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-nature-reviewgit 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-nature-review)<a href="https://agentmods.dev/skills/leonchaox/qinyan-academic-skills/qinyan-nature-review"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/qinyan-nature-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/leonchaox/qinyan-academic-skills/qinyan-nature-review"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/qinyan-nature-review.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 | $0.00141 | $0.01144 |
| Opus 5 | $0.00071 | $0.00572 |
| Sonnet 5 | $0.00028 | $0.00229 |
| Haiku 4.5 | $0.00014 | $0.00114 |
Grade A, and why
qinyan-nature-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 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.
What it actually says
沁言 Nature 投稿前预审
以审稿人的证据标准审查稿件，不扮演编辑、不预测录用，也不替作者编写回复信。
默认评审包
除非用户指定其他格式，生成：
Review setup：输入范围、评审边界、稿件核心命题和可见证据。Lens A — Conceptual significance：问题重要性、原创性、广泛读者价值。Lens B — Technical integrity：设计、方法、统计、对照、可重复性。Lens C — Evidence and communication：主张—证据一致性、图文一致性、可读性与透明度。Cross-review synthesis：共识、分歧、优先修复顺序和未能评估事项。
这些是评审视角，不是虚构的审稿人身份、机构或专业履历。
评审原则
- 只依据用户提供的稿件、图表、数据和已核验来源。
- 对每条实质性问题分配稳定
Issue key和唯一Concern ID。 - 为问题绑定
Claim pointer与Evidence pointer；缺失时写NOT_LOCATABLE。 - 把“缺少材料无法判断”与“材料显示存在缺陷”分开。
- 只有同一
Issue key被至少两个评审视角独立提出，才能称为共识。 - 给出可验证的
Resolution test，不只说“需要更多实验”。 - 不以写作风格问题掩盖科学问题，也不把偏好包装成硬性要求。
执行流程
- 界定输入。 说明收到全文还是部分章节，以及缺失材料对结论的影响。
- 抽取共享事实库。 记录核心命题、关键证据、目标读者、方法设计和作者承认的限制。
- 建立问题台账。 按评审维度登记证据位置、严重级别、适用性和解决标准。
- 执行三种视角。 共享事实，但分别强调概念、技术、证据与表达；避免人为制造分歧。
- 生成交叉综合。 合并相同问题，保留不同权重，按 P0/P1/P2 排序。
- 运行一致性校验。 把报告保存为 Markdown，执行
python scripts/review_consistency.py <report.md>。 - 交付边界。 明确哪些判断不能从当前材料得出，避免给出虚假编辑决定。
评审维度与严重级别读取 references/review-framework.md。报告字段与综合规则读取 references/report-contract.md。
严重级别
P0：核心命题无法由现有设计或证据建立；通常需要改变主张或补充关键验证。P1：重要缺陷会显著削弱可信度、可重复性或解释，但存在明确修复路径。P2：局部清晰度、报告完整性或呈现问题，不改变主要结论。
严重级别表示对论证的影响，不等同于接收、修改或拒稿建议。
默认问题格式
Concern ID: A-M1
Issue key: evidence-causality-01
Severity: P0
Axis: claim–evidence alignment
Claim pointer: Results, paragraph 3
Evidence pointer: Fig. 2b–d
Concern: ...
Why it matters: ...
Resolution test: ...
红线
- 不虚构审稿人身份、稿件行号、图件内容、实验、文献或编辑政策。
- 不把期刊适配度陈述为确定事实。
- 不把领域偏好写成普遍方法学要求。
- 不把同一问题换词重复以制造“多人共识”。
- 不替作者隐去不利结果或合理限制。
- 用户要求回复审稿意见时，先完成问题解析，再交由适合的回复/写作流程。
资料路由
| 任务 | 读取 |
|---|---|
| 原创性、意义、严谨性、统计、复现与表达检查 | references/review-framework.md |
| 问题字段、三视角结构、共识规则与最终 QA | references/report-contract.md |
What ships with it
4 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.
- 9d ago First seen · 81 lines · 141 tokens per session scan A 9241fde18891
qinyan-nature-review is a skill published in the GitHub repository LeonChaoX/qinyan-academic-skills (884 stars, last pushed 1mo ago), licensed MIT. It adds 141 tokens to every session and 1,144 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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scopus-researcher
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lc_arxiv
A tool for searching arXiv, a public repository of research papers, mainly in science and technology.