A Claude Code skill that removes signs of AI-generated writing from text — in both English and Japanese.
LLMs produce text with predictable tells: inflated significance, promotional tone, excessive hedging, sycophancy, em-dash overuse, rule-of-three lists, AI vocabulary words, and more. This skill detects those patterns and rewrites text to sound like a human wrote it.
- English: 24 patterns based on Wikipedia's "Signs of AI writing"
- Japanese: 30 patterns across 6 categories, adapted for Japanese-specific LLM artifacts (formatting leaks, monotonous rhythm, instruction-manual tone, fence-sitting, abstract filler, cliched metaphors)
Language is auto-detected. No configuration needed.
Clone directly into your Claude Code skills directory:
mkdir -p ~/.claude/skills
git clone https://github.com/kgraph57/akanuke.git ~/.claude/skills/akanukeOr copy just the skill file:
mkdir -p ~/.claude/skills/akanuke
curl -o ~/.claude/skills/akanuke/SKILL.md \
https://raw.githubusercontent.com/kgraph57/akanuke/main/SKILL.mdInvoke the skill in Claude Code:
/humanize [paste your text]
Or just ask naturally:
Humanize this: [your text]
人間っぽくして: [your text]
AI臭を消して: [your text]
| Flag | Description |
|---|---|
--report / レポートも |
Append a diagnostic report with pattern counts, severity, and AI score |
--formal |
Business document mode (polished but not robotic) |
--academic |
Scholarly writing mode (objective, no unsupported claims) |
--medical |
Healthcare content mode (preserves terminology, adjusts to evidence level) |
| Category | Patterns |
|---|---|
| Content | Significance inflation, notability name-dropping, superficial -ing analyses, promotional language, vague attributions, formulaic challenges sections |
| Language | AI vocabulary, copula avoidance, negative parallelisms, rule of three, synonym cycling, false ranges |
| Style | Em dash overuse, boldface overuse, inline-header lists, title case headings, emojis, curly quotes |
| Communication | Chatbot artifacts, knowledge-cutoff disclaimers, sycophantic tone |
| Filler | Filler phrases, excessive hedging, generic positive conclusions |
| Category | Patterns |
|---|---|
| Leftover formatting | Asterisk bold, em dashes, over-bracketing 「」, nested 「『』」, colon-space, over-parenthesizing, parenthetical disclaimers, slash-separated concepts |
| Monotonous rhythm | Same endings, excessive conjunctions, flat temperature, neat closings, repeated not-A-but-B |
| Instruction-manual tone | Long preambles, hollow "conclusion first", structure re-announcements, STEP labels, closing cliches |
| Fence-sitting | Insurance clauses, forced neutrality, weak negation, case-by-case escape |
| Abstract filler | Abstract-only prose, strong claims with zero evidence, synonym barrages, AI vocabulary (JP) |
| Cliched metaphors | Tool metaphors (羅針盤, 地図), body metaphors (土台, 柱), machine metaphors (車の両輪), cooking metaphors (レシピ) |
Before:
AI-assisted coding serves as an enduring testament to the transformative potential of large language models, marking a pivotal moment in the evolution of software development. It's not just about autocomplete; it's about unlocking creativity at scale.
After:
AI coding assistants speed up some tasks. In a 2024 study by Google, developers using Codex completed simple functions 55% faster, but showed no improvement on debugging or architectural decisions.
Before:
現代の医療現場において、生成AIの活用は非常に重要なテーマとなっています。以下では、その具体的な活用方法について3つの観点から解説します。これは単なる「ツール」ではなく、医療の「羅針盤」とも言えるでしょう。参考になれば幸いです。
After:
医療の現場で生成AIが使える場面は、思ったより広い。診断支援では鑑別診断のリストアップや見落としの防止に向いている。万能ではないが、定型的な作業を肩代わりさせるだけでも、浮いた時間を患者や研究に回せる。
- Training data bias — Manuals, FAQs, regulations, and training materials are overrepresented. LLMs learn them as templates.
- Safety alignment side effects — "Don't say anything harmful" becomes "don't say anything at all." Every position gets hedged.
- English-centric architecture — Markdown formatting, bulleted-list culture, and English logical structures leak into other languages.
- Wikipedia: Signs of AI writing — primary source for English patterns
- WikiProject AI Cleanup — maintaining organization
- blader/humanizer — original English skill
- op7418/Humanizer-zh — Chinese adaptation
- hardikpandya/stop-slop — AI cliche removal
MIT