使用方法
- 复制安装命令，在自己的智能体环境安装前先检查源码包。
- 从原始文档核对所需工具、凭据和支持的宿主。
- 说明输入材料与期望交付物，并对照来源检查输出。
npx skills add https://github.com/probabl-ai/skore-skills --skill choose-python-library开始前需了解
- 安装不包含模型订阅、API 额度或访问文件的权限。
- 仓库星数和安装量反映活跃情况，不等于独立质量或安全测试。
按源仓库内容呈现，保留标题、案例、代码、表格、链接以及原文引用的演示图片。
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.
