P1: compose the skills the archetypes' templates actually use

[ortizeg]

P1 from the review. Archetype composition had drifted from what the templates ship, so a generated project used tools nothing had taught the agent about.

Every change below is grounded in what each rendered template actually contains — I rendered all six and read their real dependencies rather than working from a wishlist.

The gaps

Change	Evidence
pixi → required on all 6	Every template ships a pixi.toml and every generated README opens with pixi install — yet 0 of 6 archetypes composed the skill explaining it.
code-quality → required on the 5 lacking it	All six configure ruff + mypy strict in their pyproject.toml.
testing → required on research-notebook	It ships tests/.
pydantic-ai → recommended on data-processing-pipeline	VLM-assisted labeling is a natural extension of a dataset pipeline, and it was referenced by zero archetypes.
abstraction-patterns, library-review → recommended on library-package	Both are precisely the subject of publishing a library.
onnx, gcp → recommended on pytorch-training-project	Its README promises export; Vertex is the training target.
tensorrt → recommended on model-zoo	Listed as an extension point in its README.

Two corrections the audit missed

Found by inspecting rendered dependencies rather than the READMEs:

• model-zoo required pytorch-lightning and onnx, but its template deliberately keeps torch optional (real deps: pydantic, pyyaml, loguru, httpx). Both moved to recommended, so required means "needed to run."
• research-notebook required pytorch-lightning while its template has no torch at all (real deps: numpy, matplotlib, pydantic, loguru). Moved to recommended.

What stops it drifting again

A new test ties composition to the template: if a template ships pixi.toml or tests/, or configures ruff/mypy, the archetype must require the matching skill. Composition can no longer silently diverge from what the generated project uses.

Deliberately not added

The audit also suggested opencv on cv-inference-service and matplotlib on data-processing-pipeline. Both templates use neither (cv-inference-service uses pillow; the data pipeline ships no plotting), so adding them would be noise. kubernetes was skipped for the same reason — the k8s claims were trimmed from that README in #11.

Verification

• uv run pytest tests/ → 257 passed (1 new composition test)
• whet doctor → all skill dependencies resolve
• ruff / format / mypy --strict / mkdocs build --strict → clean

🤖 Generated with Claude Code