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humanize-korean

AI(ChatGPT·Claude·Gemini)가 쓴 한글 텍스트를 사람이 쓴 글처럼 윤문한다. 번역투·영어 인용 과다·기계적 병렬·관용구·피동 남용·접속사 남발·리듬 균일·이모지/불릿 과다 등 10대 카테고리 70개 AI 티 패턴을 탐지·분류해 내용은 한 글자도 건드리지 않고 문체·리듬·표현만 자연스럽게 재작성한다. 트리거 — "AI 티 없애줘", "AI 윤문", "ChatGPT 티 제거", "번역투 고쳐", "사람이 쓴 것처럼", "humanize Korean". 단순 맞춤법 교정·번역·내용 추가는 대상 아님.

Quality

94%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

85%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A tight, well-structured single-call procedure with explicit validation, rollback, and grading — the body itself is close to exemplary. The main weakness is that the skill's core knowledge (the A–J pattern taxonomy, S1/S2 severities, self-verification checklist) lives in referenced files that are absent from the bundle and referenced via a non-resolving external path.

Suggestions

Ship the rulebook files inside the bundle and use skill-relative paths (e.g., `references/quick-rules.md` instead of `../../../skills/humanize-korean/references/quick-rules.md`) so steps 1 and 6 are actually executable.

Define the A–J category IDs and S1/S2/S3 severity scale inline (one line each, or a compact legend), since the grading thresholds ('S1 0건, S2 2건 이하') and the processing order depend on them.

Specify how `run_id` is generated and how 변경률 (change rate) is computed (e.g., character-level diff ratio), so the 30%/50% abort thresholds and the summary block can be produced deterministically.

DimensionReasoningScore

Conciseness

The ~45-line body is lean and dense: 7 iron rules, 8 procedure steps, a grade table, options, and references — every token earns its place and nothing explains concepts Claude already knows. Firmly the score-5 anchor ('lean and efficient; assumes Claude's competence').

5 / 5

Actionability

Guidance is largely executable: concrete processing order ('D → A → I → G → H → F → B → C·J → E 순서로 문단 단위 처리'), numeric thresholds ('변경률 30% 초과 = 경고, 50% 초과 = 작업 중단·롤백'), and an exact output contract ('`_workspace/{run_id}/final.md`', 정확히 하나의 `<!-- HUMANIZE-SUMMARY -->` 블록). However the A–J category IDs and S1/S2/S3 severity scale that the thresholds and grades depend on are never defined in-body, and the rulebook that defines them ('quick-rules.md') does not exist at the referenced path — more than the score-5 'copy-paste ready, covers the common cases' bar.

4 / 5

Workflow Clarity

The 8-step sequence is explicit with a validation checkpoint (step 6, quick-rules 자체검증 6항) and a feedback loop for error recovery ('위반 edit만 롤백해 최대 한 번 다시 처리한다'), plus abort/rollback thresholds at 50% change rate. This matches the score-5 anchor (clear sequence, explicit validation, retry loop) rather than score 4's 'minor validation gaps'.

5 / 5

Progressive Disclosure

References are well-labeled and one level deep ('슬림 룰북', '분류 체계 본진', '윤문 처방'), but all three point to `../../../skills/humanize-korean/references/*.md` — a path outside this bundle — and no `references/` directory exists at all, so none of the referenced files resolve. Navigation is signaled but broken, fitting score 3 ('references present but not clearly/signally usable, organization could improve') rather than score 4's 'minor organization gaps'.

3 / 5

Total

17

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An exemplary description: it states concrete capabilities with a comprehensive category inventory, provides natural bilingual trigger phrases, answers both 'what' and 'when' explicitly, and delineates its boundary against adjacent skills. Voice is consistently third-person declarative with no fluff.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — '탐지·분류해…재작성한다' (detects, classifies, rewrites) — and comprehensively names the pattern categories (번역투, 영어 인용 과다, 기계적 병렬, 피동 남용, 접속사 남발, 리듬 균일, 이모지/불릿 과다 등 10대 카테고리 70개 패턴) with an explicit scope constraint ('내용은 한 글자도 건드리지 않고 문체·리듬·표현만 자연스럽게 재작성한다'). Coverage is comprehensive with no noticeable gaps, matching the score-5 anchor rather than the score-4 'minor gaps' anchor.

5 / 5

Completeness

Both 'what' (detect/classify 70 AI-tell patterns across 10 categories and rewrite style/rhythm/expression only, preserving meaning) and 'when' are explicitly and concretely answered via the '트리거 — …' clause with concrete phrases, plus boundary exclusions ('단순 맞춤법 교정·번역·내용 추가는 대상 아님'). This clearly matches the score-5 anchor, not score 4 where 'when' could be more explicit.

5 / 5

Trigger Term Quality

Explicit natural trigger phrases users would actually say are provided in both Korean and English: '"AI 티 없애줘", "AI 윤문", "ChatGPT 티 제거", "번역투 고쳐", "사람이 쓴 것처럼", "humanize Korean"'. This covers synonyms and register variations comprehensively, matching the score-5 anchor.

5 / 5

Distinctiveness Conflict Risk

A clear niche (removing AI tells from Korean text) with distinct triggers and explicit exclusions separating it from spell-checking, translation, and content-addition skills. Minimal conflict risk, matching the score-5 anchor.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

epoko77-ai/im-not-ai

Path

copilot/skills/humanize-korean/SKILL.md

Reviewed

Table of Contents

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