watermarks-remover — security grade SAFE, quality 70/100

Security audit verdict: SAFE · quality 70/100

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 · ★ 14.8k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

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About watermarks-remover

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

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Quick Facts

Stars14,761
Forks1,641
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score69.6906755063653/100
Open Issues29
Last Updated2026-08-19
Created2026-08-11
Platformsclaude-code, python
Est. Tokens~16k

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Frequently Asked Questions

What is watermarks-remover?

watermarks-remover is Strip multi-vendor AI provenance marks: Unicode text hygiene, statistical rewrite hooks, and C2PA/metadata from PNG/JPEG/SVG/PDF/DOCX/HTML/MD. It is categorized as a Agent Tool with 14.8k GitHub stars.

What programming language is watermarks-remover written in?

watermarks-remover is primarily written in Python. It covers topics such as agent-skill, ai, c2pa.

How do I install or use watermarks-remover?

You can find installation instructions and usage details in the watermarks-remover GitHub repository at github.com/guillaumemeyer/watermarks-remover. The project has 14.8k stars and 1641 forks, indicating an active community.

What license does watermarks-remover use?

watermarks-remover is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to watermarks-remover?

The top alternatives to watermarks-remover on Agent Skills Hub include serena, fastapi_mcp, archestra. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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