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 op7418 · Claude Skill · ★ 144
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is guizang-sports-skill safe to install? View the security audit →
Guizang Sports Skill · FIT/KML 运动分析与 3D 路线故事 一个面向 Claude Code、Codex 等本地 Agent 环境的运动轨迹 Skill。把一个或多个 / 文件交给 Agent，它会分析骑行、跑步或徒步数据，生成有依据的结论与建议，并自动打开本地可视化报告页。需要分享时，还能继续制作带 3D 路线、数据卡片和沿途照片的 PNG / H.264 MP4。 Ride Relief 是这个 Skill 自带的本地报告网页，不需要另装软件。 它只监听本机地址，轨迹和分析通过系统临时目录交给页面，不会复制进仓库。 30 秒开始 安装后直接对 Agent 说： 如果还没有收到轨迹文件，Agent 会请你把文件拖进对话，或直接告诉它本地路径（例如 ）。收到后会自动检查运行依赖、完成分析并打开报告页，不需要你手动安装网页依赖或启动服务。 没有现成文件也可以立即体验： 仓库内的 是完全合成的闭环骑行，不包含真实运动者或住址数据；坐标仅供界面演示，不代表实际可骑行路线。 也可以把需求和文件一起发过去： text 分析这份 FIT，重点看看踏频、爬坡和数据质量。 把这几次骑行做一个综合分析，找出最长、爬升最多和传感器最完整的一次。 这是两步路导出的徒步 KML，分析路线强度和主要爬坡。 把最长的那次做成一个 9:16 的 3D 路线演示视频。
| Stars | 144 |
| Forks | 11 |
| Language | JavaScript |
| Category | Claude Skill |
| License | AGPL-3.0 |
| Quality Score | 65.7847236009202/100 |
| Last Updated | 2026-08-09 |
| Created | 2026-08-09 |
| Platforms | claude-code, cli, codex, node |
| Est. Tokens | ~14k |
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guizang-sports-skill is 🚴 Claude Code / Codex skill for FIT & KML sports analysis — cycling, running, hiking, sensor and grade insights, local 3D route reports, PNG & H.264 MP4 exports. 运动轨迹分析与 3D 路线故事. It is categorized as a Claude Skill with 144 GitHub stars.
guizang-sports-skill is primarily written in JavaScript. It covers topics such as activity-tracker, agent-skill, ai-agent.
You can find installation instructions and usage details in the guizang-sports-skill GitHub repository at github.com/op7418/guizang-sports-skill. The project has 144 stars and 11 forks, indicating an active community.
guizang-sports-skill is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to guizang-sports-skill on Agent Skills Hub include compose-skill, creative-director-skill, hypernovum. 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: