What is model-selection?

A method for choosing a machine-learning model for a medical-imaging study and checking the chosen code or model files. It uses research papers to choose a model family, then checks licences, versions, training-weight origins, and benchmark overlap.

Use it to choose among image classification, segmentation, detection, or related model families and to vet a repository or saved model before use.

It separates evidence about which kind of model fits the study from evidence about whether a particular implementation can be trusted.

model-selection — Skill for Claude Code by Aperivue/medsci-skills

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add Aperivue/medsci-skills --skill model-selection

Clone the repo

git clone --depth 1 https://github.com/Aperivue/medsci-skills

Made for: Claude Code.

Or install medsci-modeling, the plugin that ships this one along with the rest of its 15 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/aperivue/medsci-skills/model-selection/github.svg)](https://agentmods.dev/skills/aperivue/medsci-skills/model-selection)

Your own site

<a href="https://agentmods.dev/skills/aperivue/medsci-skills/model-selection"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/model-selection/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/aperivue/medsci-skills/model-selection"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/model-selection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 63 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,710 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00063	$0.02710
Opus 5.5	$0.00025	$0.01084
Sonnet 5.5	$0.00013	$0.00542
Haiku 4.5	$0.00006	$0.00271

Measured 17d ago against content hash d800d0379f2b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

model-selection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 17d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/check_model_provenance_challenge/verify.sh, scripts/check_model_provenance.py, tests/test_model_provenance.sh), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/model-selection/SKILL.md · 143 lines

How it starts

The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Model-Selection Skill

Two questions, in this order. Phases 1–3 answer a literature question with a stable answer: which architecture family suits the task. Phases 4–6 answer a provenance question: which concrete artifact — repository, revision, checkpoint — will be run, and what its numbers may claim. The skill writes decision notes and runs one stdlib gate; it never downloads, runs, trains or benchmarks a model, and it describes archetypes, not a live SOTA leaderboard.

Elsewhere: building the repo → /model-scaffold; profiling data and planning preprocessing → /imaging-data; validation design, metrics, uncertainty, explainability → /model-assessment; documenting a model you built → /model-card; an LLM/MLLM, including benchmark contamination in that setting → /mllm-eval; AI-vs-expert reader study → /design-ai-benchmarking.

Workflow

Phase 1 — Frame the question

State the task (classification / segmentation / detection / synthesis / transfer), the modality and dimensionality (2-D vs 3-D volume), the labelled-data scale (events / structures, not just images), label availability (lots / few / unlabelled pool), and the constraints (class imbalance, small structures, interpretability, deployment compute).

Phase 2 — Walk the decision tree, read the family card

Follow ${CLAUDE_SKILL_DIR}/references/index.md (task → constraints → default pick). It routes to one card; each gives the source paper, core idea, when-to-use, medical-imaging use, reference implementation, and the typical validation setup for that class.

Card	Families
`${CLAUDE_SKILL_DIR}/references/classification.md`	ResNet / DenseNet / EfficientNet / Inception / ConvNeXt / ViT / Swin / DeiT
`${CLAUDE_SKILL_DIR}/references/segmentation.md`	U-Net / 3-D U-Net / V-Net / Attention & Residual U-Net / nnU-Net (+ ResEnc, MedNeXt, STU-Net) / SegResNet / Swin-UNETR / Mask R-CNN
`${CLAUDE_SKILL_DIR}/references/detection.md`	nnDetection / Faster R-CNN + FPN / Mask R-CNN / RetinaNet / YOLO / DETR
`${CLAUDE_SKILL_DIR}/references/synthesis.md`	Pix2Pix / CycleGAN / SPADE / diffusion (DDPM, latent) / VAE / fastMRI reconstruction
`${CLAUDE_SKILL_DIR}/references/foundation_models.md`	SAM / MedSAM / MedSAM2 / nnInteractive / VISTA3D / TotalSegmentator / SegVol / BiomedCLIP / DINO / MAE / SimCLR / MoCo
`${CLAUDE_SKILL_DIR}/references/graph.md`	GCN / GraphSAGE / GAT / GIN / BrainGNN for connectomes and population graphs (PyTorch Geometric / DGL; not scaffolded by `/model-scaffold`)

Read the full file on GitHub · 143 lines

Files

What ships with it

18 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

17d ago Changed · +12 lines d800d0379f2b
19d ago First seen · 131 lines · 63 tokens per session scan A ecfc38aef655

Subscribe to this mod's changes

model-selection is a skill published in the GitHub repository Aperivue/medsci-skills (346 stars, last pushed yesterday), licensed MIT. It adds 63 tokens to every session and 2,710 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-01.

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