radiomics-ml SKILL.md skillA
radiomics-ml 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/radiomics-ml@git:20261003.5e119f6Pins git:20261003.5e119f6 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/radiomics-ml/SKILL.md- Versions recorded
- 7
- Latest version
- git:20261003.5e119f6, committed Oct 3, 2026
- First version recorded
- Jul 3, 2026
- Size
- 11,073 bytes
- Repository stars
- 366
- License
- MIT
Use when building or auditing a radiomics or tabular clinical-ML prediction model with a classical learner (LASSO, SVM, random forest, XGBoost and similar). Enforces nested CV, dimensionality control, in-fold feature selection, feature stability, calibration and external validation.
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
# Radiomics / Classical-ML Skill ## Purpose Radiomics + tree-ensemble studies (features → random forest / XGBoost → a clinical outcome) are the **most common solo-doable clinical-ML workflow** — no GPU, no engineer — and the **most commonly over-optimistic**: hundreds-to-thousands of features on tens of patients, hyperparameters tuned on the same folds the performance is reported from, features selected on the whole dataset, unstable features never filtered, and discrimination (AUC) reported without calibration. This skill produces the pipeline correctly and audits an existing one, so the clinical result survives review (Lambin 2017; CLEAR; TRIPOD+AI; PROBAST-AI). It sits beside the imaging-DL lane: where `/model-scaffold` builds a deep network, **radiomics-ml** covers the feature-based classical-ML path. It **integrates** scikit-learn / xgboost / pyradiomics (referenced in the emitted code); it does not reimplement them and never runs a model on real patient data. ## When to use - You have a radiomics or clinical/tabular feature table and want to build a random-forest / XGBoost clinical prediction model that will pass statistical review. …
Read the whole file at its exact version.
How to install
mdr add aperivue/medsci-skills/radiomics-ml@sha256:024ba57ccb1afc22The line at the top of this page pins the label git:20261003.5e119f6, 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_g5seb3psm52yvchk)
3 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 7 versions of this file, between Jul 3, 2026 and Oct 3, 2026, about one every 15 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20261003.5e119f6 latest | 2026-10-03 | 5e119f6 | 11,073 B | A | view · diff |
| git:20261002.30395e8 | 2026-10-02 | 30395e8 | 10,988 B | A | view · diff |
| git:20260930.c8ed96c | 2026-09-30 | c8ed96c | 10,187 B | A | view · diff |
| git:20260930.30387c2 | 2026-09-30 | 30387c2 | 7,560 B | A | view · diff |
| git:20260930.e4f475f | 2026-09-30 | e4f475f | 8,274 B | A | view · diff |
| git:20260703.32d8187 | 2026-07-03 | 32d8187 | 8,273 B | A | view · diff |
| git:20260703.336c8d3 | 2026-07-03 | 336c8d3 | 6,946 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 (11073 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_g5seb3psm52yvchk GET https://markdownregistry.com/api/v1/resolve?ref=aperivue/medsci-skills/radiomics-ml GET https://markdownregistry.com/api/v1/blob/024ba57ccb1afc221901b58e702535811c79901d0fa001b8992af0767ec0bdbe
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.