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 modu-ai/moai-cowork --skill career-resumegit clone --depth 1 https://github.com/modu-ai/moai-coworkWrote 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/modu-ai/moai-cowork/career-resume)<a href="https://agentmods.dev/skills/modu-ai/moai-cowork/career-resume"><img src="https://agentmods.dev/badge/skills/modu-ai/moai-cowork/career-resume/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/modu-ai/moai-cowork/career-resume"><img src="https://agentmods.dev/badge/skills/modu-ai/moai-cowork/career-resume.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 | $0.00150 | $0.03667 |
| Opus 5 | $0.00075 | $0.01834 |
| Sonnet 5 | $0.00030 | $0.00733 |
| Haiku 4.5 | $0.00015 | $0.00367 |
Grade A, and why
career-resume 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 — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
이력서/자소서 빌더 (career-resume)
2026 한국 채용 시장은 챗GPT·제미나이로 쓴 자소서 진정성 검증과 팀핏 평가가 핵심입니다. 본 스킬은 신입 공채부터 헤드헌터·리멤버 경력 오퍼까지 동일한 KKK-STAR + USP+CAR 골격으로 처리하며, AI 흔적 가드와 NCS·블라인드 모드를 기본 탑재했습니다.
지원 영역
| 영역 | 설명 |
|---|---|
| 자기소개서 | STAR 기법 기반 맞춤 자소서, 기업 유형별(대기업/스타트업/공기업) 전략 |
| 이력서/CV | 이력서(사진형/블라인드), 영문 CV, ATS 키워드 최적화 |
| 경력기술서 | 프로젝트 기반 성과 기술, 수치화된 기여도 정리 |
| 프로필 최적화 | LinkedIn 프로필, 원페이지 소개서, 헤드라인 최적화 |
참조 가이드: references/korean-resume.md, references/cover-letter-guide.md
실전 작성 패턴 (한국 채용 시장)
1. USP + CAR 프레임 (이력서 작성 핵심)
STAR는 자소서, 이력서는 USP + CAR.
USP (Unique Selling Point) — 한 줄 셀링 포인트
"매출 30억 → 80억으로 키운 콘텐츠 마케터"
CAR (Challenge - Action - Result)
- C: 회사·시장 상황 (객관적)
- A: 본인이 한 행동 (주관적, 동사 우세)
- R: 정량 결과 (숫자·배수·증감)
각 경력 1줄 — USP / 3-5줄 — CAR 3 케이스.
2. "이력서는 광고다" 6초 원칙
500장 검토한 합격 로드맵 — 이력서 평균 검토 시간 6초. 그 안에 통과·탈락.
| 6초 안에 보이는 영역 | 작성 원칙 |
|---|---|
| 상단 1/3 | 이름 + 한 줄 타이틀 + 핵심 USP |
| 가장 최근 경력 첫 줄 | 매출·성장·임팩트 정량 |
| 키워드 강조 (Bold) | JD 일치 키워드 5-7개 |
3. 기본 문항 4종 답변 가이드
| 문항 | 핵심 |
|---|---|
| 지원동기 | 회사 분석 + 본인 경험·역량 연결 (회사·본인 5:5) |
| 성장과정 | 1-2 에피소드 + 현재 가치관 형성 |
| 성격 장단점 | 직무 적합 강점 + 개선 노력 중 단점 |
| 입사 후 포부 | 1년 / 3년 / 5년 단계별 + 회사 기여 |
4. 헤드헌터 활용 노하우
- 헤드헌터 첫 미팅 = 면접 (1차 검증)
- 이력서 영문 + 국문 동시 준비
- 희망 연봉 ±20% 명확히
- 회사 추천 받으면 2-3주 답변
- 헤드헌터 통한 협상 결렬 시 직접 지원 어려움 (블랙리스트 위험)
5. AI 생성 티 제거 — 후처리 체인 위임
채용 담당자가 AI 텍스트를 구분하는 시대에는, 초안을 직접 손보는 자체 체크리스트보다 검증된 후처리 체인에 맡기는 편이 일관됩니다. 자소서·이력서·경력기술서 초안을 완성한 뒤 다음 체인을 반드시 거칩니다.
career-resume → moai-coworker:ai-slop-reviewer → moai-writer:korean-humanize → 최종 검수
moai-coworker:ai-slop-reviewer— "~한 경험이 있습니다", "성장했습니다" 같은 범용 표현, 클리셰 결론, 형용사 남발 등 AI 패턴을 1차 검수·교정합니다.moai-writer:korean-humanize— 균일한 문장 길이·동일 접속사 패턴을 사람이 쓴 듯한 리듬(단문+장문 혼합, 구어 표현)으로 2차 다듬습니다.
체인 산출 후에도 본인만의 고유 에피소드(시간·장소·인물 구체), 결정 동기, 회사 사업·문화에 대한 구체 언급은 직접 보강해야 진정성이 완성됩니다.
자소서 작성 프레임워크 (STAR + 두괄식)
2026년 채용 시장에서는 AI 생성 텍스트를 채용 담당자가 구분하므로, 본인의 실제 경험과 고유한 관점을 반영하는 것이 핵심입니다.
KKK-STAR 구조
What ships with it
2 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.
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 · 222 lines · 150 tokens per session scan A 9d07d245122c
career-resume is a skill published in the GitHub repository modu-ai/moai-cowork (300 stars, last pushed 9d ago), licensed Apache-2.0. It adds 150 tokens to every session and 3,667 once invoked, about $0.0007 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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