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 ishara-madu · Agent Tool · ★ 58
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is gemini-watermark-remover safe to install? View the security audit →
✨ Free Gemini & Veo 3 AI Watermark Remover Instantly remove Google Gemini and Veo 3 AI watermarks from images and videos with 100% mathematical precision. Completely free, private, and runs 100% client-side in your web browser. 🌐 Live Website 👉 Try it online: https://ishara-madu.github.io/gemini-watermark-remover/ 💡 Why This Tool? Most AI watermark removers use generative AI inpainting, which hallucinates missing pixels and blurs the background. Google Gem
| Stars | 58 |
| Forks | 15 |
| Language | JavaScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 63.4294071649755/100 |
| Last Updated | 2026-09-19 |
| Created | 2026-08-05 |
| Platforms | browser, cli, gemini, node |
| Est. Tokens | ~14k |
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gemini-watermark-remover is Free, 100% client-side tool to remove Google Gemini and Veo 3 AI watermarks from images and videos using mathematical alpha unblending with zero quality loss.. It is categorized as a Agent Tool with 58 GitHub stars.
gemini-watermark-remover is primarily written in JavaScript. It covers topics such as ai-watermark-remover, alpha-unblending, canvas-api.
You can find installation instructions and usage details in the gemini-watermark-remover GitHub repository at github.com/ishara-madu/gemini-watermark-remover. The project has 58 stars and 15 forks, indicating an active community.
gemini-watermark-remover is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to gemini-watermark-remover on Agent Skills Hub include gemini-watermark-remover, gemini-watermark-and-synthid-remover, gemini-antigravity-cli. 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: