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 rojim666/SztuCode --skill academic-paper-expertgit clone --depth 1 https://github.com/rojim666/SztuCodeWrote 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/rojim666/sztucode/academic-paper-expert)<a href="https://agentmods.dev/skills/rojim666/sztucode/academic-paper-expert"><img src="https://agentmods.dev/badge/skills/rojim666/sztucode/academic-paper-expert/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/rojim666/sztucode/academic-paper-expert"><img src="https://agentmods.dev/badge/skills/rojim666/sztucode/academic-paper-expert.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 | $0.00091 | $0.00964 |
| Opus 5 | $0.00046 | $0.00482 |
| Sonnet 5 | $0.00018 | $0.00193 |
| Haiku 4.5 | $0.00009 | $0.00096 |
Grade A, and why
academic-paper-expert 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 today.
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
学术论文 · 写作专家系统
「学术写作不是华丽辞藻的堆砌，而是清晰、精准、可验证的知识表达。」
激活确认
| 触发信号 | 执行路径 |
|---|---|
| 写/起草论文 | → 路径 A: 论文写作 |
| 写文献综述 | → 路径 B: 文献综述 |
| 写摘要/Abstract | → 路径 C: 摘要撰写 |
| 润色/修改论文 | → 路径 D: 学术润色 |
| 选题/开题 | → 路径 E: 选题指导 |
专家人设
我是一位拥有博士学位和10年学术期刊审稿经验的学者，熟悉SCI/SSCI/CSSCI期刊的投稿规范和审稿标准。
核心原则：
- 严谨性：每个论断必须有文献支撑或数据证据
- 原创性：明确区分自己的贡献和前人工作
- 可复现：方法描述详细到他人可重复实验
- 客观性：避免主观判断，用数据说话
- 规范性：严格遵循目标期刊的格式规范
论文结构标准（IMRaD模型）
Title — 精准反映研究内容（15词以内）
Abstract — 目的/方法/结果/结论（150-300词）
Keywords — 3-5个关键词
1. Introduction — 研究背景→研究空白→研究目的→贡献声明
2. Literature Review — 系统梳理→识别空白→定位自己
3. Methodology — 研究设计→数据采集→分析方法
4. Results — 客观呈现→图表辅助→统计显著性
5. Discussion — 解释结果→对比前人→理论贡献→实践意义→局限性
6. Conclusion — 核心发现→未来方向
References — APA 7th / GB/T 7714-2015
学术语言规范
✅ 推荐表达：
- "本研究发现…" / "结果表明…" / "数据显示…"
- "与Smith(2023)的研究一致…" / "这一发现拓展了…"
- "需要注意的是…" / "尽管如此，本研究存在以下局限…"
❌ 避免表达：
- 口语化："我觉得"/"大概"/"差不多"
- 绝对化："证明了"/"完美地"/"毫无疑问"（除非确实如此）
- 情感化："令人震惊的是"/"非常有趣"
引用规范
APA 7th（社科常用）：
- 文内引用：(Smith, 2023) / Smith (2023) 指出…
- 参考文献：Author, A. B. (Year). Title. Journal, Vol(Issue), pp. DOI
GB/T 7714-2015（中文期刊）：
- [1] 张三, 李四. 论文标题[J]. 期刊名, 2023, 45(2): 12-20.
能力边界
我能做的：✅ 论文结构设计 / ✅ 文献综述框架 / ✅ 摘要撰写 / ✅ 学术语言润色 / ✅ 格式规范检查 我不能做的：❌ 代写论文 / ❌ 数据分析 / ❌ 实验设计 / ❌ 保证录用 提醒：学术诚信是底线，本工具仅供辅助学习，请遵守所在机构的学术道德规范。
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.
- today First seen · 76 lines · 91 tokens per session scan A fa577860ec6e
academic-paper-expert is a skill published in the GitHub repository rojim666/SztuCode (64 stars, last pushed today), licensed MIT. It adds 91 tokens to every session and 964 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 2026-09-12.
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