radiology-prereview

radiology-prereview is a Peer review & grants skill for AI agents, published by huang-sir1 in the radiology-skills repository on GitHub. Skillspace rates radiology-prereview C (71/100) and it passed the safety scan; the rating is based on static analysis only.

Run a rigorous pre-submission mock peer review of an imaging-AI / radiomics / radiogenomics manuscript — simulate the methods, statistics, reporting-guideline, figure, citation/claim-verification, and data-sharing reviewer a top journal would assign, and surface the issues that cause desk-reject or major revision before submission. Use when the user wants a mock review, pre-submission audit, "投稿前预审/模拟审稿", "find the holes before a reviewer does", a two-pass abstract/figure/table claim audit, or a readiness check. Returns a reviewer-style report with Blocker / Major / Minor issues, each tied to the manuscript location and the reporting-guideline or methodological risk, plus an editor-style recommendation and a prioritised fix order. Never fabricates compliance or papers over a real weakness.

Grade

C

Quality

71 / 100

Safety

Passed

GitHub stars

819

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

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