Linkedin Humanizer
Remove AI tells from LinkedIn posts/comments: 2026 vocabulary density, reveal bridges...
Install
npx skills add sergebulaev/linkedin-skills --skill linkedin-humanizer -g -a claude-code -y- Repository
- sergebulaev/linkedin-skills5.5k
- Category
- Media
- Updated
- 8d ago
- License
- MIT
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- Yes, review before use
What it does
Remove AI tells from LinkedIn posts/comments: 2026 vocabulary density, reveal bridges, staccato fragments, stacked triads, performed sincerity. Tiered rewriter plus `--mode audit` LinkedIn post auditor and `--mode profile` voice builder. Not for drafting from scratch (use linkedin-post-writer) or beating AI detectors. Keywords: humanize, de-AI, post audit, post auditor, audit before posting.
LinkedIn Humanizer V3
Rewrites any text to remove the AI tells that human readers notice and that LinkedIn's "AI slop" filter reacts to. Based on Wikipedia's "Signs of AI writing" taxonomy, the 2025-2026 stylometry literature, and our own length-controlled corpus. V3 (2026-09): recalibrated on 2026 evidence. Vocabulary is scored by density, em dashes are capped instead of banned, forced rhythm is now a tell instead of a fix, and there is an over-correction guard.
What this skill does not do: it does not make text "pass" GPTZero, Pangram, Turnitin or Originality. Those are trained classifiers keyed on the instruction-tuning style signature; prompt-style "sound like a real person" rewrites are caught 92-95% of the time (VUB IJEI 2026, Russell 2025), and light mechanical rewriting raises detectability (arXiv 2603.17522). No post-hoc edit reliably beats a Pangram-class detector, and detector scores on LinkedIn-length text (100-300 words) are noise. The real value is elsewhere: expert human readers cite vocabulary (53%) and sentence structure (36%) as what gives AI text away, and LinkedIn's July 2026 slop-report button costs a flagged post roughly 40% of its views. This skill removes what those readers and that filter react to.
What changed in V3
Evidence tier in brackets: [strong] = replicated across 2+ independent 2025-2026 studies or our own length-controlled corpus; [vendor] = single platform or vendor dataset; [weak] = one study or expert-panel report.
