Agent skill

Qinyan Nature Statistics

by LeonChaoX in LeonChaoX/qinyan-academic-skills

面向 Nature Portfolio 与高影响力期刊的实验设计一致型统计分析、审查和报告技能。用于定义独立实验单位与重复层级、制定统计分析计划、检查数据质量、选择模型与估计量、计算效应量和不确定性、处理多重比较与敏感性分析,并重写 Methods、Results、表格和图注中的统计文本。触发场景包括 Nature 统计、数据统计、statistical analysis、p…

MITAuto-check passedData & Analytics

Install Qinyan Nature Statistics

skills CLI
$ npx skills add LeonChaoX/qinyan-academic-skills --skill qinyan-nature-statistics -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeonChaoX/qinyan-academic-skills qinyan-nature-statistics --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeonChaoX/qinyan-academic-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'skills/沁言学术skills/qinyan-nature-statistics' .claude/skills/qinyan-nature-statistics && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
qinyan-nature-statistics
GitHub stars
944
Token cost
~639 tokens
SKILL.md length
94 words
Files
6 (incl. scripts, references)
Skills in repo
31
Repo updated
First seen
Licence
MIT

At a glance

面向 Nature Portfolio 与高影响力期刊的实验设计一致型统计分析、审查和报告技能。用于定义独立实验单位与重复层级、制定统计分析计划、检查数据质量、选择模型与估计量、计算效应量和不确定性、处理多重比较与敏感性分析,并重写 Methods、Results、表格和图注中的统计文本。触发场景包括 Nature 统计、数据统计、statistical analysis、p…

  • Works in 6 steps: 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? → …
  • Tasks that involve Statistics
  • SKILL.md covers 工作模式, 必须先回答的设计问题, 执行流程 and 默认输出, plus 2 more sections
  • Runs Python scripts from its folder; calls python

What it does

Qinyan Nature Statistics is an agent skill from LeonChaoX/qinyan-academic-skills. 面向 Nature Portfolio 与高影响力期刊的实验设计一致型统计分析、审查和报告技能。用于定义独立实验单位与重复层级、制定统计分析计划、检查数据质量、选择模型与估计量、计算效应量和不确定性、处理多重比较与敏感性分析,并重写 Methods、Results、表格和图注中的统计文本。触发场景包括 Nature 统计、数据统计、statistical analysis、p value、sample size、effect size、confidence interval、replicates、multiple comparisons、统计方法、图注统计和审稿人统计意见。

Its SKILL.md is about 640 tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/analysis-plan.md` and `references/design-integrity.md`).

It sits in Data & Analytics, covering Statistics. The repository describes itself as: A curated, multilingual library of 182 installable AI agent skills for end-to-end academic research—spanning literature discovery, scientific writing, grant development… The licence is MIT.

When your agent uses it

  • Tasks that involve Statistics

Example prompts

  • “/qinyan-nature-statistics”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. 科学问题和主要 estimand 是什么?
  2. 独立实验单位是什么,n 如何定义?
  3. 生物重复、技术重复、子样本、批次和重复测量如何嵌套?
  4. 主要与次要终点、组别、时间点和协变量是什么?
  5. 分配、随机化、盲法、纳排、缺失和异常如何处理?
  6. 哪些比较是预设,哪些是探索性?

What it can do on your machine

Read from SKILL.md and the folder at commit df5a498. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Qinyan Nature Statistics loads about 639 tokens when it runs, and up to ~2.4k if it reads all its reference files. Until then it costs about 79 tokens; SKILL.md has 94 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~79
When it runs · the whole SKILL.md, loaded when a task matches
~639
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from LeonChaoX/qinyan-academic-skills at commit df5a498, republished under its MIT licence (© LeonChaoX). 94 words, ~639 tokens.

Download SKILL.mdSave it as .claude/skills/qinyan-nature-statistics/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
qinyan-nature-statistics
description
面向 Nature Portfolio 与高影响力期刊的实验设计一致型统计分析、审查和报告技能。用于定义独立实验单位与重复层级、制定统计分析计划、检查数据质量、选择模型与估计量、计算效应量和不确定性、处理多重比较与敏感性分析,并重写 Methods、Results、表格和图注中的统计文本。触发场景包括 Nature 统计、数据统计、statistical analysis、p value、sample size、effect size、confidence interval、replicates、multiple comparisons、统计方法、图注统计和审稿人统计意见。

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

  1. 科学问题和主要 estimand 是什么?
  2. 独立实验单位是什么,n 如何定义?
  3. 生物重复、技术重复、子样本、批次和重复测量如何嵌套?
  4. 主要与次要终点、组别、时间点和协变量是什么?
  5. 分配、随机化、盲法、纳排、缺失和异常如何处理?
  6. 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

  1. 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。
  2. 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。
  3. 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。
  4. 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。
  5. 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。
  6. 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。
  7. 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。
  8. 运行报告审计。 对统计文本执行 python scripts/reporting_audit.py <file> --context methods|results|legend。
  9. 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

text
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

© LeonChaoX, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (scripts, references) in skills/沁言学术skills/qinyan-nature-statistics of LeonChaoX/qinyan-academic-skills.

  • SKILL.md
  • agents/openai.yaml
  • references/analysis-plan.md
  • references/design-integrity.md
  • references/reporting-and-figures.md
  • scripts/reporting_audit.py

Open the folder on GitHubat commit df5a498

Compare with similar skills

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Qinyan Nature Statistics compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Qinyan Nature Statistics this skillLeonChaoX/qinyan-academic-skills944—~639Automated safety check: PassMIT
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AI Daily DigestvigorX777/ai-daily-digest1.6k—~1.3kAutomated safety check: PassNone
Statistical Powerspacering-net/codeg3.9k1 repos~3.6kAutomated safety check: NotesMIT

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Questions about Qinyan Nature Statistics

What does Qinyan Nature Statistics do?

面向 Nature Portfolio 与高影响力期刊的实验设计一致型统计分析、审查和报告技能。用于定义独立实验单位与重复层级、制定统计分析计划、检查数据质量、选择模型与估计量、计算效应量和不确定性、处理多重比较与敏感性分析,并重写 Methods、Results、表格和图注中的统计文本。触发场景包括 Nature 统计、数据统计、statistical analysis、p…. Qinyan Nature Statistics is an agent skill from LeonChaoX/qinyan-academic-skills.

When should I use Qinyan Nature Statistics?

Qinyan Nature Statistics fits situations like: tasks that involve Statistics.

How do I install Qinyan Nature Statistics in Claude Code?

Run `npx skills add LeonChaoX/qinyan-academic-skills --skill qinyan-nature-statistics -a claude-code`. Or copy the skill folder (skills/沁言学术skills/qinyan-nature-statistics in LeonChaoX/qinyan-academic-skills) into .claude/skills/qinyan-nature-statistics in your project. Claude Code loads it when a task matches its description.

How do I install Qinyan Nature Statistics in Codex?

Run `npx skills add LeonChaoX/qinyan-academic-skills --skill qinyan-nature-statistics -a codex`. Or copy the skill folder (skills/沁言学术skills/qinyan-nature-statistics in LeonChaoX/qinyan-academic-skills) into .agents/skills/qinyan-nature-statistics in your project. Codex loads it when a task matches its description.

Can I use Qinyan Nature Statistics in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeonChaoX/qinyan-academic-skills --skill qinyan-nature-statistics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/qinyan-nature-statistics, .gemini/skills/qinyan-nature-statistics, .github/skills/qinyan-nature-statistics and .opencode/skills/qinyan-nature-statistics in your project.

What does Qinyan Nature Statistics need to run?

Going by SKILL.md and its folder, Qinyan Nature Statistics needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Qinyan Nature Statistics access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Qinyan Nature Statistics safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Qinyan Nature Statistics use?

Qinyan Nature Statistics is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Qinyan Nature Statistics use?

About 639 tokens (SKILL.md is roughly 2.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.7k tokens, read only when the agent opens those files.

What are the alternatives to Qinyan Nature Statistics?

Skills that share tags, products or a category with Qinyan Nature Statistics: Sandbox Bench (vercel/next.js, 143k stars), Statistical Analysis (spacering-net/codeg, 3.9k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.7k stars) and AI Daily Digest (vigorX777/ai-daily-digest, 1.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Qinyan Nature Statistics?

LeonChaoX (a GitHub user) maintains it in LeonChaoX/qinyan-academic-skills, which has 944 GitHub stars. The repository holds 31 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeonChaoX/qinyan-academic-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.