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probabl-ai/skore-skills

choose-python-library

Resolve a library choice and add the selected dependency.

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Come usare

  1. Copia il comando e controlla il pacchetto prima di installarlo nel tuo ambiente agente.
  2. Controlla strumenti, credenziali e ambienti richiesti nel documento originale.
  3. Descrivi input e risultato desiderato; confronta l’output con le fonti.
npx skills add https://github.com/probabl-ai/skore-skills --skill choose-python-library

Prima di iniziare

  • L’installazione non include abbonamenti, quote API o accesso ai file.
  • Stelle e installazioni indicano attività, non un test indipendente di qualità o sicurezza.
Documento Skill originale

Contenuto dal repository con titoli, esempi, codice, tabelle, link e immagini preservati.

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.

  1. State the job and constraints.
  2. 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.

  1. For a genuine choice, present the smallest useful option set and

ask the user. Do not pick silently.

  1. After the user chooses, load add-python-package when

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.

  1. 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.

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