clean-user-facing-text

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Audit and finalize authorized natural-language text meant for readers: strip suspicious invisible Unicode, then rewrite prose while keeping facts, meaning, and the writer's voice. Use when the user asks to clean, humanize, polish, or finalize articles, manuscripts, reports, documentation, emails, product copy, UI text, Markdown, or HTML prose, or when a project rule explicitly requires this workflow. Don't use for code-only tasks or undisclosed authorship evasion; leave code, commands, identifiers, paths, APIs, formulas, citations, required disclosures, and verbatim quotations unchanged.

Data & Documents 841 stars 80 forks Updated 1 weeks ago MIT

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Quality Score: 90/100

Stars 20%
97
Recency 20%
90
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# 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 1. Identify the prose that readers will see. 2. 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 3. Preserve every claim, fact, number, name, citation, and requirement. 4. 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 5. 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 req...

Details

Author
ShadowAqueduct
Repository
ShadowAqueduct/watermark-remover
Created
2 weeks ago
Last Updated
1 weeks ago
Language
Python
License
MIT

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