model-scaffold SKILL.md skillA
model-scaffold from aperivue/medsci-skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add aperivue/medsci-skills/model-scaffold@git:20261004.5b44a65Pins git:20261004.5b44a65 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- aperivue/medsci-skills
- Path
skills/model-scaffold/SKILL.md- Versions recorded
- 9
- Latest version
- git:20261004.5b44a65, committed Oct 4, 2026
- First version recorded
- Jun 28, 2026
- Size
- 12,452 bytes
- Repository stars
- 366
- License
- MIT
Use when you need a runnable PyTorch training repo for a medical-imaging task (segmentation, classification, detection, synthesis, self-supervised, or fine-tuning a pretrained backbone). Emits a patient-level seed-locked split, train/evaluate scripts, config and a Methods stub.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Model-Scaffold Skill ## Purpose This skill stamps out a **runnable PyTorch training repo** for a medical-imaging task — `--task` **segmentation** (U-Net), **classification** (CNN / `timm` backbone), **detection** (torchvision Faster R-CNN / FPN), **synthesis** (Pix2Pix generator + PatchGAN), **ssl** (SimCLR encoder), or **finetune** (transfer-learning a pretrained backbone with a frozen→unfrozen schedule + a provenance record) — with the reproducibility guarantees **baked in by construction** — so the build is leakage-safe and reproducible before a single epoch runs. It is the imaging analogue of how `/analyze-stats` generates runnable statistical code: the generator produces the repo, you run the training on your GPU / Colab, and the lane's deterministic gates verify the network-free parts. It is the **missing middle link** in the lane: `/model-selection` (choose) → **model-scaffold (build)** → `/model-assessment` (validate the split / design, compute metrics) → `/analyze-stats` → `/write-paper` + `/check-reporting` (publish). It **integrates** MONAI / nnU-Net / TorchIO (referenced in the generated `requirements.txt`); it does not reimplement them. ## When to use …
Read the whole file at its exact version.
How to install
mdr add aperivue/medsci-skills/model-scaffold@sha256:d0c0d20bbeab5a42The line at the top of this page pins the label git:20261004.5b44a65, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_2wlwtvxuqms66kxd)
4 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 9 versions of this file, between Jun 28, 2026 and Oct 4, 2026, about one every 12 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20261004.5b44a65 latest | 2026-10-04 | 5b44a65 | 12,452 B | A | view · diff |
| git:20261003.87332ea | 2026-10-03 | 87332ea | 12,412 B | A | view · diff |
| git:20261002.5344242 | 2026-10-02 | 5344242 | 10,703 B | A | view · diff |
| git:20260930.c8ed96c | 2026-09-30 | c8ed96c | 8,704 B | A | view · diff |
| git:20260930.30387c2 | 2026-09-30 | 30387c2 | 8,719 B | A | view · diff |
| git:20260704.f343c85 | 2026-07-04 | f343c85 | 9,527 B | A | view · diff |
| git:20260704.5492182 | 2026-07-04 | 5492182 | 9,237 B | A | view · diff |
| git:20260628.244e415 | 2026-06-28 | 244e415 | 7,686 B | A | view · diff |
| git:20260628.90a6b58 | 2026-06-28 | 90a6b58 | 7,048 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (12452 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
aperivue/medsci-skills · 366 stars · license MIT · pushed 2026-11-02 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_2wlwtvxuqms66kxd GET https://markdownregistry.com/api/v1/resolve?ref=aperivue/medsci-skills/model-scaffold GET https://markdownregistry.com/api/v1/blob/d0c0d20bbeab5a428ce1054cf2dbba42c37a7248eb0734295a4cdd810c14273b
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.