radiology-skills is a collection of Codex skills for medical-imaging research, covering radiomics, deep learning, imaging genomics, multimodal studies, statistics, validation, and scientific publishing. It is intended for researchers who design, analyze, write, and submit medical-imaging AI studies. The catalogue entries are its modular research workflows and specialist advisory skills.
Skill Claude CodeCodex
Multi-source literature, dataset, and local-corpus search for imaging research, with deduplication, identifier verification, reference management, and survey/RAG workflow support. Searches PubMed, arXiv, Crossref, and imaging dataset registries (TCIA, GEO, cBioPortal, OpenNeuro, Grand Challenge, Medical Segmentation…
Skill Claude CodeCodex
Plan, run, and report imaging biostatistics the way Radiology (RSNA) reviewers expect — diagnostic accuracy with CIs, ROC/AUC comparison (DeLong/bootstrap), reader agreement (Cohen/Fleiss kappa, ICC, Bland-Altman), multi-reader multi-case (MRMC) studies, calibration and decision-curve analysis, multiplicity control…
Skill Claude CodeCodex
Move a validated imaging model from retrospective performance toward clinical use — design the clinical-use scenario, model outputs, reader (multi-reader multi-case) studies of radiologist-vs-AI-vs-radiologist+AI, decision thresholds tied to actions, net-benefit/decision-curve framing, prospective and real-world…
Skill Claude CodeCodex
Draft or rebuild imaging-AI / radiomics / radiogenomics manuscript sections for Radiology (RSNA), Nature-portfolio/npj, European Radiology, NEJM, Science, or Lancet-family style from author-provided results, figures, notes, or Chinese drafts. Enforces the target venue's manuscript shape: Radiology Summary/Key Results…
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: