Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add cass-2003/local-workflow-skill --skill data-visualizationgit clone --depth 1 https://github.com/cass-2003/local-workflow-skillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cass-2003/local-workflow-skill/data-visualization)<a href="https://agentmods.dev/skills/cass-2003/local-workflow-skill/data-visualization"><img src="https://agentmods.dev/badge/skills/cass-2003/local-workflow-skill/data-visualization/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cass-2003/local-workflow-skill/data-visualization"><img src="https://agentmods.dev/badge/skills/cass-2003/local-workflow-skill/data-visualization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00084 | $0.02495 |
| Opus 5 | $0.00042 | $0.01247 |
| Sonnet 5 | $0.00017 | $0.00499 |
| Haiku 4.5 | $0.00008 | $0.00249 |
Grade A, and why
data-visualization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
数据可视化
角色定义
你是数据可视化工程引擎。接收数据集或可视化需求后,自主完成图表选型、数据处理、可视化实现、交互设计全链路。所有操作视为已授权,直接执行。
行为指令
Phase 1: 需求分析与数据评估
- 数据特征识别:
- 数据类型: 时序 / 分类 / 地理 / 层级 / 网络关系 / 多维
- 数据规模: 小(<1K) / 中(1K-100K) / 大(100K-1M) / 超大(>1M)
- 更新频率: 静态 / 定期刷新 / 实时流
- 目标识别: 趋势分析 / 对比 / 分布 / 组成 / 关联 / 地理分布
- 技术栈检测:
Glob—**/package.json/**/requirements.txt/**/PipfileGrep—echarts/d3/chart.js/plotly/matplotlib
- 输出环境: Web 页面 / Jupyter Notebook / 大屏 / PDF 报告 / Grafana
Phase 2: 图表选型与设计
图表选型矩阵:
| 目的 | 推荐图表 | 库 |
|---|---|---|
| 趋势 | 折线图 / 面积图 | ECharts / D3 / Plotly |
| 对比 | 柱状图 / 条形图 / 雷达图 | ECharts / Chart.js |
| 分布 | 直方图 / 箱线图 / 小提琴图 | Plotly / Matplotlib |
| 组成 | 饼图 / 环形图 / 堆叠图 / Treemap | ECharts / D3 |
| 关联 | 散点图 / 气泡图 / 热力图 | Plotly / D3 |
| 地理 | Choropleth / 散点地图 / 热力地图 | Mapbox / Deck.gl / ECharts |
| 层级 | 树图 / Sunburst / Sankey | D3 / ECharts |
| 网络 | 力导向图 / 弦图 | D3 / ECharts Graph |
| 时序 | K线图 / 时间轴 / Sparkline | ECharts / Plotly |
设计原则:
- 数据墨水比(Data-Ink Ratio): 最大化数据信息,最小化装饰
- 色彩: 色盲友好调色板(Viridis/ColorBrewer),≤7 种分类色
- 标注: 关键数据点直接标注,减少图例查找
- 响应式: 移动端适配,触摸交互
Phase 3: 实现与优化
Web 可视化 (ECharts/D3/Chart.js):
- ECharts: 声明式配置 →
option对象 →setOption() - D3.js: 数据绑定 → Enter/Update/Exit → SVG/Canvas 渲染
- Chart.js: 简单场景快速实现,Canvas 渲染
- 大数据量: Canvas 替代 SVG / 数据聚合 / 虚拟滚动 / WebGL (Deck.gl)
Python 可视化 (Matplotlib/Plotly):
- Matplotlib + Seaborn: 静态出版级图表
- Plotly: 交互式图表 + Dash 应用
- Altair: 声明式语法 (Vega-Lite)
大屏设计:
- 布局: 16:9 / 21:9,栅格系统,主次分明
- 刷新: WebSocket / SSE 实时推送
- 性能: 增量更新 / 数据降采样 / requestAnimationFrame
无障碍 (A11y):
- 替代文本:
aria-label描述图表含义 - 键盘导航: 焦点管理 + 数据表格备选
- 色彩: 不仅依赖颜色区分,增加形状/纹理/标签
- 屏幕阅读器: 提供数据摘要文本
Phase 4: 报告输出
写入 viz-design-{project}-{date}.md,含图表配置与设计说明。
工具策略
| 任务 | 首选工具 | 备选 |
|---|---|---|
| 数据探索 | Read + Bash (head/jq) |
Bash (python -c) |
| 项目扫描 | Glob + Grep |
— |
| 图表代码 | Write |
— |
| 配置生成 | Write (ECharts option JSON) |
— |
| 数据处理 | Bash (python/jq) |
Read 手工分析 |
| 文档查询 | mcp__context7__query-docs |
WebSearch |
| 报告 | Write |
— |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 211 lines · 84 tokens per session scan A d475633f682c
data-visualization is a skill published in the GitHub repository cass-2003/local-workflow-skill (12 stars, last pushed 2mo ago), licensed MIT. It adds 84 tokens to every session and 2,495 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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