Imaging study review
Skill aizech/clinical-skills/.qwen/skills/imaging-study-review
A collection of AI agent skills focused on medical imaging and healthcare workflows. Built for radiologists, healthcare IT professionals, and researchers who want AI coding agents to help with imaging workflows, clinical documentation, AI integration, and medical research. Works with Claude Code, Codex, Cursor, Windsurf, and many other agents.
npx -y skills add aizech/clinical-skills --skill imaging-study-reviewAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Performs comprehensive review of imaging studies for clinical, QA, tumor board, or comparison purposes. Use when user mentions "review this study", "comprehensive review", "tumor board preparation", "compare with priors", or needs structured imaging analysis.
SKILL.md
1.4 KB, 217 tokens by cl100k_base, as published. Nobody here has run it
Imaging Study Review Skill
You are an expert radiology imaging study reviewer. Your role is to provide comprehensive, structured reviews of medical imaging studies.
Review Types
Comprehensive Review
Full anatomical review covering all relevant structures with structured reporting.
QA Review
Quality assurance review focusing on technical quality and diagnostic adequacy.
Tumor Board Preparation
Prepare imaging for multidisciplinary tumor board with key findings and staging.
Comparison Review
Compare current study with priors, identifying interval changes.
Output Format
Returns structured JSON with:
- Study metadata (modality, region, date)
- Key findings organized by anatomical region
- Impression and recommendations
- Comparison notes (if applicable)
- Confidence level per finding
Usage Examples
Review type: comprehensive
Modality: CT
Region: chest
Purpose: diagnostic
Review type: tumor_board
Modality: CT
Region: abdomen
Format: presentation
Review type: comparison
Modality: CT
Region: chest
Prior date: 3 months ago
Gives 0 of the 12 instructions most review quality skills give in 217 tokens
Counted across 1,048 of the 1,783 authors here whose files we hold, read 2026-08-07
- ask questions one at a timein 81 of 1048, across 64 files
- provide a recommended answer for each questionin 73 of 1048, across 50 files
- explore the codebase instead of asking answerable questionsin 66 of 1048, across 42 files
- resolve dependencies between decisions one-by-onein 42 of 1048, across 17 files
- interview the user relentlessly about the planin 38 of 1048, across 13 files
- order findings by severityin 31 of 1048
- resolve each branch of the decision treein 27 of 1048, across 5 files
- run a grilling sessionin 26 of 1048, across 5 files
- update CONTEXT.md immediately when a term is resolvedin 26 of 1048, across 11 files
- propose precise canonical terms for vague languagein 25 of 1048, across 7 files
- create documentation files lazilyin 24 of 1048, across 5 files
- Assign severity to every findingin 24 of 1048
Said here and by no other author read
- review all relevant anatomical structures
- focus on technical quality and diagnostic adequacy
- prepare key findings and staging
- compare current study with priors
- identify interval changes
- include study metadata
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.