human-voice

Make AI-drafted docs read like a person wrote them, without changing a single fact.

human-voice is a Claude Code skill that rewrites or generates prose so it doesn't read as AI-written. Most "humanizer" tools swap a few words and call it done. The text still reads like a machine, because the giveaways aren't mostly lexical. They're structural (em-dash overuse, relentless rule-of-three, bold-bullet listicles, sentences that are all the same length) and substantive (paragraphs that say nothing, a survey where a verdict belongs, invented specifics). This skill fixes those first and treats word choice as the last and shallowest pass.

See the difference

Same claim, two voices. The scores come from the bundled linter — run it on both yourself.

BEFORE   score 354 · strong-tell · 16 tells
─────────────────────────────────────────────────────────────────
In today's fast-paced digital landscape, leveraging cutting-edge
solutions is crucial for success. Our robust, scalable, and seamless
platform empowers teams to delve into actionable insights, unlock
their full potential, and move the needle. It's not just a tool, it's
a game-changer that stands as a testament to innovation.

AFTER    score 0 · clean
─────────────────────────────────────────────────────────────────
Your team's notes live in six different tools, and the context dies
with each one. We put the conversation, the files, and the decision
in one place. The first thing you'll notice is small: you stop asking
"where did we land on this?"

The "before" isn't a vocabulary problem. It's the whole machine signature at once: filler (leverage, delve, seamless), business jargon (move the needle, actionable), a rule-of-three, the it's not X, it's Y reflex, and puffery (stands as a testament). Swapping synonyms leaves all of that standing. The skill strips it and commits to one concrete claim.

Why use it

• It fixes the tells that actually give AI away. The signature is structural and substantive, not just word choice. human-voice cuts vacuity, breaks uniform sentence rhythm (what detectors call burstiness), dismantles rule-of-three and bold-bullet templates, and makes the text take a position. Diction comes last.
• It never fabricates to sound human. Numbers, quotes, citations, defined terms, and code are invariants. When a draft needs a fact it doesn't have, the skill marks [SOURCE NEEDED] rather than inventing one. That anti-hallucination protocol is built in.
• It matches the genre instead of forcing one voice. A technical report stays professional, marketing copy addresses "you", a blog post has personality. Six register profiles share one universal core of tells fixed everywhere.
• It ships a linter you can gate CI on. A dependency-free Python script scores the regex-able tells and prints a verdict. Wire it into a build with --fail-over. It stays honest about what it can't see.
• It's honest about detectors. No homoglyph or zero-width tricks, no deliberate typos. The goal is genuinely better writing, not a passing score. AI detectors also misclassify non-native-English writing as machine-made (Liang et al. 2023), so no detector is ground truth anyway.

Install

1. Plugin marketplace (one command)

/plugin marketplace add stephenoffer/human-voice
/plugin install human-voice@human-voice

Then run /human-voice in any session.

2. Manual skill copy

git clone https://github.com/stephenoffer/human-voice.git
cp -r human-voice/skills/human-voice ~/.claude/skills/        # user scope
# or, for one project only:
cp -r human-voice/skills/human-voice <your-project>/.claude/skills/

3. Use it from this repo directly

The skill already lives at skills/human-voice/. Open this repo in Claude Code and invoke /human-voice.

Use

/human-voice <file-path | pasted-text> [fix|generate] [register: technical|business|marketing|academic|casual|creative]

• fix (default) rewrites an AI-sounding draft.
• generate drafts new copy that reads human from the start.
• register matches the genre's conventions; it's inferred if you omit it.

Run it on its own anytime:

python3 skills/human-voice/scripts/detect_ai_prose.py <file>
python3 skills/human-voice/scripts/detect_ai_prose.py --register marketing <file>
python3 skills/human-voice/scripts/detect_ai_prose.py --dialect american <file>
python3 skills/human-voice/scripts/detect_ai_prose.py --fail-over 5 <file>   # exit 1 if score > 5 (CI gate)
python3 skills/human-voice/scripts/detect_ai_prose.py --fix <file>          # rewrite em-dashes/--/spaced hyphens to commas, strip emoji, swap filler
python3 skills/human-voice/scripts/detect_ai_prose.py --fix-dry-run <file>  # preview the autofix without writing
printf '%s' "$TEXT" | python3 skills/human-voice/scripts/detect_ai_prose.py -

--fix applies only the unambiguous, deterministic edits: dash-to-comma normalization, decorative-emoji removal, and 1:1 filler/jargon swaps. It skips dash and emoji changes in the creative register (and keeps emoji in casual), never touches code, numbers, or links, and leaves the judgment work — varying the replacement mark, cutting vacuity, sharpening stance — to the rewrite pass.

On Windows, use the py launcher (or python) instead of python3, and pipe text with PowerShell: $TEXT | py skills/human-voice/scripts/detect_ai_prose.py -.

It needs only Python 3 (3.8+), no pip install. The word and spelling lists live in skills/human-voice/scripts/ai_prose_patterns.json; edit them to taste, including the category weights and verdict bands.

How the score works

The score is weighted tells per 1000 words, with a band attached: below 5 reads clean, 5 to 15 is watch, and 15+ is a strong-tell. Lower is better. Treat it as a floor, not a judgment: it catches cheap, regex-able tells but can't see vacuity, weak stance, or fabrication. The real test is a skeptical human read.

skills/human-voice/examples/ has a before/after pair for every register (technical, marketing, casual, academic, email) plus a generate-mode example, a refusal-to-fabricate example, a restraint case, and an annotated walkthrough. Each "after" scores clean; run it on both halves to confirm.

The linter is measured, not asserted: eval/ holds a labeled corpus and run_eval.py, and eval/EVAL.md reports precision/recall and the false-positive rate on human-written text.

How it compares

The difference: other tools score or nitpick. human-voice fixes the structural and substantive tells first, matches the genre, and treats word choice as the last and shallowest pass.

FAQ

Will this beat GPTZero? Sometimes, as a side effect, but that isn't the point and isn't a promise. The aim is prose a skeptical human reads as human-written. No detector is ground truth; they carry real false-positive rates.

Why did it flag my human-written text? The linter is a regex floor; it over- flags sometimes. Lower a threshold, add a protected_terms/context_exceptions entry, or open a false-positive issue — those feed the corpus and the FPR measurement.

Does it work on non-English text? No. The word lists and dialect map are English-only today. --lang accepts only en.

Can I tune it per project? Yes. Drop a .humanvoicerc (JSON) at your repo root to set a default register/dialect, override thresholds and category weights, and add protected terms. See CONTRIBUTING.md.

What it won't do

It improves writing; it does not disguise machine text. No Unicode homoglyphs, no zero-width characters, no deliberate typos, no meaning-degrading synonym swaps, and never an invented fact or a faked quote to seem human. Passing a detector is a side effect of good writing, not the objective.

Contributing

See CONTRIBUTING.md and CHANGELOG.md. Tests: python3 tests/stress_test.py (also run on Python 3.8–3.13 in CI).

Credits

The v0.4 recalibration — ranking tells by what readers cite rather than what a scanner matches — draws on two MIT-licensed projects and the ~90k-post Reddit study behind them: JCarterJohnson/vibecoded-design-tells and ryanthedev/oberskills. See references/cited-vs-matched.md.

License

MIT. See LICENSE.