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 ShadowAqueduct/watermark-remover --skill clean-user-facing-textgit clone --depth 1 https://github.com/ShadowAqueduct/watermark-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/shadowaqueduct/watermark-remover/clean-user-facing-text)<a href="https://agentmods.dev/skills/shadowaqueduct/watermark-remover/clean-user-facing-text"><img src="https://agentmods.dev/badge/skills/shadowaqueduct/watermark-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/shadowaqueduct/watermark-remover/clean-user-facing-text"><img src="https://agentmods.dev/badge/skills/shadowaqueduct/watermark-remover/clean-user-facing-text.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector 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 | $0.00121 | $0.01089 |
| Opus 5 | $0.00060 | $0.00544 |
| Sonnet 5 | $0.00024 | $0.00218 |
| Haiku 4.5 | $0.00012 | $0.00109 |
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.
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clean user-facing text
Final hygiene pass on prose the user owns or is authorized to process. Unicode cleanup is deterministic. Statistical-watermark reduction is best-effort. Never claim a rewrite proves human authorship or is undetectable. Keep 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.
- 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
- keep required disclosures, uncertainty, and the writer's actual point of view
- Rewrite the remaining prose once:
- vary clause order, sentence boundaries, rhythm, connectors, and function words
- replace formulaic transitions and filler with direct, natural wording
- keep the concrete details and judgement that make the text recognisable as the writer's
- treat unusual grammar, repetition, directness, and phrasing as possible voice or accessibility choices; change them only when the user asks or when they create a clear reading problem
- preserve the requested language, tone, structure, and formatting; never translate unless asked
- for non-English text, use fluent constructions native to that language rather than English sentence patterns
- do not add or remove claims merely to increase variation
- For text artifacts or supplied text files, run the deterministic Unicode pass after rewriting.
- Return only the polished result unless the user asks for an audit or explanation.
For practical guidance on preserving a writer's voice and removing formulaic prose, read references/writing-in-your-voice.md whenever the user asks to retain or adjust voice.
What ships with it
7 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.
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 · 82 lines · 121 tokens per session scan A 559ce6d4774d
clean-user-facing-text is a skill published in the GitHub repository ShadowAqueduct/watermark-remover (835 stars, last pushed 17d ago), licensed MIT. It adds 121 tokens to every session and 1,089 once invoked, about $0.0006 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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