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 allenk · Agent Tool · ★ 3.0k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is GeminiWatermarkTool safe to install? View the security audit →
Gemini Watermark Tool ## 📌 Original Author Notice (Reverse Alpha Blending) I am the original author of GeminiWatermarkTool and the reverse alpha-blending restoration method used to remove the visible "Gemini" watermark while preserving image detail (Allen Kuo / allenk). This project achieves high-precision restoration by using my calibrated 48×48 and 96×96 Reverse-Alpha Masks to invert the blending equation. Since I published this work and these assets, many derivative tools (desktop apps, websites, browser extensions, etc.) have appeared using the same approach and/or directly reusing the masks produced by this project — because the method is deterministic and highly effective. ✅ MIT License reminder This project is released under the MIT Li
| Stars | 3,021 |
| Forks | 269 |
| Language | C++ |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 65.4634210827707/100 |
| Open Issues | 9 |
| Last Updated | 2026-07-28 |
| Created | 2025-12-12 |
| Platforms | cli, gemini |
| Est. Tokens | ~24k |
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GeminiWatermarkTool is VEO / Gemini 3.5 Nano Banana Pro watermark maintenance tool. It is categorized as a Agent Tool with 3.0k GitHub stars.
GeminiWatermarkTool is primarily written in C++. It covers topics such as cli-app, gemini, gui-application.
You can find installation instructions and usage details in the GeminiWatermarkTool GitHub repository at github.com/allenk/GeminiWatermarkTool. The project has 3.0k stars and 269 forks, indicating an active community.
GeminiWatermarkTool is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to GeminiWatermarkTool on Agent Skills Hub include StoryGen-Atelier, unity-mcp, bytebot. 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: