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 ShadowAqueduct · Claude Skill · ★ 837
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is watermark-remover safe to install? View the security audit →
watermarks-remover Agent skill + stdlib Python service that strips multi-vendor AI provenance marks from text and files. For privacy and hygiene on content you own. The skill is a thin HTTP client — the agent host needs no Python. All work runs in the service. Author: ShadowAqueduct Latest release: v0.5.0 PNG, JPEG, WebP, AVIF, HEIC, BMP, GIF, TIFF, SVG, PDF, DOCX, XLSX, PP
| Stars | 837 |
| Forks | 79 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 67.3176437258062/100 |
| Open Issues | 8 |
| Last Updated | 2026-08-31 |
| Created | 2026-08-23 |
| Platforms | claude-code, cli, codex, python |
| Est. Tokens | ~15k |
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watermark-remover is Purge multi-vendor AI watermarks: clean Unicode text, apply statistical rewrite hooks, and clear C2PA plus metadata from PNG, JPEG, SVG, PDF, DOCX, HTML, and MD.. It is categorized as a Claude Skill with 837 GitHub stars.
watermark-remover is primarily written in Python. It covers topics such as claude-ai, claude-code, claude-code-plugin.
You can find installation instructions and usage details in the watermark-remover GitHub repository at github.com/ShadowAqueduct/watermark-remover. The project has 837 stars and 79 forks, indicating an active community.
watermark-remover is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to watermark-remover on Agent Skills Hub include claude-codex-settings, pro-workflow, awesome-llm-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: