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 →
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🔒 Is qmreader-ios safe to install? View the security audit →
QMReader iOS 中文 | English 专注中文长文、翻译与乔木改写的原生 SwiftUI 阅读器。 A native SwiftUI reader for Chinese long-form reading, translation, and Qiaomu rewrites. 已验证： ；Xcode 26.2 模拟器构建、Apple Development 真机安装与 App Store Distribution Archive/IPA 导出通过。App Store 提交正在进行中。 这是什么 QMReader iOS 是 QMReader 的轻量原生客户端。它把 RSS 文章、中文翻译和“乔木改写”放进一个安静、快速、可离线回看的阅读界面，重点解决三件事： 先看到可读的中文改写，而不是先闪出英文原文。 用原生导航、缓存、下拉刷新和频道历史缩短等待。 用真正会切换的中文字体、行距、页边距和纸面背景改善长文阅读。 核心能力 | 原生频道
| Stars | 76 |
| Forks | 6 |
| Language | Swift |
| Category | AI Tool |
| License | MIT |
| Quality Score | 65.72444198133/100 |
| Last Updated | 2026-09-07 |
| Created | 2026-08-23 |
| Est. Tokens | ~16k |
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qmreader-ios is 专注中文长文、翻译与乔木改写的原生 iOS 阅读器 | Native iOS reader for Chinese long-form reading, translation, and Qiaomu rewrites.. It is categorized as a AI Tool with 76 GitHub stars.
qmreader-ios is primarily written in Swift. It covers topics such as chinese, ios, open-source.
You can find installation instructions and usage details in the qmreader-ios GitHub repository at github.com/joeseesun/qmreader-ios. The project has 76 stars and 6 forks, indicating an active community.
qmreader-ios is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to qmreader-ios on Agent Skills Hub include ContextKit, awesome-ios-ai, gemini-rss-app. 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: