# Data Science Python Stack

> Opinionated Python stack for data-science / ML work — one library per job, organized into tiers (mandatory / user choice / optional / transitive). SKILL.md is the index; per-library references/<library.md files carry scope, "pick this when" / "pick something else when", and pairings. TRIGGER when (any of these): (1) a library import fails in this stack's domain — the answer is install, not substitute (see § "Missing dependency"); (2) a library choice has to be made — explicitly (the user asks "which library for X?") or implicitly (code is about to introduce a new dependency, or the project is being scaffolded and the tabular library hasn't been picked yet); (3) starting a new Python data-science / ML project; (4) the user or current code reaches for a substitute outside the stack (xgboost, lightgbm, black, isort, flake8, poetry, hatch), or reaches for mlflow to log params/metrics, or for crossvalscore + handwritten reporting — redirect: tracking → skore Project API, evaluation / reporting → skore report classes, mlflow stays only for model serving / registry. SKIP when: the project is non-Python; the work is web / backend / infra unrelated to data science; the library is already chosen and installed and the task is implementation inside it (bug fix, feature work, refactor) with no new dependency in play. HOW TO USE: read this SKILL.md end-to-end before recommending or installing anything — picking from a single index entry hides the tier (whether the library is mandatory, a user-choice, optional, or already transitively present) and the pairings, and both matter. Then read the linked references/<library.md for the chosen library's scope and tradeoffs. Don't silently substitute one library for another; if no entry fits, surface the gap to the user.

## Facts
- Page: https://tashan.sh/capability/skill-probabl-ai-data-science-python-stack
- tashan id: skill:probabl-ai/data-science-python-stack
- Source: https://github.com/probabl-ai/skills
- Type: skill
- Category: other
- tashan score: not scored (catalogued only — too little public evidence)
- Adoption: 9.0
- Upkeep: 97.0
- Freshness: 94.0
- Evidence coverage: 84% of the inputs this score can use
- Health: active
- Instruction depth: not yet graded
- License: BSD-3-Clause
- Official: no

## Install

```sh
cp -r data-science-python-stack ~/.claude/skills/
```

## Security audit
Not scanned. We audit npm-published capabilities; this one has no npm package we can resolve, or has not reached the queue. This is not a clean bill of health.

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Measured 2026-08-14 by tashan (https://tashan.sh) from public evidence. Scorer s5.