# huang-sir1/radiology-skills

Repository: https://github.com/huang-sir1/radiology-skills
Canonical: https://ross.abutalabs.com/products/radiology-skills
Language: Python
License: MIT
License Family: permissive
Last push: 2026-07-27T07:34:29+00:00

## Health v2 (maintenance only)
Score: 56/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 94, release rhythm 35, longevity 5
- inputs: {"age_days": 76, "days_push": 37, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1495, forks 17 (observed 2026-08-28T04:04:53.142586+00:00)

## What it is
An open-source collection of 27 Claude/Codex 'skills' (modular rule files) for medical imaging research, covering radiomics, deep learning, imaging genomics, multi-omics fusion, federated learning, statistics, and manuscript/grant writing. It encodes reviewer-level methodological standards from high-impact journals like Radiology and Nature series to help researchers design, validate, and publish imaging AI studies.

## Use cases
- design a radiomics study from CT/MRI data and check feasibility
- audit a deep learning pipeline for data leakage before submission
- check a manuscript against CLAIM, TRIPOD+AI, and CLEAR reporting guidelines
- plan multi-omics fusion or federated learning study design
- translate Chinese drafts and reviewer responses into English submission text
- prepare grant proposals for imaging AI projects
- simulate a strict peer review before submitting to Radiology or Nature Medicine

## When to choose
- you are a clinical research team working on radiomics or medical imaging AI
- you target high-impact journals like Radiology, Lancet Digital Health, or Nature Medicine
- you need reviewer-perspective checks on study design, statistics, and reporting compliance
- you want structured workflows for grant writing and journal submission in medical imaging

## When to avoid
- you need actual image processing or model training code rather than research guidance
- you work outside medical imaging or radiology research
- you want a general-purpose paper writing assistant without domain-specific rules
- you need a tool that runs independently of Claude or Codex agents

## Facets
- artifact type: plugin
- maturity: active
- function: agent-framework, prompt-engineering, documentation, developer-tools, machine-learning, nlp
- domain: artificial-intelligence, machine-learning, healthcare, education, tutorials, developer-tools
- platform: python, cli, cross-platform
- tags: claude-skills, codex-skills, radiology, radiomics, medical-imaging-ai, research-workflows, manuscript-writing, peer-review-simulation, grant-writing, medical-ai

## Member repositories
- huang-sir1/radiology-skills (main) score 56

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:53.142586+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:33:17.148611+00:00, confidence not recorded.
  - readme: https://github.com/huang-sir1/radiology-skills (fetched 2026-08-28T04:04:53.142586+00:00, sha a1b69de14915)
- Data as of 2026-08-30T08:39:29.467469+00:00.