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Data Explorer Agent Skill

| CSV·Excel 데이터의 프로파일링·품질 보고서를 만들어 드립니다. 컬럼 요약·결측값/이상값 탐지·상관관계 분석을 거쳐 데이터 품질 보고서로 정리합니다.

658 tokens
context cost
the whole folder, loaded on every use
2
files
instructions only
0
copies elsewhere
how many repositories repackaged it
265
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/modu-ai/cowork-plugins --skill data-explorer

The instruction itself

6 sections, as written by the author

데이터 탐색기 (Data Explorer)

역할

CSV/Excel 파일을 받아 데이터 프로파일링, 품질 검사, 기초 분석을 수행하는 전문가.

워크플로우

Step 1: 데이터 로딩

  • 파일 형식 감지 (CSV, XLSX, TSV)
  • 인코딩 자동 감지 (UTF-8, CP949, EUC-KR)
  • 행/열 수, 데이터 타입 추론

Step 2: 프로파일링

  • 각 컬럼별: 타입, 유니크 수, 결측률, 최소/최대/평균/중앙값
  • 범주형 컬럼: 상위 5개 빈도
  • 수치형 컬럼: 분포 요약 (왜도, 첨도)

Step 3: 품질 검사

  • 결측값 패턴 (MCAR/MAR/MNAR 추론)
  • 이상값 탐지 (IQR 방법, Z-score)
  • 중복 행 식별
  • 데이터 타입 불일치

Step 4: 상관관계 분석

  • 수치 컬럼 간 피어슨/스피어만 상관계수
  • 높은 상관(|r| > 0.7) 하이라이트
  • 범주-수치 간 관계 (ANOVA F-test)

Step 5: 인사이트 + 분석 방향 제안

  • 핵심 발견 3가지 요약
  • 분석 방향 3가지 제안 (AskUserQuestion)

산출물

  • 데이터 프로파일 보고서 (마크다운)
  • 품질 점수 (0-100)
  • 분석 방향 추천

도구 사용

  • Bash: Python pandas 스크립트 실행 (프로파일링)
  • Read: CSV/Excel 파일 직접 읽기
  • 시각화 필요 시: moai-data:data-visualizer로 연계

이 스킬을 사용하지 말아야 할 때

  • 차트/그래프 생성 → moai-data:data-visualizer 사용
  • 공공데이터 조회 → moai-public-data:public-data 사용
  • PPT/Word 변환 → moai-office 플러그인 사용

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How to use it

Copy the folder

Take modu-ai/data-explorer from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.