Agent skill

Qinyan Nature Figures

by LeonChaoX in LeonChaoX/qinyan-academic-skills

面向 Nature Portfolio 与高影响力期刊的证据驱动科研绘图技能。用于从原始或汇总数据设计单图与多面板 figure、选择合适图形语法、编写 Python/R 绘图代码、重绘现有图件、生成机制示意图草案、撰写图注并导出可编辑 SVG/PDF 与高分辨率 TIFF/PNG;同时检查数据完整性、颜色可访问性、统计标注和最终尺寸可读性。触发场景包括 Nature…

MITAuto-check passedResearch & Science

Install Qinyan Nature Figures

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

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

GitHub CLI
$ gh skill install LeonChaoX/qinyan-academic-skills qinyan-nature-figures --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-figures' .claude/skills/qinyan-nature-figures && 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-figures
GitHub stars
944
Token cost
~600 tokens
SKILL.md length
93 words
Files
6 (incl. scripts, references)
Skills in repo
31
Repo updated
First seen
Licence
MIT

At a glance

面向 Nature Portfolio 与高影响力期刊的证据驱动科研绘图技能。用于从原始或汇总数据设计单图与多面板 figure、选择合适图形语法、编写 Python/R 绘图代码、重绘现有图件、生成机制示意图草案、撰写图注并导出可编辑 SVG/PDF 与高分辨率 TIFF/PNG;同时检查数据完整性、颜色可访问性、统计标注和最终尺寸可读性。触发场景包括 Nature…

  • Works in 6 steps: 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。 → …
  • Tasks that involve Data visualization
  • SKILL.md covers 路由, 图件契约, 执行流程 and 默认交付, plus 2 more sections
  • Runs Python scripts from its folder; calls python

What it does

Qinyan Nature Figures is an agent skill from LeonChaoX/qinyan-academic-skills. 面向 Nature Portfolio 与高影响力期刊的证据驱动科研绘图技能。用于从原始或汇总数据设计单图与多面板 figure、选择合适图形语法、编写 Python/R 绘图代码、重绘现有图件、生成机制示意图草案、撰写图注并导出可编辑 SVG/PDF 与高分辨率 TIFF/PNG;同时检查数据完整性、颜色可访问性、统计标注和最终尺寸可读性。触发场景包括 Nature 绘图、科研作图、论文配图、scientific figure、publication plot、multi-panel figure、graphical abstract、机制图、图形摘要和 figure audit。

Its SKILL.md is about 600 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/chart-selection.md` and `references/figure-contract.md`).

It sits in Research & Science, covering Data visualization. It works with Python. 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 Data visualization

Example prompts

  • “/qinyan-nature-figures”

Requirements

  • Python 3

Workflow steps

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

  1. Conclusion:读者看完图后应能复述的一句话。
  2. Evidence hierarchy:主证据、支持证据、对照与边界。
  3. Panel map:每个面板的任务、数据和与其他面板的关系。
  4. Data contract:变量、单位、独立样本、缺失、排除和变换规则。
  5. Statistics contract:估计量、误差、检验、校正、n 与配对/重复结构。
  6. Export contract:栏宽、目标尺寸、字体、矢量/栅格、分辨率和 source data。

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 Figures loads about 600 tokens when it runs, and up to ~1.9k if it reads all its reference files. Until then it costs about 79 tokens; SKILL.md has 93 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
~600
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.9k

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). 93 words, ~600 tokens.

Download SKILL.mdSave it as .claude/skills/qinyan-nature-figures/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
qinyan-nature-figures
description
面向 Nature Portfolio 与高影响力期刊的证据驱动科研绘图技能。用于从原始或汇总数据设计单图与多面板 figure、选择合适图形语法、编写 Python/R 绘图代码、重绘现有图件、生成机制示意图草案、撰写图注并导出可编辑 SVG/PDF 与高分辨率 TIFF/PNG;同时检查数据完整性、颜色可访问性、统计标注和最终尺寸可读性。触发场景包括 Nature 绘图、科研作图、论文配图、scientific figure、publication plot、multi-panel figure、graphical abstract、机制图、图形摘要和 figure audit。

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:

  1. Conclusion:读者看完图后应能复述的一句话。
  2. Evidence hierarchy:主证据、支持证据、对照与边界。
  3. Panel map:每个面板的任务、数据和与其他面板的关系。
  4. Data contract:变量、单位、独立样本、缺失、排除和变换规则。
  5. Statistics contract:估计量、误差、检验、校正、n 与配对/重复结构。
  6. 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

  1. 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。
  2. 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。
  3. 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。
  4. 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。
  5. 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。
  6. 自动预检。 执行 python scripts/figure_preflight.py <source.py|source.R> --artifact <figure.svg> ...。
  7. 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。
  8. 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

text
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 QAreferences/visual-standards.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-figures of LeonChaoX/qinyan-academic-skills.

  • SKILL.md
  • agents/openai.yaml
  • references/chart-selection.md
  • references/figure-contract.md
  • references/visual-standards.md
  • scripts/figure_preflight.py

Open the folder on GitHubat commit df5a498

Compare with similar skills

Qinyan Nature Figures next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Qinyan Nature Figures compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Qinyan Nature Figures this skillLeonChaoX/qinyan-academic-skills944—~600Automated safety check: PassMIT
Nature-Style Scientific FiguresYuan1z0825/nature-skills47k—~3.1kAutomated safety check: PassApache-2.0
deepTools NGS Toolkitdavila7/claude-code-templates33k12 repos~4.5kAutomated safety check: PassMIT
FBA Flux Analyzeraiming-lab/AutoResearchClaw15k—~2.3kAutomated safety check: PassMIT
MathModel Figure Templatesjihe520/MathModelAgent6.2k—~627Automated safety check: NotesNone
Scientific Figure MakingChenLiu-1996/figures4papers8.3k—~557Automated safety check: PassCustom licence

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Works with

Questions about Qinyan Nature Figures

What does Qinyan Nature Figures do?

面向 Nature Portfolio 与高影响力期刊的证据驱动科研绘图技能。用于从原始或汇总数据设计单图与多面板 figure、选择合适图形语法、编写 Python/R 绘图代码、重绘现有图件、生成机制示意图草案、撰写图注并导出可编辑 SVG/PDF 与高分辨率 TIFF/PNG;同时检查数据完整性、颜色可访问性、统计标注和最终尺寸可读性。触发场景包括 Nature…. Qinyan Nature Figures is an agent skill from LeonChaoX/qinyan-academic-skills.

When should I use Qinyan Nature Figures?

Qinyan Nature Figures fits situations like: tasks that involve Data visualization.

How do I install Qinyan Nature Figures in Claude Code?

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

How do I install Qinyan Nature Figures in Codex?

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

Can I use Qinyan Nature Figures 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-figures -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-figures, .gemini/skills/qinyan-nature-figures, .github/skills/qinyan-nature-figures and .opencode/skills/qinyan-nature-figures in your project.

What does Qinyan Nature Figures need to run?

Going by SKILL.md and its folder, Qinyan Nature Figures 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 Figures 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 Figures 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 Figures use?

Qinyan Nature Figures 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 Figures use?

About 600 tokens (SKILL.md is roughly 2.4k 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 1.3k tokens, read only when the agent opens those files.

What are the alternatives to Qinyan Nature Figures?

Skills that share tags, products or a category with Qinyan Nature Figures: Nature-Style Scientific Figures (Yuan1z0825/nature-skills, 47k stars), deepTools NGS Toolkit (davila7/claude-code-templates, 33k stars), FBA Flux Analyzer (aiming-lab/AutoResearchClaw, 15k stars) and MathModel Figure Templates (jihe520/MathModelAgent, 6.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Qinyan Nature Figures?

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.