by BinaryFroggy · Codex Skill · ★ 63
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 63 |
| Forks | 3 |
| Language | Swift |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 58/100 |
| Last Updated | 2026-07-27 |
| Created | 2026-04-17 |
| Platforms | claude-code, cli, codex |
| Est. Tokens | ~16k |
简体中文 · English Hopet 一只常驻 macOS 桌面的 AI 宠物，用动画和气泡显示 Claude Code / Codex CLI 正在思考、执行工具、等待确认、请求权限或完成任务，并通过桌面宠物与灵动岛提醒你关注 agent 进展。 项目介绍 Hopet 是一只常驻 macOS 桌面的 AI 编程宠物，它把 Claude Code / Codex CLI 的会话状态变成可见、可感知的桌面反馈：思考、执行工具、等待确认、请求权限、完成或失败，都能通过宠物动画、气泡提示和灵动岛 / 顶部状态条表现出来。 它不是另一个聊天窗口，而是一个轻量的工作陪伴层，让开发者在写代码时不用频繁切回终端，也能直观看到 AI agent 当前在做什么、是否需要你介入，以及一次会话是否顺利推进。 Hopet 让本来隐藏在命令行里的 agent 生命周期变得更清楚、更亲近，也让长时间的 AI...
Looking for a Hopet alternative? If you're comparing Hopet with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
CodexBar 是一个 macOS 菜单栏应用，用于查看本机 Codex 账号状态、额度、Token 用量、详细统计信息
Cover your Mac's screen with a hotkey while AI agents keep running, even with the lid closed. The covered scre
Open-source status companion for your AI coding agents — Claude Code, Codex, Antigravity, Grok, and Cursor. Li
Keep Your Mac Awayke With the Lid Closed
Approve Claude Code from your Mac's notch. A native SwiftUI menu-bar app that surfaces Claude Code permission
Just another desktop client for OpenCode 2. Manage projects, sessions, parallel agents, requests, and changes
Explore other popular codex skill tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: