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Dyz Med Figures Tables

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duyunzhoudyz
dyz-med-figures-tables

规划、生成和核对医学论文的结果表格与统计图,建立数据来源清单,检查正文、图表和已执行分析输出的一致性, 并生成可审阅的标题、图注和脚注。当用户问“需要哪些图表”“帮我做森林图/Table 1”或要求核对图文数字时 使用;CONSORT、STROBE、PRISMA 等流程图由 dyz-med-flowchart 专门处理。 Use for medical manuscript tables, statistical figures and text–figure consistency.

Overview

Publisherduyunzhoudyz
Repositorydyz-med-paper-skills
Skill namedyz-med-figures-tables
Stars
102
Forks
1
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

    Published by duyunzhoudyz on GitHub. Read the source before you install it.

Installation

Install the Dyz Med Figures Tables 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/duyunzhoudyz/dyz-med-paper-skills/tree/main/dyz-med-figures-tables
  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/duyunzhoudyz/dyz-med-paper-skills.git /tmp/dyz-med-paper-skills
mkdir -p .claude/skills
cp -r /tmp/dyz-med-paper-skills/dyz-med-figures-tables .claude/skills/dyz-med-figures-tables
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dyz Med Figures Tables 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 Dyz Med Figures Tables 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 Dyz Med Figures Tables 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.

医学论文图表规划、生成与核对

核心原则

  1. 每个图表都服务于一个研究问题;不要按研究类型机械生成整套图表。
  2. 数值必须来自同一次成功执行且可追溯的分析输出,不从示例、占位符或稿件文字反向造表。
  3. 先做图表计划,再生成代码或文件。源数据缺失时交付规格和缺项,不填入模拟数值。
  4. 正文、图表与结构化结果不一致时追溯源脚本和运行日志,不自动选择“看起来正确”的一个。
  5. 不自动安装包,也不运行仍含占位符或未经语法检查的脚本。
  6. 期刊格式会变化;投稿级导出前核对期刊官网的当前作者指南并记录核对日期。

所需输入

  • 研究设计、研究问题和预设结局;
  • 已执行分析的结果文件、脚本、日志和数据字典;
  • 当前稿件及已有图表;
  • 目标期刊及其图表数量、尺寸、格式、分辨率和颜色要求;
  • 需要交付的可编辑格式和最终提交格式。

工作流

1. 建立图表计划

为每个候选项目记录:

text
编号 | 目的 | 数据/结果来源 | 关键字段 | 生成方式 | 输出格式
正文引用位置 | 状态 | 负责人 | 尚缺信息

删除不能回答研究问题、与正文重复或无法追溯的数据展示。把流程图列入计划,但交给 dyz-med-flowchart 生成。

2. 选择最小充分展示

以下只作选择提示,不是强制清单:

研究类型常见核心展示条件性展示
随机试验基线描述、主要/次要结局、不良事件生存曲线、预设亚组图
观察性研究人群描述、调整前后效应估计生存曲线、剂量反应、影响诊断
系统综述/Meta研究特征、风险偏倚、合并效应小研究效应图、影响分析
诊断准确性2×2 数据、敏感度/特异度及区间ROC 或分层森林图
预测模型模型表现、校准图决策曲线,仅在方法和数据支持时
定性/范围综述参与者或证据来源特征、主题/证据地图时间、地域或证据缺口图

漏斗图通常只在研究数量和方法条件足够时考虑;不对称检验一般至少需要 10 项研究,还要按效应量选择方法。 不把不对称直接标为发表偏倚。不要为美观生成 3D 图、双轴图或没有不确定性表达的装饰图。

3. 生成表格

  • 明确分析人群、分母、缺失值、单位、参考类别、效应量和区间水平。
  • RCT 基线表以描述为主,不做基线显著性检验,也不按基线 P 值决定调整变量。
  • 观察性研究列出每个模型的调整集及选择依据。
  • 回归表报告模型样本量、事件数、估计值、区间、单位和必要诊断。
  • 系统综述区分研究、报告和结局行;Summary of Findings 只有在完成相应证据体评估后生成。
  • 保留可编辑表格,并另存最终投稿格式。

4. 生成统计图

  • 从实际输入模式生成最短可复现脚本;不要套用字段未核对的通用代码。
  • 对比值型效应使用正确的对数轴和无效线,对差值型效应使用线性轴和零线。
  • 展示区间、删失、风险集、样本量或其他理解图形所必需的信息。
  • 亚组图优先展示交互检验,不用“某亚组显著、另一亚组不显著”推断组间差异。
  • ROC、校准、决策曲线和阈值应对应独立验证或明确的数据集与时间点。
  • 使用色盲友好配色、可读字号和清晰图例;颜色不作为唯一编码。

生成后必须:

  1. 运行语法检查;
  2. 在当前环境执行脚本;
  3. 检查退出状态和警告;
  4. 保存软件版本、脚本、日志和输出;
  5. 目视检查裁切、字体、图例、轴、面板和分辨率。

若环境无法执行,交付标为 not_rendered 的脚本和依赖清单,不声称图已生成。

5. 核对图、表、正文

使用图文一致性清单,至少检查:

  • 样本量、事件数、分母和分析人群;
  • 估计值、区间、P 值、单位、方向和参考组;
  • 允许的四舍五入差异;
  • 图表编号、正文引用、补充材料引用和文件存在性;
  • 同一结局的时间点、调整集和数据版本;
  • 图注与图中实际内容是否一致。

状态使用:

  • MATCH:来自同一结果且在允许的舍入范围内;
  • MISMATCH:数值或定义冲突;
  • MISSING_SOURCE:无法追溯到执行输出;
  • NOT_CITED:图表存在但稿件未引用;
  • MISSING_FILE:稿件引用但文件不存在。

6. 写标题、图注和脚注

标题说明展示对象与设计,不重复正文结论。图注/表注按需要写明:

  • 人群、分析集、结局、时间点和单位;
  • 效应量、区间、参考线和调整集;
  • 缺失数据、删失、误差条或模型说明;
  • 面板、颜色、线型和符号含义;
  • 缩写全称及统计检验。

不要固定加入 *P<0.05 星号体系;仅在期刊要求且多重性和检验定义清楚时使用。

7. 按期刊要求导出

从期刊官网记录核对日期和要求,再决定 TIFF、EPS、PDF、PNG、DOCX 等格式、尺寸、DPI、颜色空间和字体嵌入。 矢量图优先保持矢量;线稿、照片和组合图按期刊各自要求处理,不套用一个固定 DPI。

输出

建议交付:

text
paper/data/figures/figure-plan.yaml
paper/analysis/figures/
paper/data/figures/
paper/data/tables/
paper/evidence/display-items.md
paper/data/figures/consistency-report.md

目录可适配现有项目。每个最终文件都应指向来源结果、生成脚本、运行日志和期刊规格记录。

完成标准

  • 每个图表有明确目的和可追溯来源
  • 没有占位数据、未执行结果或无必要展示
  • 生成脚本通过语法检查并真实执行,或明确标为未渲染
  • 图表、正文和分析输出已核对
  • 标题、图注、脚注和缩写完整
  • 可访问性和目视检查通过
  • 当前期刊规格及核对日期已记录

参考材料

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 Dyz Med Figures Tables AI skill do?

规划、生成和核对医学论文的结果表格与统计图,建立数据来源清单,检查正文、图表和已执行分析输出的一致性, 并生成可审阅的标题、图注和脚注。当用户问“需要哪些图表”“帮我做森林图/Table 1”或要求核对图文数字时 使用;CONSORT、STROBE、PRISMA 等流程图由 dyz-med-flowchart 专门处理。 Use for medical manuscript tables, statistical figures and text–figure consistency.

Why use Dyz Med Figures Tables on TypingMind?

Because you install it once and use it with any model. Dyz Med Figures Tables 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 Dyz Med Figures Tables in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/duyunzhoudyz/dyz-med-paper-skills/tree/main/dyz-med-figures-tables. 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 Dyz Med Figures Tables?

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 Dyz Med Figures Tables?

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

Is the Dyz Med Figures Tables AI skill free?

It is published on GitHub by duyunzhoudyz. Check the repository for licensing terms. 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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