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 Devin-AXIS · Codex Skill · ★ 253
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is jev-dsh-decision safe to install? View the security audit →
Jev DSH 决策引擎 为 Agent Harness 提供结构化决策能力。 将 TypeSafe AI 的 Jev 决策能力接入你的 Agent：帮助选择工具、Skill 和任务负责人，评估产出质量，并以结构化结果返回判断与概率。Jev 负责辅助决策，原 Agent 继续负责规划和执行。 提供 DeepSeek Harness 原生插件和 iPolloWork 导入插件两个安装入口；在 iPolloWork 中可供 OpenCode、DeepSeek Harness、Codex Harness 使用。同一份决策核心，不修改引擎源码，不接管主 Agent。 快速开始：安装到 DeepSeek Harness · 安装到 iPolloWork 支持与接入方式 OpenCode 和 Codex Harness 当前支持的是 iPolloWork 内的接入方式，本仓库尚未提供它们各自的独立原生安装包。其他 Harness 可以基于共享决策核心开发适配器，不代表已经自动兼容。 核心能力 根据任务，在当前真实可用且获准使用的工具、Skill、Agent 之间推荐选择。 按明确标准评分、判断是否需要某项能力；独立问题可以一次批量评估。 返回选择、概率、置信度与用量；不执行推荐动作，也不自动切换模型或路由所有请求。 提供本地配置检查和真实连接测试。连接测试会产生少量 TypeSafe API 用量。 Jev 是 TypeSafe AI 的结构化决策模型，不是聊天模
| Stars | 253 |
| Forks | 83 |
| Language | JavaScript |
| Category | Codex Skill |
| Quality Score | 61.6889246637747/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-20 |
| Created | 2026-09-20 |
| Platforms | codex, node |
| Est. Tokens | ~5k |
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jev-dsh-decision is Jev DSH 决策引擎｜面向 Agent Harness 的结构化决策插件。原生支持 DeepSeek Harness，通过 iPolloWork 支持 OpenCode、Codex Harness。. It is categorized as a Codex Skill with 253 GitHub stars.
jev-dsh-decision is primarily written in JavaScript. It covers topics such as agent-harness, ai-agents, apollo-plugin.
You can find installation instructions and usage details in the jev-dsh-decision GitHub repository at github.com/Devin-AXIS/jev-dsh-decision. The project has 253 stars and 83 forks, indicating an active community.
The top alternatives to jev-dsh-decision on Agent Skills Hub include wesight, graph-memory, modsearch. 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: