AI가 쓴 한글 텍스트를 자연스럽게 윤문하는 진입 명령. humanize-korean 파이프라인을 Fast 모드(기본)로
Scanned 9/2/2026
Install to Claude Code
npx -y skills add nota-america/forgecat-agent-profiles --skill humanize --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Humanize?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/nota-america-humanize-forgecat-agent-profiles)More formats (shields.io, HTML) on the badges page.
---
name: humanize
description: AI가 쓴 한글 텍스트를 자연스럽게 윤문하는 진입 명령. humanize-korean 파이프라인을 Fast 모드(기본)로
실행하고 `--strict`면 정밀 3콜(진단→겨냥 윤문→finalize). 트리거 — "/humanize".
argument-hint: "[윤문할 텍스트 또는 파일 경로]"
disable-model-invocation: true
---
# /humanize — 한글 AI 티 제거
`humanize-korean` 스킬을 발동해 인자로 전달된 한글 텍스트(또는 파일)에 윤문을 실행한다.
## 입력
$ARGUMENTS
## 동작
1. 인자가 비면: "윤문할 텍스트를 붙여넣어 주세요" 안내 후 종료.
2. 인자가 파일 경로(.txt/.md)면 `Read`로 본문 로드.
3. 인자가 텍스트면 그대로 입력으로 사용.
4. `humanize-korean` 스킬 SKILL.md 절차(Phase 0 → 결과 전달)를 따른다 — 기본 **Fast 모드**, `--strict` 시 정밀 3콜(진단→겨냥 윤문→finalize).
5. 결과 전달:
- 한 줄 상태(변경률 / 등급 / 자체검증 통과)
- 윤문본 본문(마크다운 블록)
- 카테고리별 탐지 건수 before/after
- 주요 변경 하이라이트 3~5건
- 등급 B 이하면 "`/humanize-redo`로 2차 윤문 가능" 안내
## 옵션 (인자 끝에 자연어로)
- `장르: 칼럼|리포트|블로그|공적` — 장르 명시 (생략 시 첫 300자로 자동 추정)
- `강도: 보수|기본|적극` — 윤문 강도 (기본값: 기본)
- `최소심각도: S1|S2|S3` — 탐지 임계값 (기본값: S2)
- `--strict` — 정밀 3콜(진단→겨냥 윤문→finalize) 강제
## 참고
- 분류 체계: `humanize-korean/references/ai-tell-taxonomy.md`
- 윤문 처방: `humanize-korean/references/rewriting-playbook.md`
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!
Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
**Complete production-ready guide for Google Gemini embeddings API** This skill provides comprehensive coverage of the `gemini-embedding-001` model for generating text embeddings, including SDK usage, REST API patterns, batch processing, RAG integration with Cloudflare Vectorize, and advanced use cases like semantic search and document clustering. ---
Interview, source-challenge, verify, save, and ADR-gate fuzzy coding requests into Codex-ready implementation specs. Use when a feature, bugfix, refactor, migration, repo-wide change, or architecture task needs user-verified requirements, source-backed decisions, durable architecture decisions, acceptance criteria, validation commands, rollout notes, saved spec/ADR files, and a Codex execution prompt. Do not use when already fully specified or when the user wants direct implementation now.
Use when a repo needs CodeGraph plus ast-grep for Codex MCP setup, exploration, impact analysis, structural search, or safe refactor planning.