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.
npx skills add guillaumemeyer/watermarks-remover --skill clean-user-facing-textgit 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/skills/guillaumemeyer/watermarks-remover/clean-user-facing-text)<a href="https://agentmods.dev/skills/guillaumemeyer/watermarks-remover/clean-user-facing-text"><img src="https://agentmods.dev/badge/skills/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/skills/guillaumemeyer/watermarks-remover/clean-user-facing-text"><img src="https://agentmods.dev/badge/skills/guillaumemeyer/watermarks-remover/clean-user-facing-text.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
- Socket pass
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 | $0.00123 | $0.03070 |
| Opus 5.5 | $0.00049 | $0.01228 |
| Sonnet 5.5 | $0.00025 | $0.00614 |
| Haiku 4.5 | $0.00012 | $0.00307 |
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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clean user-facing text
Apply a final text-hygiene pass to prose the user owns or is authorized to process. Treat Unicode cleanup as deterministic and statistical-watermark reduction as best-effort; never claim that a rewrite proves human authorship or is undetectable. Preserve required academic, legal, platform, and regulatory disclosures.
Workflow
-
Identify the prose that readers will see.
-
Protect non-prose spans:
- fenced and inline code
- commands, paths, URLs, identifiers, API names, and exact values
- formulas, citations, and text the user asks to quote verbatim
-
Preserve every claim, fact, number, name, citation, and requirement. Never invent a detail, name, number, quote, or source to make the prose easier to write or more varied: if a fact is missing, flag the gap rather than fill it. The rewrite may sharpen, compress, or reorder, but it may not add or remove claims.
-
Measure before. Inspect and score the input with the vendored zero-LLM stylometry estimator (see Scoring) and record the score. Read the report's
density_tier: rewrite only when it ishigh; forlowormedium, verify the text and otherwise leave the text unchanged. For a flag-only audit that never rewrites, use--audit:PYTHON "$SCRIPTS/inspect_text.py" --stylometry --json INPUT PYTHON "$SCRIPTS/inspect_text.py" --audit INPUT # detect-only: lists flagged spans, no rewrite -
Establish the writing brief before changing prose:
- use a voice sample only when the user owns it or is authorised to use it; don't imitate another named person
- when there is no sample, make the prose clear and natural without pretending to imitate a particular person
- never inject a voice the source lacks: no fake first person ("I've seen this"), invented specifics, forced contrarianism, performed candor, or added stance and personality. Preserve the writer's deliberate rough edges and domain terms rather than scrubbing them
- keep required disclosures, uncertainty, and the writer's actual point of view
- pick the voice and domain preset the text fits (see Voice and domain presets); the default is general prose
-
Layer A — strip artifacts first. For text artifacts or supplied text files, run the deterministic Unicode pass before rewriting, so the rewrite operates on clean, marker-free text:
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/detectors.md 8.1 KB
- references/responsible-use.md 536 B
- references/watermark-notes.md 1.2 KB
- references/writing-in-your-voice.md 2.5 KB
- scripts/clean_text.py 3.0 KB runs code
- scripts/common.py 21 KB runs code
- scripts/inspect_text.py 4.4 KB runs code
- scripts/score_stylometry.py 19 KB runs code
- scripts/text_unicode.py 25 KB runs code
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.
- 2mo ago First seen · 192 lines · 123 tokens per session scan A 07ce27ad6e3c
clean-user-facing-text is a skill published in the GitHub repository guillaumemeyer/watermarks-remover (26,309 stars, last pushed 2d ago), licensed MIT. It adds 123 tokens to every session and 3,070 once invoked, about $0.0005 per session on Opus 5.5. 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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