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Cell Visualization Code

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yrui-cmd
cell-visualization-code

生成、重构或审核科研可视化代码,支持 Python 的 Matplotlib/Seaborn 与 MATLAB。 适用于用户要求按论文或报告规范编写绘图代码、统一多图样式、改造已有绘图脚本、 适配 IEEE Transactions 或 Elsevier 等出版版式、校准最终物理尺寸、矢量导出或 检查代码可复现性。保留数据、统计和科学含义, 不用于从参考论文反向复现数据图、机制示意图、图片编辑或图像描摹。

Overview

Publisheryrui-cmd
RepositoryCell
Skill namecell-visualization-code
Stars
111
Forks
1
Bundled files
19
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 19 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by yrui-cmd on GitHub. Read the source before you install it.

Installation

Install the Cell Visualization Code AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/yrui-cmd/Cell/tree/main/skills/cell-visualization-code
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yrui-cmd/Cell.git /tmp/Cell
mkdir -p .claude/skills
cp -r /tmp/Cell/skills/cell-visualization-code .claude/skills/cell-visualization-code
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cell Visualization Code in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cell Visualization Code on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cell Visualization Code is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

科研可视化代码规范

把绘图代码做成可复现的科研产物,而不是一次性调图脚本。最终判断基于真实数据、实际运行结果和目标版面中的可读性。

路由与边界

先确定任务是新建代码、重构已有代码还是审核代码,并保留用户指定的后端。

  • Python/Matplotlib/Seaborn:读取 Python 规范。
  • MATLAB:读取 MATLAB 规范。
  • 两条路径都读取 共享代码合同;选择字号和尺寸时读取 版式策略与示例 profile;准备交付时读取 输出与验收。
  • 目标为 IEEE Transactions 或 Elsevier 时,另读 出版商 Profile,并以具体期刊 Guide for Authors 覆盖通用值。
  • 需要将参考论文图先用参考数据复现、通过门槛后再换用户数据时,改用 cell-data-figure。
  • 需要生成机制图、图形摘要或概念示意图时,不使用本 Skill。
  • 只处理 LaTeX 全文分页、浮动体和投稿材料时,改用 cell-submission。

用户只要求审核时不直接改文件;用户要求生成或修改时才写入代码。已有代码能局部修复就不整体换语言、换库或重写分析流程。

不可破坏的事实

  1. 不改变观测值、分组、样本身份、单位、时间顺序、统计口径和缺失规则来改善外观。
  2. 不从图片重建虚假原始点,不用随机数替代缺失实验数据,不复制参考图的 P 值、误差条或效应量。
  3. 图中 n、误差、区间、显著性和比较对象必须能回到真实输入或已核验计算;设计信息不足时只做有边界的描述性图。
  4. 用户提供的已有计算结果默认只可视化;除非任务要求,不在绘图脚本中重新训练模型、重新拟合主分析或隐式改变筛选。
  5. 颜色不能成为唯一信息通道;同时使用线型、标记、标签、位置或面板结构。语义相同的对象在同一项目中保持相同编码。
  6. 哈希、文件存在、静态配置通过不能证明图正确;必须实际运行并查看输出。

工作流程

1. 锁定输入和用途

读取真实数据、已有代码、目标图和期刊/报告要求。明确每行数据代表什么、独立单位、配对或重复测量、单位、变换、缺失与筛选规则。确定最终使用场景、单栏/双栏/自定义宽度、需要的格式和是否要求可编辑源文件。

未知期刊尺寸时不要伪造官方要求;使用可说明的项目 profile,并把数字标为项目选择。IEEE 8.89 cm 等值只在已确认对应模板时使用。

2. 建立任务 profile

从 示例 profile 复制到任务工作区并替换为实际值。它记录后端、源画布、文档显示宽度、字体层级、线宽、语义颜色、输入和输出,不记录科研结论。native_final_size 的源宽等于显示宽;scaled_source 必须在导出后测量实际 PDF 边界并复核缩放后的可见字号,不能把两种策略的数值混用。

bash
python scripts/check_profile.py path/to/profile.json

检查通过只表示 profile 完整且路径安全。已有项目若使用同等配置对象,不要求为迁就本 Skill 重写格式,但必须保留相同信息。

3. 编写或重构代码

代码至少分离:输入读取、输入验证、必要计算、绘图、导出和入口。样式常量集中管理,单图代码只定义科学内容与必要布局。使用相对路径、命令行参数或显式配置,不写开发者机器的绝对路径。

随机抖动、抽样或布局算法显式保存种子;无随机过程不为形式添加种子。禁止静默捕获异常后继续输出“成功”图。

可从 Python 模板 或 MATLAB 模板 开始,但必须根据真实字段、设计和目标图修改;模板示例不是用户数据或完成证据。

4. 在最终物理尺寸下设计

先确定最终宽高,再设置字体、线宽、标记、图例和子图间距。不要先制作巨大画布再整体缩小,也不要用缩小字体解决布局冲突。单栏和双栏应是独立 profile;复杂多面板从一栏改两栏时重新组织面板,而非简单把宽度乘二。

图例不遮挡数据、科学计数法、误差条或关键区间。自动位置可以作为初值,正式交付前必须实际检查;人工拖动后的坐标要回写代码。

5. 实际运行和双层验收

在干净工作目录用交付代码和必要输入实际重跑。先检查图本身,再检查它插入最终论文/报告后的页面。源图在 100% 缩放下好看,不代表缩放到栏宽后可读。

若 bbox_inches="tight"、外置色条或轴外文字改变 PDF 页面边界,以导出后的实际尺寸为准。检查字体替换、文字是否仍为文字、线条/图元是否保持矢量,以及栅格层是否具有足够分辨率。

6. 交付

交付实际可运行的代码、最少必要输入或输入合同、任务 profile,以及用户或目标期刊要求的图件。MATLAB 在用户需要继续手调时可交 .fig;Python 不制造虚假的可编辑工程格式。

内部保留运行命令、软件版本、输入与输出哈希和实际查看记录;不把缓存、测试图、旧版脚本和未采用 profile 混入最终目录。未实际运行所选后端时,只能交待运行代码并明确未完成运行验收。

完成条件

  • 代码从声明的输入重新生成全部声明输出,不依赖开发者绝对路径或未交付的隐藏文件。
  • 数据映射、单位、统计标注、样本量和图注含义一致。
  • 最终物理尺寸、字体层级、线宽、面板、图例和颜色编码已经实际查看。
  • 矢量与栅格格式符合当前任务要求;扩展名与真实格式一致。
  • 同一项目的图使用同一个已确认 profile,确需例外时在代码中说明具体原因。
  • 插入目标文档后重新编译或渲染并检查;未完成这一项时不宣称出版版面验收通过。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Cell Visualization Code AI skill do?

生成、重构或审核科研可视化代码,支持 Python 的 Matplotlib/Seaborn 与 MATLAB。 适用于用户要求按论文或报告规范编写绘图代码、统一多图样式、改造已有绘图脚本、 适配 IEEE Transactions 或 Elsevier 等出版版式、校准最终物理尺寸、矢量导出或 检查代码可复现性。保留数据、统计和科学含义, 不用于从参考论文反向复现数据图、机制示意图、图片编辑或图像描摹。

Why use Cell Visualization Code on TypingMind?

Because you install it once and use it with any model. Cell Visualization Code is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cell Visualization Code in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yrui-cmd/Cell/tree/main/skills/cell-visualization-code. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cell Visualization Code?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cell Visualization Code?

As many as you like. As long as a model supports skills, you can use Cell Visualization Code with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cell Visualization Code AI skill free?

Yes. It is published on GitHub by yrui-cmd under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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