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 tiwe0 · MCP Server · ★ 117
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is GeoChat safe to install? View the security audit →
GeoChat Desktop 本地优先的 AI 数学可视化工作台。输入题目，GeoChat 自动构造、作图并解释。 English · 官网 · 下载 · 预览 · 快速开始 · 功能 · Star History 当前稳定版： · 官网： 预览 截图 视频 演示视频 1 无法播放时打开视频文件 演示视频 2 <video src="https://raw.githubusercontent.com/tiwe0/GeoCh
| Stars | 117 |
| Forks | 34 |
| Language | TypeScript |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 62.6544301278346/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-28 |
| Created | 2025-04-02 |
| Platforms | mcp, node |
| Est. Tokens | ~15k |
These tools work well together with GeoChat for enhanced workflows:
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GeoChat is Local-first AI math visualization desktop app: draw, analyze, and explain geometry with GeoGebra, Tauri, React, Bun, and native tool calling.. It is categorized as a MCP Server with 117 GitHub stars.
GeoChat is primarily written in TypeScript. It covers topics such as ai-sdk, artificial-intelligence, bun.
You can find installation instructions and usage details in the GeoChat GitHub repository at github.com/tiwe0/GeoChat. The project has 117 stars and 34 forks, indicating an active community.
GeoChat is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to GeoChat on Agent Skills Hub include neurolink, gno, CoWork-OS. 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: