watermarks-remover is a privacy-focused application that removes AI provenance marks from text and files owned by the user, including hidden text markers and metadata in many document, image, audio, and video formats. Its agent skill sends requests to a separate Python service, allowing supported coding agents to use the removal tools without installing Python on the agent host.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/guillaumemeyer/watermarks-removerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/guillaumemeyer/watermarks-remover/clean-user-facing-text)<a href="https://agentmods.dev/rules/guillaumemeyer/watermarks-remover/clean-user-facing-text"><img src="https://agentmods.dev/badge/rules/guillaumemeyer/watermarks-remover/clean-user-facing-text/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/guillaumemeyer/watermarks-remover/clean-user-facing-text"><img src="https://agentmods.dev/badge/rules/guillaumemeyer/watermarks-remover/clean-user-facing-text.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00174 | $0.00174 |
| Opus 5 | $0.00087 | $0.00087 |
| Sonnet 5 | $0.00035 | $0.00035 |
| Haiku 4.5 | $0.00017 | $0.00017 |
Grade A, and why
clean-user-facing-text scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Clean user-facing text
Before finalizing substantial natural-language content intended for readers, use the clean-user-facing-text skill.
- Apply this only to content the user owns or is authorized to process, and preserve required disclosures.
- Apply it to articles, papers, reports, documentation, emails, product copy, UI text, Markdown prose, and HTML prose.
- Preserve facts, numbers, names, citations, requirements, language, tone, and formatting.
- Do not modify fenced or inline code, commands, paths, URLs, identifiers, APIs, formulas, or verbatim quotations.
- Treat statistical-watermark reduction as best-effort; never claim the result is certified undetectable or proves human authorship.
- Skip the skill for code-only tasks.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 16 lines · 174 tokens per session scan A 4e9401576985
clean-user-facing-text is a cursor rule published in the GitHub repository guillaumemeyer/watermarks-remover (21,459 stars, last pushed yesterday), licensed MIT. It adds 174 tokens to every session, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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