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-statisticsgit 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-statistics)<a href="https://agentmods.dev/skills/leonchaox/qinyan-academic-skills/qinyan-nature-statistics"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/qinyan-nature-statistics/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-statistics"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/qinyan-nature-statistics.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 | $0.01259 |
| Opus 5 | $0.00071 | $0.00629 |
| Sonnet 5 | $0.00028 | $0.00252 |
| Haiku 4.5 | $0.00014 | $0.00126 |
Grade A, and why
qinyan-nature-statistics 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
沁言 Nature 统计分析与报告
从研究设计和估计目标出发，再选择模型和检验。统计显著性不能替代效应大小、数据质量或科学意义。
工作模式
plan：在分析前定义问题、实验单位、主要终点、模型、校正与敏感性分析。analyse：用户提供数据后执行可复现分析，并保留数据处理与诊断记录。audit：审查现有统计方法、结果、表格与图注。rewrite：在事实充分时生成可粘贴的统计方法或结果文本。review-response：解析审稿人统计问题，给出验证路径与保守回复要点。
复杂临床试验、监管分析或患者级决策必须服从协议、统计分析计划和专业统计师审核。
必须先回答的设计问题
- 科学问题和主要 estimand 是什么？
- 独立实验单位是什么，
n如何定义？ - 生物重复、技术重复、子样本、批次和重复测量如何嵌套？
- 主要与次要终点、组别、时间点和协变量是什么？
- 分配、随机化、盲法、纳排、缺失和异常如何处理？
- 哪些比较是预设，哪些是探索性？
这些事实不清时，不给出最终检验选择；使用 AUTHOR_INPUT_NEEDED。
执行流程
- 建立设计图。 画出实验单位、层级、配对、重复测量、批次与时间结构。
- 定义 estimand。 指明要估计的差异、比值、斜率、关联、预测性能或时间效应及其目标人群。
- 审计数据。 记录数据类型、单位、缺失、范围、重复、异常、排除和变换；保留前后计数。
- 选择分析策略。 根据设计、分布、样本量和 estimand 选择模型，不仅依赖正态性检验。读取 references/analysis-plan.md。
- 执行与诊断。 报告模型假设、残差/拟合诊断、收敛、影响点、多重比较和敏感性分析。
- 解释效应。 优先给出效应量、置信区间和实际意义，再报告精确 p 值。
- 对齐图表。 确保图中数据层级、误差、星号、图注和正文与分析完全一致。读取 references/reporting-and-figures.md。
- 运行报告审计。 对统计文本执行
python scripts/reporting_audit.py <file> --context methods|results|legend。 - 交付复现信息。 提供分析代码、软件版本、随机种子、数据字典、处理日志和未解决风险。
实验单位、伪重复和常见故障读取 references/design-integrity.md。
默认输出
Statistical scope
- Mode / input / boundary:
- Scientific question and estimand:
- Independent unit and n:
- Design hierarchy:
Analysis specification
- Outcome / predictors / contrasts:
- Model or test:
- Assumptions and diagnostics:
- Multiplicity:
- Sensitivity analyses:
Results
- Effect estimate and uncertainty:
- Exact inferential result:
- Practical interpretation:
Ready-to-paste reporting
[Methods / Results / legend]
AUTHOR_INPUT_NEEDED
- [事实性缺口]
Reviewer-risk note
- [剩余风险]
红线
- 不虚构样本量、p 值、自由度、区间、功效、软件版本、排除、随机化或盲法。
- 不把细胞、视野、技术读数、模拟运行或同一个体的多次测量默认为独立
n。 - 不用“显著”表示重要、巨大、因果或生物学相关。
- 不因 p > 0.05 宣称“无差异”或“等效”，除非设计支持相应推断。
- 不用组内显著/不显著差异推断组间交互。
- 不通过删除数据、改变终点或尝试多个模型后只报告最佳结果来追求显著性。
- 不把探索性分析包装成预设确认性分析。
资料路由
| 任务 | 读取 |
|---|---|
| 实验单位、嵌套、重复测量、伪重复、缺失与排除 | references/design-integrity.md |
| estimand、模型选择、诊断、效应量、多重比较与敏感性 | references/analysis-plan.md |
| Methods、Results、表格、图注和统计图形报告 | references/reporting-and-figures.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 · 93 lines · 141 tokens per session scan A 3b486b288e6b
qinyan-nature-statistics 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 1,259 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.
Other skills, from other repositories
academic-research
Nested swiss-knife reference for academic literature work — find papers, fetch full-text PDFs, trace citations, write LaTeX manuscripts. First action for any "get me this paper" request: python3 /scripts/fetchpaper.py — walks arXiv → Unpaywall → Europe PMC → CORE → in-house publisher-page extraction…
research-agent
Project-first Research pipeline with live gates, source/PDF evidence, claim boundaries, compute and publication controls; includes an optional PubMed/arXiv standard-library helper.
openalex
Use when the user asks about academic literature, research papers, scholarly works, authors, citations, institutions, journals, or any academic metadata. Trigger when users want to search for papers, find author profiles, track citations, discover related works, or explore academic topics. Also use when users mention…
paper-comic
A visual-explanation workflow for showing what a research paper's method does and how it works. It analyzes the paper, proposes cover, overview, and mechanism diagrams, and waits for the user's choices before generating them.
scopus-researcher
Expert academic researcher using the Scopus MCP. Finds papers, retrieves full abstracts, builds author profiles, analyzes citation impact, and constructs advanced Boolean queries across the Elsevier Scopus database. Activate when asked to search for academic papers, analyze research trends, find citations, profile…
lc_arxiv
A tool for searching arXiv, a public repository of research papers, mainly in science and technology.