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 guillaumemeyer · Agent Tool · ★ 23.2k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is watermarks-remover safe to install? View the security audit →
watermarks-remover Agent skill + stdlib Python scripts to strip multi-vendor AI provenance marks from text and files — for privacy and hygiene on content you own. Vendors / ecosystems (class-level): Claude, Gemini / SynthID-Text, OpenAI provenance surfaces, open-LLM Kirchenbauer-style marks. Latest release: v0.2.0 Skill path: [](skills/remove-a
| Stars | 23,237 |
| Forks | 2,666 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 69.5326769865272/100 |
| Open Issues | 15 |
| Last Updated | 2026-10-02 |
| Created | 2026-08-11 |
| Platforms | claude-code, gemini, python |
| Est. Tokens | ~16k |
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watermarks-remover is A privacy-first app that strips AI watermarks from content you own.. It is categorized as a Agent Tool with 23.2k GitHub stars.
watermarks-remover is primarily written in Python. It covers topics such as agent-skill, ai, anthropic.
You can find installation instructions and usage details in the watermarks-remover GitHub repository at github.com/guillaumemeyer/watermarks-remover. The project has 23.2k stars and 2666 forks, indicating an active community.
watermarks-remover is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to watermarks-remover on Agent Skills Hub include LibreChat, repomix, lobehub. 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: