Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add bam-bam-2/solo-skills --skill humanize-koreangit clone --depth 1 https://github.com/bam-bam-2/solo-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bam-bam-2/solo-skills/humanize-korean)<a href="https://agentmods.dev/skills/bam-bam-2/solo-skills/humanize-korean"><img src="https://agentmods.dev/badge/skills/bam-bam-2/solo-skills/humanize-korean/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bam-bam-2/solo-skills/humanize-korean"><img src="https://agentmods.dev/badge/skills/bam-bam-2/solo-skills/humanize-korean.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00351 | $0.03721 |
| Opus 5 | $0.00176 | $0.01861 |
| Sonnet 5 | $0.00070 | $0.00744 |
| Haiku 4.5 | $0.00035 | $0.00372 |
Grade A, and why
humanize-korean scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanize Korean — AI 한글 티 제거 오케스트레이터 (v1.5)
v1.5 변경 고지 (2026-04-26) — v1.1 베이스라인 + Monolith Fast Path v1.2(voice profile)·v1.3(candidate pool)·v1.4(역할별 모델 분산)는 모두 핫패스 비용을 잡지 못해 5,000자 입력에 25분이 걸렸습니다. v1.5는 v1.1 단순 구조로 롤백한 뒤 단일 호출 monolith 에이전트만 추가한 설계입니다.
- Fast 모드(디폴트) —
humanize-monolith에이전트가 한 콜에서 탐지·윤문·자체검증 일괄 처리. 도구 호출 45회. 5,000자 이하 wall-clock 23분 목표.- Strict 모드(
--strict) — v1.1 5인 파이프라인 그대로(detector·rewriter·auditor·reviewer + taxonomist 분류 자산 유지). 정밀 검증·장문(8,000자+) 처리·etc.- 삭제됨: voice profile·candidate pool·promotion-checklist·sample-collection·권한 위계 §1~§6.
- 유지됨: 분류 체계 본진(C-9·C-10·D-7·H-3·I-3·I-4 등 v1.2~v1.3.1 신규 패턴)·rewriting-playbook·5인 에이전트 정의(strict 모드 백본).
Phase 0: 컨텍스트 확인 및 모드 결정
작업 시작 시 가장 먼저 다음 한 줄을 사용자에게 출력한다.
humanize-korean v1.5 — {fast|strict} 모드 / run_id: {YYYY-MM-DD-NNN}
모드 결정
- 사용자가
--strict·"정밀 모드"·"5인 파이프라인" 명시 → strict - 입력 8,000자 초과 → strict (자동 승급 + 사용자에 1줄 고지)
- 그 외 모두 → fast (디폴트)
run_id 결정
- 모든 경로는 cwd 기준. 새 폴더 생성도 cwd 기준
_workspace/{YYYY-MM-DD-NNN}/에 만든다. - 기존 시퀀스 확인은
Glob도구로 표지 파일을 매칭해 간접 조회. 올바른 사용법:Glob(pattern="_workspace/YYYY-MM-DD-*/01_input.txt")→ 결과에서 폴더명 추출 후 NNN 최댓값 + 1. 주의: Glob은 디렉토리 자체는 매칭하지 못한다. 반드시 그 안의 표지 파일(01_input.txt)을 매칭할 것.Bash ls는 OS·셸 환경에 따라 경로 해석이 달라지므로 사용 금지. - 당일 폴더가 없으면 NNN = 001. 있으면 마지막 NNN + 1.
- 부분 재실행 신호("이 카테고리만 다시"·"2차 윤문")일 경우 기존 run_id 재사용 + strict 모드로 자동 승급.
Fast 모드 (디폴트)
Phase 1: 입력 저장
- cwd 기준
_workspace/{run_id}/생성 - 입력 텍스트를
01_input.txt에 저장 - 첫 300자로 장르 자동 추정 (사용자 명시 시 우선)
Phase 2: Monolith 호출
humanize-monolith 에이전트를 Agent 도구로 1회 호출.
입력:
input_path: <abs path>/_workspace/{run_id}/01_input.txt
quick_rules_path: ${CLAUDE_SKILL_DIR}/references/quick-rules.md
genre_hint: 칼럼 | 리포트 | 블로그 | 공적 | null
출력 (에이전트가 직접 작성):
_workspace/{run_id}/final.md— 윤문본_workspace/{run_id}/summary.md— 메트릭·자체검증·하이라이트
monolith는 단일 호출 안에서 다음을 모두 수행 (자세히는 에이전트 정의 참조):
- quick-rules 룰북 로드 → 메모리에서 패턴 탐지 + 윤문 + 자체검증 6항 점검
- 변경률 50% 초과 시 자동 롤백
- 자체검증 위반 시 1회 부분 재실행
- final.md + summary.md 작성
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/ai-tell-taxonomy.md 59 KB
- references/baseline_v2.json 13 KB
- references/baseline.json 6.0 KB
- references/metrics_v2.py 27 KB runs code
- references/metrics.py 14 KB runs code
- references/quick-rules.md 8.9 KB
- references/rewriting-playbook.md 11 KB
- references/scholarship.md 24 KB
- references/web-service-spec.md 7.8 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 209 lines · 351 tokens per session scan A 8a0d2a3cbee0
humanize-korean is a skill published in the GitHub repository bam-bam-2/solo-skills (362 stars, last pushed 8d ago), licensed MIT. It adds 351 tokens to every session and 3,721 once invoked, about $0.0018 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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