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 jiawei248 · Claude Skill · ★ 61
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is dear-skill safe to install? View the security audit →
dear 中文 · English 中文 "亲爱的 ，我做了一份小东西想给你。" dear 是一个给 Claude Code 用的礼物 skill。它会把每一次分享欲都变成一份有仪式感的电子礼物：一张有趣的照片、一段 TA 可能会喜欢的音乐、一句突然想起的话，都可以长成一张图、一个可以打开的小网页，或者一封只属于你们的信。 在生日、纪念日、母亲节、毕业、重逢，或者任何你想认真表达的时刻，dear 也可以作为实体礼物之外的另一层惊喜：不是替代那束花、那顿饭、那份礼物，而是把你们之间具体的记忆、玩笑、语气和心意做成一个可以被打开的体验。 你可以直接指定 H5、图片或文字，也可以把素材交给 dear，让它根据你们的关系和这一次想表达的情绪来提案。 它能做什么 · 快速开始 · 三种触发方式 · 模板模式 · 它怎么做礼物 · 安装 · 仓库结构 它能做什么 如果你已经知道想要什么形式，可以直接说；如果还没有想好，dear 会先看素材里最有生命力的部分，再决定什么形式最能把这份心意送出去。 快速开始 在 Claude Code 输入 把照片、聊天截图、朋友圈截图、文字片段或文件夹路径发进来 补充你想给谁、为什么想送、希望 TA 收到时有什么感觉 让 dear 把这份素材整理成一个可以交付的礼物 素材很多时，可以把它们放进一个文件夹，再把路径发给 。 三种触发方式 每种方式对应一种“我现在突然想给 TA 做点什么”的状态。 拖素材进聊天 先输入： 然后直接把照片、聊天截图、朋友圈 / 小红书 / Instagram 截图拖进 Claude Code。dear 会把这些图片当作礼物素材来读，再追问真正影响创意方向的那一两件事。 支持的素材：照片、截图（朋友圈/小红书/Instagram/聊天记录都行）、文字片段、文字笔记、Mac 微信导出的聊天记录（ 或 ）、PDF。音频和视频只读文件名。 扔一个文件夹 如果素材已经攒成一批文件，把它们放进桌面上的一个文件夹： dear 会先看文件列表，再读取里面能用的图片、文字、聊天记录和 PDF，从里面找可以做成礼物的细节。 一句话起头 如果你手头没有素材，只是心里有个画面： bash /dear 给我朋友小A做一份礼物 — TA最近开始学陶艺 /dear 明天是我
| Stars | 61 |
| Forks | 5 |
| Language | HTML |
| Category | Claude Skill |
| Quality Score | 62.5473628186626/100 |
| Last Updated | 2026-05-24 |
| Created | 2026-05-08 |
| Platforms | claude-code, codex |
| Est. Tokens | ~3015k |
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dear-skill is Codex/Claude skill for dear interactive gift templates. It is categorized as a Claude Skill with 61 GitHub stars.
dear-skill is primarily written in HTML.
You can find installation instructions and usage details in the dear-skill GitHub repository at github.com/jiawei248/dear-skill. The project has 61 stars and 5 forks, indicating an active community.
The top alternatives to dear-skill on Agent Skills Hub include skyll. 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: