humanize
Skill4 starsChanged 14 days agoMITView on GitHub
Rewrites AI-sounding prose so it reads like a person wrote it, removing AI tells (em dashes, 'it's not X, it's Y', model vocabulary, stock openers) while keeping every fact. Use when the user says 'humanize this', 'de-slop', 'this sounds like AI', 'robotic', or 'like ChatGPT', or 'too many em dashes', or asks why a draft feels off. For a report without a rewrite, see house-style. For matching a specific person's voice, see brand-voice. For a new post, see article.
74 other projects use a version of this.
Adopted by
Most recently taken up by
- pedrohcgs/claude-code-my-workflowtheir own version, first seen Nov 8, 2026
- jmusick/skillstheir own version, first seen Nov 8, 2026
- Nossie/humanizetheir own version, first seen Nov 8, 2026
- droxey/skillstheir own version, first seen Nov 8, 2026
- lalulali/vespyrtheir own version, first seen Nov 6, 2026
- angusfretwell/skillstheir own version, first seen Nov 4, 2026
Used with
Projects with it also use humanize-redo, humanize-korean and 10 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
dawn840705Skillhumanize-redo
가장 최근 윤문 결과를 2차로 다시 다듬는다 — 특정 카테고리·문단·강도 조정도 가능. humanize-korean 의 윤문 콜을 기존 run_id 에 재실행해 잔존 finding 을 처리한다. 트리거 — "$humanize-redo". **명시적 슬래시 호출 전용** — 자연어 후속 요청은 `humanize-korean` 오케스트레이터가 받는다.
Also in 5 projects that use this
mskim8717Skillhumanize-korean
AI(ChatGPT·Claude·Gemini 등)가 쓴 한글 텍스트를 "사람이 쓴 글처럼" 윤문해주는 오케스트레이터 스킬. 번역투·영어 인용 과다·기계적 병렬·관용구·피동태 남용·접속사 남발·리듬 균일성·이모지/불릿 과다 등 10대 카테고리 40+ AI 티 패턴을 탐지·분류해 내용은 한 글자도 건드리지 않고 문체·리듬·표현만 자연스러운 한국어로 재작성한다. 트리거 — "AI 티 없애줘", "AI 같은 글 자연스럽게", "GPT/ChatGPT 문체", "AI 번역투 고쳐", "사람이 쓴 것처럼 윤문", "AI 윤문", "ChatGPT 티 제거", "한글 AI 탐지·윤문", "AI 글 사람처럼", "번역투 제거", "영어 인용 많은 글 윤문", "AI 글 티 안 나게", "휴머나이저", "humanize Korean", "AI detector bypass 한글". 후속 작업 — "특정 카테고리만 다시", "윤문 강도 조정", "장르 바꿔서", "이 문단만", "2차 윤문" 도 모두 이 스킬. 단순 맞춤법·오탈자 교정은 직접 처리, 번역은 번역 스킬, 내용 추가·삭제를 동반한 재작성은 별도 집필 스킬.
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Structured performance profiling workflow. Identifies bottlenecks, measures against budgets, and generates optimization recommendations with priority rankings.
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Rayan55050Skillcontent
Создание контента: посты, статьи, тексты
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Run the proofreading protocol on manuscript files. Checks grammar, typos, overflow, consistency, and academic writing quality. Produces a report without editing files.
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Other versions74 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 74
- epoko77-ai/im-not-ai★ 6ksame name
- pedrohcgs/claude-code-my-workflow★ 1.7ksame name
- PolyArch/humanize★ 1.5ksame name
- cwinvestments/memstack★ 425same name
- Mathews-Tom/armory★ 340same name
- whawkinsiv/solo-founder-skills★ 260same name
- whawkinsiv/claude-code-skills★ 258same name
- c-kick/hnl-agent-skills★ 56same name
- tzengyuxio/skills★ 45same name
- amondnet/yoonmoon★ 17same name
64 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository14 other files it gives its agents.
- AGENTS.mdAGENTS.md
- ai-seoSkill
- articleSkill
- blog-contextSkill
- brand-voiceSkill
- content-planSkill
- house-styleSkill
- internal-linksSkill
- keyword-researchSkill
- metaSkill
- refreshSkill
- seo-optimizeSkill
- serp-analysisSkill
- youtube-to-blogSkill
The file
We split this skill into the rules it gives an agent
3 rules. 0 are common, 2 are distinctive, and 1 is very specific to this project.
0
Common
Most of the 64 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 20
If .agents/blog-context.md or a voice-<name>.md profile exists, read it and write the rewrite in that voice: its tone, point of view, and …
- Distinctive: line 26
Read references/ai-tells.md.
- Distinctive: line 51
Keep a voice. Removal alone leaves bland prose.
“Common” is measured: kept by at least 6 in 10 of the 64 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Humanize
You rewrite AI-sounding prose so it reads like a specific human wrote it. You are not a grammar checker. You hunt the patterns that make readers stop trusting a page because they can tell a model wrote it.
You're done when the rewrite passes the rulebook's self-check, keeps every supported fact and claim from the original, adds none, and stays within about 10% of the original length. Cutting padding or flagged unsourced claims can take it shorter.
Initial assessment
- If
.agents/blog-context.mdor avoice-<name>.mdprofile exists, read it and write the rewrite in that voice: its tone, point of view, and traits. Shifting a faceless draft into the recorded voice (first person, the author's humor, their rhythm) is restyling, not adding claims; inventing experience the draft doesn't state is still off-limits. A habit the profile lists as intentional (fragments, rhetorical questions, em dashes) stays, even where the rulebook would cut it. - Read references/ai-tells.md. The hard rules, word, sentence, rhythm, tone, formatting, and whole-piece sections drive this job. The stock title, intro, and closing molds are hard rules and come out on sight; the length and shape rules in those sections are for authoring, so leave the draft's structure alone.
- Facing a paragraph where the tells stack? See references/example.md for a worked before and after.
Method
- Hard rules first. Fix every hit: em and en dashes, curly quotes and arrows, bold-label bullets, model vocabulary used as metaphor, chatbot residue. These are what readers clock fastest.
- Structure. Rewrite negative reframes ("it's not X, it's Y"), self-answered questions ("The result? Devastating."), stacked threes, participle padding, and signposted conclusions into direct statements.
- Cadence. Apply the rulebook's sentence-length and sentence-start rules (under Rhythm and paragraphs), and leave paragraphing and lists as the author set them. Aim for spread, not shortness: a run of five-word sentences is its own tell.
- Frequency. For everything else in the rulebook, fix repeats and stacks and leave single honest uses alone. Scale to length: what a 2,000-word post can carry once reads as a tell in a single paragraph.
- Scope. If most sentences are tells, stop patching. Rewrite the passage from its actual argument, keep every fact and the author's claim, then check it against the steps above. A near-total rewrite is the right call for near-total slop.
- Keep a voice. Removal alone leaves bland prose. Where the draft makes a real claim, keep one concrete detail (a number, an example, a named thing, a plain opinion). Unsourced claims follow the rulebook's no-fabrication rule: cut and flag. If the context file's "Experience and proof" section holds a real detail that would fill the gap, suggest it in the notes instead of adding it yourself.
- Voice pass. Reread the rewrite against the recorded voice. If it could have come from any competent writer, it isn't done: apply the voice's point of view, humor, rhythm, and signature moves until someone who reads this blog would recognize it. With no recorded voice, aim for a plain, specific, slightly opinionated register rather than neutral filler, and keep the draft's point of view.
What to preserve
The facts, the argument, the structure, the format (markdown stays markdown, a list stays a list), the genre, and any intentional voice the draft already has: slang, asides, a signature rhythm. When the draft has no voice of its own, the recorded voice supplies one. You change how it reads, not what it says.
Output
Return the rewritten text, with no commentary above it. If you cut length because padding was the tell, say so in the notes. The notes don't count toward the length.
Then, below a --- line, list the top three tell categories you removed, using the
rulebook's own rule names, with the count of instances in the original. Rank hard-rule
hits above frequency tells, then by count. Each bullet, dash, or phrase is one
instance, and each counts once: a span that breaks two rules (a reframe with an em
dash in it) goes under the more serious one. Then a short note on any missing fact
the author should supply.
Related skills
article · house-style · brand-voice · seo-optimize · blog-context
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Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

