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Audit score 70

data-report

nexu-io/open-design

How to install data-report

npx skills add https://github.com/nexu-io/open-design --skill data-report
Claude Code
Cursor
Windsurf
Cline
Full instructions (SKILL.md)

Source of truth, from nexu-io/open-design.


name: data-report zh_name: "数据可视化报告" en_name: "Data Visualization Report" emoji: "📊" description: "Turns CSV, Excel, or JSON data into a polished visual report page." zh_description: "把 CSV/Excel/JSON 数据转成漂亮的可视化报告页" en_description: "Turns CSV, Excel, or JSON data into a polished visual report page." category: data scenario: finance aspect_hint: "桌面长页面" featured: 10 tags: ["data", "report", "chart", "数据", "报告"] example_id: sample-data-weekly-report example_name: "数据报告 · 周报" example_format: csv example_tagline: "KPI 卡 + Chart.js 图表 + 表格" example_desc: "9 个月增长数据自动渲染成可视化报告, 内联 Chart.js" od: mode: prototype surface: web platform: desktop scenario: finance upstream: "https://github.com/nexu-io/html-anything" preview: type: html entry: index.html reload: debounce-100 design_system: requires: false example_prompt: "Use the Data Visualization Report template to turn my CSV, Excel, or JSON data into a polished visual report page. Preserve the template's visual signature, use real content and data, and avoid lorem ipsum or placeholder images." example_prompt_i18n: zh-CN: "用「数据可视化报告」模板把我的内容做成一份「把 CSV/Excel/JSON 数据转成漂亮的可视化报告页」。保持模板的视觉签名,使用真实内容和数据,避免 lorem ipsum 和占位图片。"

【模板: 数据可视化报告】

  • 头部: 报告标题 + 时间区间 + 数据来源说明。
  • KPI 卡片网格: 3-5 个最重要指标, 每个卡片显示数值 + 同比变化 + 微型趋势线。
  • 主图表区: 至少 2 个图表 (柱状 / 折线 / 饼 / 散点), 使用 Chart.js 或 ECharts (jsdelivr CDN 引入), 数据从用户输入解析得到。
  • 图表容器必须有固定高度: 每个 <canvas> 外层包一个 <div style="position:relative;height:NNNpx"> (KPI 迷你图 ~40px, 主图表 ~240–280px)。Chart.js 用 responsive:true, maintainAspectRatio:false 时若父容器没有显式高度, 会陷入 ResizeObserver 死循环, 图表无限增高直至卡死浏览器。绝对不要直接给 canvas 写 height= 属性当布局, 那个只是初始值。
  • 数据表格: 用户原始数据节选, 使用 <table> + 现代化样式 (zebra stripe, hover, sticky header)。
  • 洞察块: 3-5 条文字洞察, 用 emoji 开头, 像产品周报。
  • 底部"方法论"折叠区。
  • 配色克制专业: 主色 1 + 中性色阶, 图表用调色板。
  • 必须解析用户提供的实际数据, 不要捏造。

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