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 NanmiCoder · AI Tool · ★ 95
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is jev-arena safe to install? View the security audit →
Jev Arena 给两个模型同一批评论，直观看速度，逐条比结果。 导入 CSV / Excel，填写两侧 Key，即可实时对比、保存回放，并离线生成两份研究报告。 Jev 模型介绍与原理 · 快速启动 · 模型配置 · 评论格式 · 生成报告 · 给 AI Agent 的指南 想了解 Jev？阅读 System One：它不生成文字，它返回判断，了解 Choice、Score、Noul 三种判断原语、概率与置信度，以及分类、路由和批量打标等应用。介绍页源码位于 ，推送到 后由 GitHub Actions 自动发布。 一万条评论历史运行的前 20 秒，1× 回放，无加速。 演示来源 · 静态关键帧 默认对比 Jev（OpenRouter）与 DeepSeek，两侧也可独立配置其他 OpenAI 兼容聊天模型。启动自带 20 条合成评论，点击「开始对决」才会调用模型。上图的速度差异仅代表该次运行。 演示数据与完整报告 一万条评论与分析上下文 · 可直接阅读的比较报告 · Jev 完整报告 · DeepSeek 完整报告 资料包含原始 CSV、两侧逐条 JSONL 标签、机器可读统计与完整报告视图，方便你或 AI Agent 复核。HTML 可下载后打开；启动项目后也能直接在报告页切换阅读，无需 Key、无需重跑。 快速启动 需要 Node.js 22 或更高版本，或使用下方 Docker 方式。 打开 本地对决场，展开「模型设置与评论导入」： 使用默认 Jev / DeepSeek 时，只需填入 OpenRouter Key 和 DeepSeek Key，点击「保存模型配置」。其他模型需
| Stars | 95 |
| Forks | 9 |
| Language | JavaScript |
| Category | AI Tool |
| License | MIT |
| Quality Score | 58.464698498655/100 |
| Last Updated | 2026-09-20 |
| Created | 2026-09-19 |
| Platforms | node |
| Est. Tokens | ~14k |
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jev-arena is Jev 模型介绍与实测：通过 Choice / Score / Noul 将自然语言转为带类型的判断与概率，用于分类、评分和路由；支持与 DeepSeek 等模型对比评论打标、速度与结果，含 CSV/Excel 导入、原速回放与离线报告。. It is categorized as a AI Tool with 95 GitHub stars.
jev-arena is primarily written in JavaScript.
You can find installation instructions and usage details in the jev-arena GitHub repository at github.com/NanmiCoder/jev-arena. The project has 95 stars and 9 forks, indicating an active community.
jev-arena is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to jev-arena on Agent Skills Hub include SciToolAgent, redesigned-pancake, claude-code-voice-skill. 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: