radiology-skills

radiology-skills is an Academic writing & papers skill for AI agents, published by huang-sir1 in the radiology-skills repository on GitHub. Skillspace rates radiology-skills C (72/100) and it passed the safety scan; the rating is based on static analysis only.

Use when planning, auditing, writing, or revising radiomics, medical imaging AI, and radiology deep learning studies for Radiology/RSNA, Nature-portfolio, Lancet, Cell, npj, European Radiology, or similar venues. Trigger for research frontiers, literature, CT/MRI/PET/ultrasound datasets, ROI/masks/segmentation annotation, radiomics features, CNN/Transformer/foundation models, trustworthy AI, FUTURE-AI, uncertainty/OOD/interpretability, radiogenomics and multi-omics mechanisms, statistics, figures, pre-submission review, reproducibility, multicenter validation, public datasets, ethics/privacy, clinical translation, validation/leakage, CLAIM/CLEAR/RQS/IBSI/TRIPOD+AI/PROBAST+AI/STARD-AI, manuscript writing, journal selection, NSFC/provincial/international grants, reviewer response, and Chinese-English support.

Grade

C

Quality

72 / 100

Safety

Passed

GitHub stars

819

Unproven: rated by static analysis only, not yet compared head-to-head.

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

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