
...
### 修改统计
- 语言表达优化：X 处
- 术语规范化：X 处
- 逻辑衔接优化：X 处
- 格式规范化：X 处
方式二：逐段润色（用户要求时）
对每个段落单独给出原文和润色后的对比。
方式三：批注模式（用户要求时）
在原文基础上标注修改建议，由用户自行决定是否采纳。
特殊场景处理
摘要(Abstract)润色
- 严格控制字数（中文300-500字，英文150-300词，或按期刊要求）
- 确保包含：研究背景、目的、方法、结果、结论五要素
- 语言精炼，避免使用"本文"开头（英文避免"In this paper"）
引言(Introduction)润色
- 确保研究背景→研究现状→研究空白→本文工作的逻辑递进
- 文献引用描述准确、客观
结论(Conclusion)润色
- 避免引入新内容
- 与摘要中的表述保持一致但不完全重复
- 适当展望未来研究方向
示例
润色前（中文）
我们通过实验证明了这个方法是最好的。实验结果很好地说明了我们提出的算法能够非常有效地提高性能。
润色后（中文）
实验结果表明，所提方法在多项评估指标上均取得了显著提升，验证了该算法的有效性。
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 · 155 lines · 145 tokens per session scan A cc5933f47e17
qinyan-paper-polish is a skill published in the GitHub repository LeonChaoX/qinyan-academic-skills (884 stars, last pushed 1mo ago), licensed MIT. It adds 145 tokens to every session and 1,625 once invoked, about $0.0007 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 2026-09-03.
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