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Clean and finalize authorized natural-language text intended for readers by auditing suspicious invisible Unicode and rewriting prose while preserving facts, meaning, and the writer's voice. Use when
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add guillaumemeyer/watermarks-remover --skill clean-user-facing-text -a cursor -y
Clean and finalize authorized natural-language text intended for readers by auditing suspicious invisible Unicode and rewriting prose while preserving 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 or instruction file 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.
Clean and finalize authorized natural-language text intended for readers by auditing suspicious invisible Unicode and rewriting prose while preserving 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 or instruction file 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.
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.
Identify the prose that readers will see.
Protect non-prose spans:
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 is high; for low or medium, 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:
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:
PYTHON "$SCRIPTS/clean_text.py" INPUT -o OUTPUT --stats --no-normalize-spaces
Layer B — rewrite once. Rewrite the remaining prose once, applying the detector levers (see Detector levers) in order:
Layer A again. Run the deterministic Unicode pass on the rewritten result to catch any artifacts the rewrite introduced (smart quotes, em dashes, homoglyphs):
PYTHON "$SCRIPTS/clean_text.py" OUTPUT -o FINAL --stats --no-normalize-spaces
Measure after. Score the rewritten text the same way. Report scores and confidence levels when available; otherwise report status: insufficient_length. A lower after-score means the measurable signals moved; it is not a verdict from any detector, and it never overrides the fact and voice rules above.
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.
For what detectors really measure and which claims are legitimate, read references/detectors.md.
scripts/inspect_text.py --stylometry (or the standalone scripts/score_stylometry.py,
which also accepts --explain) runs the zero-LLM estimator vendored from the service
pipeline: sentence-length burstiness (coefficient of variation), weighted AI-cadence
phrase density per 100 words, lexical diversity (MATTR), and a dampened composite
score from 0 to 1. Exit code 1 means the score is at or above the threshold
(default 0.65). The report also carries a density_tier (low / medium /
high, or uncalibrated when not scored) that re-labels where the score sits
so the rewrite pass engages only for high. inspect_text.py --audit produces
the same scoring plus the detect-only flagged-span list but never rewrites.
Under 30 words the estimator reports status: insufficient_length instead of a
score. Nothing here calls the network; the skill stays self-contained.
Limits: the estimator is calibrated for the statistical detector family (perplexity and burstiness style signals). It is not the output of trained neural classifiers such as GPTZero, Turnitin, Originality, or Pangram, it does not detect secret-key watermarks, and it says nothing about authenticity. A low after-score means the measured signals moved; it does not prove the text reads as human or that any particular detector would accept it. When the user asks for an audit, report the numbers as a gauge, not as a verdict (see Reporting).
Resolve SCRIPTS to this skill's scripts/ directory.
Use the available Python 3 launcher for the platform. Replace PYTHON below
with python3 on most macOS/Linux systems, py on Windows, or another verified
Python 3 command.
This skill is self-contained and runs its vendored scripts directly; it has no service or network dependency. Deterministic Layer A cleaning below is invoked by script, intentionally, so the skill works where the HTTP service is absent.
Inspect first when editing an existing file (include --stylometry to record
the before score):
PYTHON "$SCRIPTS/inspect_text.py" --stylometry --json INPUT
PYTHON "$SCRIPTS/clean_text.py" INPUT -o OUTPUT --stats --no-normalize-spaces
PYTHON "$SCRIPTS/inspect_text.py" --stylometry --json OUTPUT
Use - for stdin. Prefer a new *.cleaned.* output unless the user explicitly requests in-place editing.
Use --no-normalize-spaces by default so NBSP, narrow no-break spaces, figure spaces, and CJK ideographic spaces retain their layout semantics. Normalize spaces only when the user requests it.
Do not use --aggressive-homoglyphs, --nfkc, or --strip-emoji-glue unless the user requests aggressive normalization and accepts possible changes to multilingual text, emoji, directionality, or typography.
The scripts support plain text, source text, Markdown, and HTML source as text. For mixed Markdown or HTML, inspect hit positions first. If a hit falls inside protected code, attributes, or another non-prose span, do not run whole-file cleanup; clean only the prose segments or leave that hit unchanged. Do not pass binary containers such as PDF, DOCX, images, or archives.
For a chat-only response that is not written to a file, perform the rewrite workflow directly. Do not claim that the chat response received a deterministic post-send Unicode filter.
Statistical detectors score probability patterns: AI prose is too predictable (low
perplexity), too even (low burstiness), and too full of stock phrases. The levers
below target those signals, most effective first. Levers 1, 2, and 6 are
deterministic or near-deterministic; 3 to 5 are aims, not guarantees. Engage the
rewrite levers only when the measure-before density_tier is high; for low
or medium, the measurable AI-density signals are weak, so verify and otherwise
leave the text unchanged. The pass ordering below follows the pattern
catalogs in references/detectors.md.
clean_text.py) before anything else: invisible characters and homoglyphs are
mechanical markers that hurt with every detector family and are cheap to remove.references/writing-in-your-voice.md). Never invent a fact to raise
variance; a lower score with a fabricated detail is still a failed rewrite.Honest caveat: these levers are aimed at statistical detectors. Trained neural
classifiers are adversarially trained against paraphrase-style edits (see
references/detectors.md); against those, the only robust lever is matching a real
human distribution, and even that cannot be guaranteed.
Presets tune how hard to apply the levers and how much personality to allow. They never override the fact rules (fiction excepted, see step 3 above) and they are not guarantees of any detector result.
| Preset | Personality | Rhythm and phrasing | What to eliminate |
|---|---|---|---|
| General prose (default) | Author's voice first, no injected stance | Mild variation, natural connectors | Stock AI vocabulary, uniform cadence |
| Essay / blog | Stance, asides, mixed feelings welcome | Strong length variation, uneven rhythm | Significance hype, aphorism formulas, rule of three |
| Technical / documentation | Neutral, precise | Moderate variation, short declaratives | Promo language, em dashes, bolded mini-headers; keep code and identifiers intact |
| Academic / professional | Formal, evidence-first | Restrained variation, controlled hedging | Over-claiming verbs, novelty padding, citation dumps; keep required discipline |
| Business / product copy | Plain claims, concrete value | Direct sentences | "Seamless", "empower", vague benefits, rule of three, required disclaimers kept |
| Fiction | Invented detail allowed | Variation to fit the narrator | Uniform cadence, editorial clichés; preserve dialect and quirks |
For procedures, runbooks, errors, and other engineer-facing text, the user may request a plain-language sub-mode: short common words, one instruction per sentence, imperative verbs for steps, one meaning per term, and no marketing adjectives or unbounded hedging. This sub-mode is a clarity floor, not a new personality: it strips voice deliberately, keeps every claim and requirement, and is not a detector-evasion tool. Use the voice-preserving presets above for essays, posts, and personal prose instead.
When prose and code are mixed, rewrite prose only. Never rename variables, alter string literals, reformat code, or change executable output as part of this skill. If a Markdown or HTML file contains executable snippets, preserve those spans byte-for-byte whenever practical.
When the user asks for an audit, distinguish:
For technical background, read references/watermark-notes.md. For misuse or disclosure questions, read references/responsible-use.md.