radiology-crossmodal-mapping
radiology-crossmodal-mapping is a Bioinformatics & life sciences skill for AI agents, published by huang-sir1 in the radiology-skills repository on GitHub. Skillspace rates radiology-crossmodal-mapping C (73/100) and it passed the safety scan; the rating is based on static analysis only.
Use when an imaging study must map radiology phenotypes or habitats to single-cell, spatial-omics, or pathology-derived cell states across patients, lesions, specimens, regions, or time points. Designs paired, weakly paired, or unpaired cross-modal mapping; audits alignment, deconvolution, label transfer, contrastive learning, validation, scale mismatch, and biological-claim limits. Never treats unpaired public omics as direct patient-level mechanism proof.
- Grade
- C
- Quality
- 73 / 100
- Safety
- Passed
- GitHub stars
- 1,707
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
- 97 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
Not checked yet
- Category is a classifier prediction; no model has read this skill 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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