Flagged: sudo usage. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by joeseesun · AI Tool · ★ 222
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is qiaomu-download safe to install? View the security audit →
qiaomu-download 对 Agent 说一句 “下载这个：URL”，它会识别页面里的视频、更新下载引擎、保存最高可用画质，并验证文件真的能播放。 不记命令，不找解析网站，不手动挑音视频流。复制链接，说“下载这个”，剩下的交给 Skill。 为什么值得安装 一句话触发：只需“下载这个 + URL”，不用特意说明它是视频。 多平台统一入口：微信视频号、YouTube、B站、X、抖音、TikTok、小红书等使用同一种表达。 自动跟进 yt-dlp：每个新任务检查官方 stable release，仅在有新版时按现有包管理器升级。 公开访问优先：先匿名解析；只有公开提取失败时才按需使用本机浏览器 Cookie。 下载后验真：用 检查视频流、音频流、时长、分辨率和文件大小。 保护已有文件：默认单链接、禁止播放列表扩张、禁止覆盖、重复任务加锁。 进度可见：持续显示下载、合并和验证进度，完成后返回可点击的绝对路径。 视频号完整内置：同一个安装包包含视频号预检、在线解析、本地捕获、下载、解密、编码验证和后端安装器，不需要另装 Skill。 微信和小红书更克制：不使用 Computer Use 或 UI 自动化操作微信、小红书客户端及其内嵌页面。 风控平台先提醒：遇到小红书、抖音、TikTok、Instagram、Facebook、微博等平台，先说明只做链接解析；登录、验证码和安全验证始终由用户手动完成。 支持哪些平台 平台能力来自当前安装的 extractor。站点会改版，因此“存在 extractor”代表可以动态探测，不代表任何链接永久可用。 普通视频、番剧等；高清、会
| Stars | 222 |
| Forks | 38 |
| Language | Python |
| Category | AI Tool |
| License | MIT |
| Quality Score | 59.0169683048194/100 |
| Last Updated | 2026-09-20 |
| Created | 2026-09-19 |
| Platforms | python |
| Est. Tokens | ~15k |
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qiaomu-download is 通用在线视频下载与媒体提取技能。用户提供 YouTube/youtu.be、B站/bilibili/b23.tv、X/Twitter、Vimeo、TikTok、Instagram、Facebook、Twitch、Reddit 或其他 yt-dlp 支持的视频链接，并要求下载/保存视频、提取 audio/MP3、下载字幕、查看视频信息或更新 yt-dlp 时使用。微信视频号 weixin.qq.co. It is categorized as a AI Tool with 222 GitHub stars.
qiaomu-download is primarily written in Python.
You can find installation instructions and usage details in the qiaomu-download GitHub repository at github.com/joeseesun/qiaomu-download. The project has 222 stars and 38 forks, indicating an active community.
qiaomu-download is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to qiaomu-download on Agent Skills Hub include pivotnacci, MedgeClaw, SciToolAgent. 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: