How to use
- Copy the installation command and review the source package before installing it in your own agent environment.
- Check the source document for required tools, credentials and supported hosts.
- Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/probabl-ai/skore-skills --skill choose-python-libraryBefore you start
- Installation does not include a model subscription, API allowance or permission to access your files.
- Repository stars and installation counts describe activity, not an independent quality or security test.
Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.
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.
