How do I install the Python Stack Env skill?

Run npx skillmds@latest add vathymut/python-stack-env in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Python Stack Env skill do?

Use when a Python import fails or you need to choose between competing libraries (plotting, serving, tabular DL) or route a package to the correct feature (dev/agent/default). It is listed under AI & ML on SkillMD.

Is Python Stack Env safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Python Stack Env?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Python Stack Env free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Python Stack Env?

vathymut (@vathymut) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Python Stack Env by vathymut — Agent Skill

Name: Python Stack Env
Author: vathymut

Python Stack & Environment Manager

Opinionated Python stack (one library per job) and environment management (manager detection, feature routing, install commands). This skill owns what, why, and how; detailed contracts live in the reference map below.

When NOT to use

Need API signature/docstring for an already-installed package — use python-api.
Need to read/query a data file — use data-access.
Building/deploying a non-Python service.

Gates (one-line)

Gate	Owner	Meaning
G-PKG-NAME	`iterate-ml-experiment` §0.5	Python package name
G-ENV-MGR	`python-stack-env`	Env manager (pixi/uv/poetry/…)
G-TABULAR	`iterate-ml-experiment` §0.5	`pandas` vs `polars`
G-SKORE-MODE	`iterate-ml-experiment` §0.5	`local`/`hub`/`mlflow`
G-EDA	`ml-eda` / `iterate-ml-experiment` §0	`run`/`skip`
G-DESIGN	`iterate-ml-experiment` §3	Design note approved?
G-CV-SPLITTER	`evaluate-ml-pipeline` §Evaluate	Splitter derived from `split_kwargs`
G-RUN	`iterate-ml-experiment` §3	Run now vs leave for later

Full wording: ml-conventions:references/ml-gates.md. Harness hints never waive AskUserQuestion gates.

Next-step pointers

Came here from…	After install, next is…
`iterate-ml-experiment` § 0.5	→ editable workspace package (`references/editable_workspace.md`)
`evaluate-ml-pipeline` § Audit	→ place `audit/<stem>.py`
`build-ml-pipeline` / `evaluate-ml-pipeline` § missing dep	→ continue at failing pre-flight box
Stack question / missing dep	→ caller; import should now succeed

Stop conditions

No silent pick on a competing-library job. Two+ libraries for the same job → AskUserQuestion before any import or install. Persist answer in journal/JOURNAL.md Status Workspace decisions. "Already pulled in transitively", "quick", "no preference" are NOT waivers.
Free-text resolution. User names a listed option → resolves. "You pick" / "whatever" → does NOT resolve; fall through to AskUserQuestion.
No substitute when import fails. import skrub fails → install, not sklearn.Pipeline. import skore fails → install, not cross_val_score. Surface the dep, invoke this skill, wait for confirmation.
Wrong-manager install forbidden. Mixing managers creates untracked state. Don't run the bootstrap installer yourself — surface the command, let the user run it.
No silent bootstrap. Detection finds nothing → ask user. Default suggestion: pixi.
Routing fixed, not asked. 3-feature layout (default/dev/agent) enforced; G-ENV-SCOPE fires only for ambiguous extras — never for ruff/pytest/ipykernel (→ dev).
Don't pin without reason. Install unpinned by default. Exception: skore/skrub latest always, mlflow>=3.
Agent feature install does not register a Jupyter kernel (orphan kernelspec).
Opened earlier ≠ gates passed. Reading this skill is not firing it.
Harness hints do not waive gates: ml-conventions:references/shared-ml-conventions.md.
Post-hoc audit. Walk pre-flight, confirm every box has Evidence:.
Harness / non-interactive default. When AskUserQuestion cannot run (CI/harness), use non-interactive defaults: matplotlib for plotting, FastAPI for serving, skrub/sklearn for tabular, and persist the choice in journal/JOURNAL.md with source: harness-default (do not treat as user approval).

Library tiers

Full catalog with per-library scope and tradeoffs: references/library-catalog.md.

Tier	Scope	Libraries
1 — Mandatory	Installed at project start, no exceptions	scikit-learn, skrub, skore, ruff, pytest
2 — Competing	User picks via AskUserQuestion	tabular DL, plotting, serving, experiment tracking
3 — Optional	Install on demand	interactive viz, reporting
4 — Transitive	Already pulled in, don't install	(e.g. by skore)

Agent feature (orthogonal): ipython + pyright — agent-only tooling for audit cell runner and LSP. Install owned by Agent feature install (§ map below). Not Tier 1–4.

Pre-flight

Pre-flight (python-stack-env):
- [ ] Detection done; manager: <pixi | uv | poetry | hatch | conda | pip+venv | none>
- [ ] G-ENV-MGR resolved
- [ ] Dep category determined for each package
- [ ] G-ENV-SCOPE resolved ONLY for ambiguous extras
- [ ] Tier 1/2/3 classification done for contested libraries
- [ ] Install command syntax confirmed (references/install-commands.md)
- [ ] Package list ready
- [ ] (Agent-feature only) G-AGENT-FEATURE + pyrightconfig.json
- [ ] Pre-flight re-emitted with evidence

Evidence format: ml-conventions:references/shared-preflight-evidence.md.

Reference map

Reference	Owns
`references/ambient_detection.md`	Detection edge cases (ambiguous `pyproject.toml`, multiple signals). Base detection: first signal wins — pixi → uv → poetry → hatch → conda → pip+venv → none (ask)
`references/gates.md`	`G-ENV-MGR`, `G-ENV-SCOPE`, `G-AGENT-FEATURE` — fire conditions + `AskUserQuestion` shapes
`references/placement.md`	Fixed 3-feature layout (`default`/`dev`/`agent`/`lsp`)
`references/composition_model.md`	Why the 3-feature layout
`references/install-commands.md`	Per-manager install tables
`references/env_prefixes.md`	Run prefixes (`<env-prefix>`, `<agent-env-prefix>`)
`references/agent_feature_anatomy.md`	Agent feature install (`scripts/install_agent_feature_{manager}.sh`); footguns: `references/per_manager_footguns.md`
`references/skore_variant.md`	skore mode × source table. Read `skore mode:` from JOURNAL.md Status; `mlflow` variant pins `mlflow>=3`; row absent → back to `iterate-ml-experiment` § 0.5 (G-SKORE-MODE)
`references/platform-notes.md`	macOS post-install: skrub → run `dot -c` once in project env
`references/bootstrap.md`	Full pixi bootstrap (init → features → Tier 1 → tabular → editable workspace → pyrightconfig → sync)
`references/editable_workspace.md`	Editable workspace package (post-scaffold)
`references/ruff.md`	Ruff rationale + config (`templates/ruff.toml`)
`ml-conventions:references/shared-ml-conventions.md`	Cross-cutting rules: scratch/, harness hints, missing-dep contract, gate ownership

Ownership

Decision	Owner
Library tiers, competing-library contract, ruff/NumPyDoc config	this skill (`templates/ruff.toml`, `references/ruff.md`)
Manager + feature scope + install command syntax	this skill
skore mode variant	`iterate-ml-experiment` decides mode; this skill executes
Gate registry wording	`ml-conventions`

Related skills

python-api — verify symbols after install.
iterate-ml-experiment — delegates G-ENV-MGR here.
ml-eda — delegate agent feature install.

Completion criteria

Manager detected (pixi→uv→poetry→...→none); G-ENV-MGR resolved
Tier classification correct; competing libs gated via AskUserQuestion (or harness default noted)
Install command surfaced (not run if bootstrap); rerun pre-flight with Evidence:

Python Stack Env

File contents