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 collab-ux-designgit 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/collab-ux-design)<a href="https://agentmods.dev/skills/modu-ai/moai-cowork/collab-ux-design"><img src="https://agentmods.dev/badge/skills/modu-ai/moai-cowork/collab-ux-design/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/collab-ux-design"><img src="https://agentmods.dev/badge/skills/modu-ai/moai-cowork/collab-ux-design.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 | $0.00235 | $0.01836 |
| Opus 5 | $0.00118 | $0.00918 |
| Sonnet 5 | $0.00047 | $0.00367 |
| Haiku 4.5 | $0.00023 | $0.00184 |
Grade A, and why
collab-ux-design 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 7d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
UX 디자이너 (UX Designer)
개요
UX 디자인 분석 및 권고안 생성 스킬입니다. Fan-out/Fan-in 패턴을 사용하여 3개 차원(휴리스틱 평가, 접근성 검토, 사용자 플로우 분석)을 병렬로 분석한 후 결과를 통합하여 우선순위별 개선 로드맵을 제시합니다.
제품의 사용자 경험(UX)을 다차원으로 분석하고 실행 가능한 개선 권고안을 제공합니다.
트리거 키워드
UX 디자인, 사용자 경험, UX 분석, heuristic evaluation, 휴리스틱 평가, 접근성, accessibility, 사용성, 사용자 플로우, wireframe, UX 리서치, UI/UX 검토, 디자인 개선
워크플로우
1단계: 제품 컨텍스트 분석
제품 유형(Web App, Mobile App, Desktop Software, Physical Product), 타겟 사용자(연령, 기술 수준, 장애 여부, 주요 언어), 디자인 목표(전환율 개선, 이탈률 감소, 만족도 향상, 신규 사용자 온보딩), 기존 화면(Wireframe, Mockup, 실제 화면 캡처)을 확인합니다.
2단계: Fan-out - 병렬 분석
3개 차원이 독립적으로 병렬 분석 수행:
차원 1: 휴리스틱 평가 (Heuristic Evaluation) Nielsen's 10 Usability Heuristics를 기반으로 평가: 시스템 상태 가시성, 시스템과 현실 세계의 일치, 사용자 통제권과 자유, 일관성과 표준, 오류 예방, 인식보다 기억, 사용의 유연성과 효율성, 미적·최소화 디자인, 오류 진단·회복·정정, 도움말과 문서
차원 2: 접근성 검토 (Accessibility Review) WCAG 2.1 (Level AA) 기준으로 평가: 인지 가능성(텍스트 대안, 캡션, 구분 가능, 크기 조정), 운용 가능성(키보드 접근, 충분 시간, 광과민성, 네비게이션 가능), 이해 가능성(가독성, 예측 가능, 입력 도움), 견고성(호환성, 마크업)
차원 3: 사용자 플로우 분석 (User Flow Analysis) 사용자의 여정(User Journey)을 분석: 진입 경로, 핵심 경로, 장벽 식별(Friction Points), 예외 경로(Edge Cases), 이탈 경로(Exit Points)
3단계: Fan-in - 결과 통합
상관관계 분석(차원 간 중복 이슈와 상호 의존성 파악), 심각도 산정(Critical, Serious, Minor, Cosmetic), 영향도 평가(비즈니스 영향), 우선순위 결정(심각도 × 영향도 × 해결 비용)을 수행합니다.
4단계: 우선순위 산정
RICE 프레임워크 또는 MoSCoW 방법으로 우선순위 결정:
- RICE Score = (Reach × Impact × Confidence) / Effort
- MoSCoW: Must Have, Should Have, Could Have, Won't Have
5단계: 개선 로드맵 제안
우선순위별 실행 가능한 로드맵 작성: 단기(1-2주), 중기(1-3개월), 장기(3-6개월)
사용 예시
예시 1: 이커머스 앱 결제 플로우 UX 개선
- 제품: 모바일 이커머스 앱 (안드로이드/iOS)
- 타겟 사용자: 20-40대, 모바일 네이티브
- 디자인 목표: 결제 이탈률 감소 (현재 68%)
- 기존 화면: 장바구니 → 배송 정보 → 결제 수단 → 결제 완료 (4단계)
예시 2: SaaS 대시보드 온보딩 UX 개선
- 제품: B2B SaaS 대시보드 (웹 앱)
- 타겟 사용자: 비기술적 마케터, 30-50대
- 디자인 목표: 신규 사용자 첫 주 달성률 30% → 60%로 개선
- 기존 화면: 회원가입 → 이메일 인증 → 대시보드 진입 (빈 상태)
출력 형식
UX 분석 보고서는 다음 섹션들을 포함합니다:
- 제품 컨텍스트
- Fan-out: 차원별 분석 결과 (휴리스틱 평가, 접근성 검토, 사용자 플로우 분석)
- Fan-in: 통합 분석 (상관관계 분석, 우선순위 산정)
- 통합 UX 권고안 (P0/P1/P2/P3)
- 개선 로드맵 (단기/중기/장기)
What ships with it
1 file 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.
- 7d ago First seen · 119 lines · 235 tokens per session scan A 9a26e61c165a
collab-ux-design is a skill published in the GitHub repository modu-ai/moai-cowork (300 stars, last pushed 8d ago), licensed Apache-2.0. It adds 235 tokens to every session and 1,836 once invoked, about $0.0012 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 2026-09-03.
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