# Content Locale Humanize Ko

> Per-language calibration anchors for detecting AI-slop and translationese in Korean (ko) target text — auto-loads alongside content-locale-humanize when the target language is Korean.

- Skill: `muvon/content-locale-humanize-ko` (Agent Skill)
- Install (CLI): `npx skillmds@latest add muvon/content-locale-humanize-ko`
- Raw SKILL.md: https://api.skillmd.com/api/skills/muvon/content-locale-humanize-ko/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: Apache-2.0
- Author: Muvon (https://skillmd.com/u/muvon)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/muvon/content-locale-humanize-ko

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## Overview

Sourced calibration anchors for Korean, feeding `content-locale-humanize`'s AI-Slop & Translationese dimension. This is a calibration aid, not the checklist — reason natively beyond it (see the core skill's "why structure, not word lists" section).

## Instructions

Sourcing confidence: moderate, but with one structurally important, well-reasoned finding below.

Korean makes up roughly 1% of typical LLM training corpora versus roughly 60% English — meaning Korean output starts from a shakier native-fluency baseline than most other languages checked here. Weight interference and awkward-register findings a little more heavily for Korean; the model has comparatively less native Korean signal to draw on, so translationese bleeds through more easily.

Connector pileup: 그리고 (and), 하지만 (but), 그래서 (so) — mechanically chaining sentences instead of letting the logic connect on its own, a substitute for genuine narrative flow.

Overused descriptive-adverb endings: -게, -이, -히 — these tell rather than show; native Korean writing favors more concrete verbs over adverb-modified generic ones.

Frequency-outlier word: 중요한 (important) and its English cognate "significant" — both measured as disproportionately overused versus native baselines.

## References

rebrandb.com, zdnet.co.kr, curious-500.com.