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 youtube-sponsor-detection safe to install? View the security audit →
Sponsor Skip Skips sponsor reads on YouTube. A Chrome extension finds each read with Jev (TypeSafe's System One model) and jumps past it while you watch; a small web app does the same for a pasted link. Code owns every timestamp: Jev only ever names a transcript line or answers yes/no about what it heard. You need a TypeSafe API key. Smart and Listen modes also need a Deepgram key for speech-to-text. Chrome extension Clone this repo. Open , turn on Developer mode, click Load unpacked and pick the folder. Click the extension's icon and paste your TypeSafe key. Transcript mode works from there; add a Deepgram key to try the Smart or Listen modes. Open any YouTube video. A panel appears bottom-right with the reads found, a Skip button per read, the auto-skip toggle, the running cost and, in the audio modes, what is being heard and a log of every decision. Modes The transcript finds each read and its end; the audio is listened to only around each read, and once Jev agrees a read is playin
| Stars | 75 |
| Forks | 11 |
| Language | JavaScript |
| Category | AI Tool |
| Quality Score | 57.5181039945109/100 |
| Last Updated | 2026-09-18 |
| Created | 2026-09-17 |
| Platforms | node |
| Est. Tokens | ~15k |
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youtube-sponsor-detection is Detect youtube sponsor segment with live audio and transcript powered by Jev. It is categorized as a AI Tool with 75 GitHub stars.
youtube-sponsor-detection is primarily written in JavaScript.
You can find installation instructions and usage details in the youtube-sponsor-detection GitHub repository at github.com/trungdq88/youtube-sponsor-detection. The project has 75 stars and 11 forks, indicating an active community.
The top alternatives to youtube-sponsor-detection on Agent Skills Hub include redesigned-pancake, claude-code-voice-skill, maui-skills. 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: