radiology-data
radiology-data is an Academic writing & papers skill for AI agents, published by huang-sir1 in the radiology-skills repository on GitHub. Skillspace rates radiology-data C (71/100) and it passed the safety scan; the rating is based on static analysis only.
Prepare and audit Data/Code Availability statements, DICOM de-identification plans, repository selection, dataset citations, and FAIR/sharing checks for Radiology (RSNA) and Nature-portfolio imaging+omics submissions. Use when the user needs a data availability statement, must de-identify DICOM imaging, choose a repository (TCIA, Zenodo, GEO, dbGaP, EGA), share code/models, write dataset citations, check FAIR compliance, or needs Extended Data / Supplementary Information / Source Data planning for a Nature-family journal — including controlled-access genomics for radiogenomics. Bilingual-aware (中文作者备注 → submission-ready English). Never overstates availability or invents accessions.
- Grade
- C
- Quality
- 71 / 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
- 94 / 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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