anti-ai
SolidAnti-AI detection pass for any written text. Hard rules that strip the statistical fingerprints AI writing leaves behind — em-dash density, contrastive formula ("not X, it's Y"), nuclear phrases, copula inflation, sycophantic filler, uniform contractions, colon overuse, register monotony. Two tiers: hard rules (always fix) and voice-gated checks (verify against voice profile if present). Use when: "/anti-ai", "anti-ai pass", "anti-ai check", "strip ai signals", "does this sound like ai", "ai detection check", "humanize this", "remove ai fingerprints".
Install
Quality Score: 82/100
Skill Content
Details
- Author
- travsteward
- Repository
- travsteward/openwriter
- Created
- 6 months ago
- Last Updated
- 3 days ago
- Language
- TypeScript
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
anti-ai-writing
Use when writing or editing any prose for a human reader - documentation, READMEs, emails, reports, captions, scripts, blog posts, UI copy, commit messages, PR descriptions - or when asked to remove AI tells, de-slop text, make writing sound human, or check writing style. Applies 29 researched patterns that make text read as machine-generated.
anti-ai-slop-writing
Produces human-sounding text that avoids detectable AI writing patterns. Activates on any writing task — tweets, emails, articles, bios, captions, reports, copy, messages, LinkedIn posts, cover letters, README files, or any content where the output must not read as AI-generated. Enforces banned vocabulary, structural variety, punctuation discipline, accuracy rules, and voice calibration. Use when the user says "write," "draft," "rewrite," "make this sound human," "anti-slop," "not AI," or any variation of wanting authentic-sounding output.
humanizer-en
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, passive voice, negative parallelisms, filler phrases, and the statistical signatures AI detectors measure (burstiness, lexical density and diversity, part-of-speech distribution, emotional range).