No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by Wing900 · Agent Tool · ★ 52
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is PlotKityCat safe to install? View the security audit →
PlotKityCat 一款专为数学老师打造的 AI-native 可视化教学工具。 简介 PlotKityCat 是一个面向数学教学场景的 AI-native 可视化工具。它基于 Matplotlib 执行绘图代码，支持自然语言生成可视化，并以便携式 runtime 支撑课堂演示与离线分发。 视频介绍 https://github.com/user-attachments/assets/df8167a7-d1e9-4f6a-a42d-de15596a4456 设计原则 开源：以可审查、可扩展的技术栈承载教学工具。 美感：避免传统数学软件沉闷的视觉体验。 AI 原生：让老师通过自然语言驱动可视化生成，而不是先学编程。 功能特性 AI 绘图：通过自然语言描述数学概念，由 AI 生成 Matplotlib 绘图代码。 笔记系统：集成 Markdown 与 LaTeX 公式渲染，绑定代码，看到可视化的结果，更看到可视化的设计。 便携运行：依赖便携 Python runtime，适合 U 盘和教室环境分发。 场景包导入导出：支持 场景包的交换与复用。 技术栈 前端: Vue 3, TypeScript, Vite 后端: Go, Wails Framework 运行时: WinPython (NumPy, Matplotlib, SciPy, PyQt5) AI 接口: OpenAI API / 自定义兼容接口 快速开始 下载便携版压缩包。 配置 AI 服务商 API Key。 启动应用并新建场景。 在笔记区输入描述后运行可视化或可视化设计。 开发入口 Windows Go
| Stars | 52 |
| Forks | 11 |
| Language | TypeScript |
| Category | Agent Tool |
| License | GPL-3.0 |
| Quality Score | 61.2581332286705/100 |
| Open Issues | 2 |
| Last Updated | 2026-08-08 |
| Created | 2026-05-15 |
| Platforms | node |
| Est. Tokens | ~14k |
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PlotKityCat is PlotKityCat 是一个开源的数学可视化工具，支持运行 Matplotlib 代码并生成交互式图形。集成 AI 功能，支持通过自然语言提示词生成绘图代码。软件采用便携式设计，支持优盘即插即用，方便在教室等不同环境下快速部署与演示。. It is categorized as a Agent Tool with 52 GitHub stars.
PlotKityCat is primarily written in TypeScript. It covers topics such as ai, ai-tools, math.
You can find installation instructions and usage details in the PlotKityCat GitHub repository at github.com/Wing900/PlotKityCat. The project has 52 stars and 11 forks, indicating an active community.
PlotKityCat is released under the GPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to PlotKityCat on Agent Skills Hub include toolsdk-mcp-registry, claude-historian-mcp, arbor. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: