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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QMPrompter / 乔木提词器 QMPrompter is a small, personal iOS teleprompter app for speaking to camera. It keeps the script on screen, uses the front camera as a live preview background, and can follow your speech so the current line stays near a comfortable reading position. 乔木提词器是一款自用 iOS 提词器。它把文稿显示在摄像头预览之上，适合对着镜头练习表达、录制口播或直播前试讲。当前版本专注于提词和预览，不录制视频。 Status: early self-use version. The app is usable on a real iPhone, but it is not an App Store product yet. 状态：早期自用版本。真机可用，但还不是 App Store 发布版。 Highlights / 功能亮点 Script library: create, edit, save, search, and delete scripts. Manual input and AI-assisted script generation. AI providers: DeepSeek, OpenAI-compatible APIs, and Claude/Anthropic-compatible APIs. Custom Base URL and model selection, with quick model presets and custom model names. Remote model discovery from compatible endpoints, with searchable model selection. Large voice input button for AI prompt dictation. Front camera live preview behind the script. Full-screen teleprompter with speech-following mode by default. Speed mode with play, pause, rewind, forward, reset, and progress controls. Manual vertical dragging to adjust the reading position.
| Stars | 97 |
| Forks | 25 |
| Language | Swift |
| Category | AI Tool |
| Quality Score | 54.845716480524/100 |
| Open Issues | 1 |
| Last Updated | 2026-06-10 |
| Created | 2026-06-09 |
| Est. Tokens | ~17k |
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QMPrompter is an open-source ai tool by joeseesun with 97 GitHub stars.
QMPrompter is primarily written in Swift.
You can find installation instructions and usage details in the QMPrompter GitHub repository at github.com/joeseesun/QMPrompter. The project has 97 stars and 25 forks, indicating an active community.
The top alternatives to QMPrompter on Agent Skills Hub include SciToolAgent, redesigned-pancake, claude-code-voice-skill. 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: