radiology-response
radiology-response is a Peer review & grants skill for AI agents, published by huang-sir1 in the radiology-skills repository on GitHub. Skillspace rates radiology-response C (72/100) and it passed the safety scan; the rating is based on static analysis only.
Draft, audit, and revise point-by-point reviewer response letters for Radiology (RSNA) and imaging-journal revisions. Use when the user has reviewer comments / a major or minor revision / 审稿意见 to answer for an imaging-AI/radiomics/radiogenomics manuscript. Assigns each comment a stable ID, classifies it, maps it to a concrete manuscript action, and ties every claimed change to a specific location — without fabricating experiments, analyses, citations, line numbers, or results. Bilingual-aware (中文作者备注 → English response + Chinese confirmation items).
- 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.
Our review
- Architecture
- 96 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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