사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/probabl-ai/skore-skills --skill choose-python-library시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
Choose Python Library
Human-facing prose
Details: setup-workspace references/human_facing_prose.md. Present library options as a data-science choice for this job. Do not name skill ids or the wrapper CLI in the question.
- State the job and constraints.
- Run
python -m skore_skills env stack. A job listed under
competing is a genuine choice; anything the policy already fixes (mandatory, stage, export) is settled — use it rather than reopening the choice. Do not read the packaged JSON file directly; it lives inside the installed package.
- For a genuine choice, present the smallest useful option set and
ask the user. Do not pick silently.
- After the user chooses, load
add-python-packagewhen
status.skills reports it installed. If it is not installed, name the package and stop. Do not call env add from this skill. Typical callers: explore-ml-data (pandas vs polars), later extras (tuning). Not setup-workspace. Plotting jobs: load plot-ml-figure if installed; do not present matplotlib vs seaborn vs plotly.
- Confirm symbols with
python -m skore_skills api get <dotted>...
before writing calls. Several symbols go in one call. Scope for one package is python -m skore_skills env route <pkg>, not a guess.
Stop conditions
- Do not install every candidate.
- Do not use popularity alone as a technical decision.
- Do not run pip directly in a managed project.
- Do not substitute black/isort for Ruff or sklearn Pipeline for
skrub DataOps; those choices are already fixed.
- Do not present matplotlib vs seaborn vs plotly; that job is
plot-ml-figure.
