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-figuresgit 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-figures)<a href="https://agentmods.dev/skills/leonchaox/qinyan-academic-skills/qinyan-nature-figures"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/qinyan-nature-figures/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-figures"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/qinyan-nature-figures.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 | $0.00164 | $0.01253 |
| Opus 5 | $0.00082 | $0.00626 |
| Sonnet 5 | $0.00033 | $0.00251 |
| Haiku 4.5 | $0.00016 | $0.00125 |
Grade A, and why
qinyan-nature-figures 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
沁言 Nature 科研绘图
先定义图要证明什么，再决定画什么。期刊级图件是证据结构、视觉层级、数据诚信和可复现导出的共同产物。
路由
- 定量图件：使用 Python（matplotlib/seaborn）或 R（ggplot2/patchwork/ComplexHeatmap）。
- 机制图或图形摘要：先建立概念与关系清单，再使用矢量工具或可用的图像生成能力制作草案；不得用 AI 图替代定量证据。
- 已有图件审查：同时检查图源代码、最终导出和最终版面尺寸，不能只看屏幕截图。
优先服从用户现有语言与项目栈。用户未指定且不存在项目约束时，默认使用 Python，并在交付中说明；只有选择会显著影响复现或协作时才询问。
图件契约
绘图前写出：
Conclusion：读者看完图后应能复述的一句话。Evidence hierarchy：主证据、支持证据、对照与边界。Panel map：每个面板的任务、数据和与其他面板的关系。Data contract：变量、单位、独立样本、缺失、排除和变换规则。Statistics contract：估计量、误差、检验、校正、n与配对/重复结构。Export contract：栏宽、目标尺寸、字体、矢量/栅格、分辨率和 source data。
详细模板读取 references/figure-contract.md。
执行流程
- 审计数据。 保留输入行数、排除规则、变换和聚合前后计数；不得静默删除异常或缺失值。
- 选择图形。 根据科学问题、变量类型和实验层级选图，不按“看起来像顶刊”选图。读取 references/chart-selection.md。
- 规划版面。 先安排主面板与阅读顺序，再写绘图代码；面板数量服务论证，不追求填满页面。
- 编码与导出。 固定随机种子、字体、尺寸、颜色、排序和导出参数；保留可运行源码。
- 写图注。 说明样本、
n、中心量、误差、检验、校正、符号和缩写，使图注可独立理解。 - 自动预检。 执行
python scripts/figure_preflight.py <source.py|source.R> --artifact <figure.svg> ...。 - 视觉核验。 在最终印刷尺寸检查标签、图例、线宽、遮挡、色盲可辨识、面板一致性和缩放后的栅格清晰度。
- 交付溯源包。 提供源码、导出文件、source data、参数说明、排除记录和剩余风险。
视觉与导出标准读取 references/visual-standards.md。
默认交付
Figure contract
- Conclusion:
- Evidence hierarchy:
- Panel map:
- Data/statistics contract:
- Export contract:
Artifacts
- source:
- vector:
- raster:
- source data:
Integrity log
- input rows:
- exclusions:
- transformations:
- output rows:
Preflight and visual QA
- passed:
- warnings:
- author checks:
质量门槛
- 图形类型与数据结构、实验单位和统计推断一致。
- 主图尽量展示观测分布或个体点，而非只显示柱高与星号。
- 颜色不作为唯一编码；使用色盲可辨且语义稳定的配色。
- 面板标签、字体、线宽、单位和小数精度一致。
- 矢量文本保持可编辑；照片或显微图按目标尺寸满足分辨率要求。
- 所有排除、平滑、截断、归一化和聚合均可追溯。
- 图注与正文使用相同
n、检验、误差和比较方向。 - 机制示意图明确区分已证实关系、推测路径和视觉隐喻。
资料路由
| 任务 | 读取 |
|---|---|
| 结论、证据层级、面板、数据与导出契约 | references/figure-contract.md |
| 按变量和科学问题选择图形 | references/chart-selection.md |
| 字体、颜色、尺寸、矢量/栅格、图注和最终 QA | references/visual-standards.md |
What ships with it
5 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 · 90 lines · 164 tokens per session scan A fc629ded3102
qinyan-nature-figures is a skill published in the GitHub repository LeonChaoX/qinyan-academic-skills (884 stars, last pushed 1mo ago), licensed MIT. It adds 164 tokens to every session and 1,253 once invoked, about $0.0008 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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