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Image quality audit

Skill aizech/clinical-skills/.continue/skills/image-quality-audit

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.

Install
npx -y skills add aizech/clinical-skills --skill image-quality-audit

Assembled 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

Assesses medical image quality against clinical standards and identifies optimization opportunities. Use when user mentions "image quality audit", "artifact review", "dose analysis", "protocol deviation", "quality metrics", "diagnostic adequacy", or "technique optimization".

SKILL.md

2.0 KB, as published. Nobody here has run it

Image Quality Audit Skill

Triggers

  • "image quality audit"
  • "artifact review"
  • "dose analysis"
  • "protocol deviation"
  • "quality metrics"
  • "diagnostic adequacy"
  • "technique optimization"

Parameters

  • audit_type (required): Type of quality assessment
    • artifact - Motion, noise, streak artifacts
    • dose - Radiation dose optimization and DRL compliance
    • protocol - Protocol adherence and deviation analysis
    • adequacy - Diagnostic sufficiency for intended purpose
    • technique - Technical parameters review
    • comprehensive - Full quality review
  • modality (required): Imaging modality to audit
  • time_range (optional): Audit period - defaults to last 7 days
  • sample_size (optional): Studies to review - defaults to all in range
  • severity_threshold (optional): Minimum severity to flag

Evaluation Criteria

  • Artifacts: Type, severity (1-5), impact on diagnostic utility
  • Dose: DLP, CTDIvol vs. ACR reference levels, size-adjusted metrics
  • Protocol: Coverage completeness, sequence selection, contrast timing
  • Adequacy: Signal-to-noise, spatial resolution, positioning

Output Format

Returns structured JSON with:

  • Quality metrics summary
  • Severity distribution
  • Contributing factors analysis
  • Improvement recommendations ranked by impact
  • Training priorities for technologist/site issues

Usage Examples

audit_type: artifact
modality: CT
time_range: last_week
severity_threshold: 3

audit_type: dose
modality: CT
time_range: last_month

Standards Reference

  • ACR Physical Parameters for CT, MRI, Ultrasound, Mammography
  • ICRP and ACR dose reference levels
  • modality-specific practice guidelines

Gives 0 of the 12 instructions most audit compliance skills give

Counted across 936 of the 1,487 authors here whose files we hold, read 2026-08-06

  • group findings by severityin 44 of 936
  • Fetch latest guidelines before each reviewin 43 of 936, across 3 files
  • Check files against all fetched rulesin 42 of 936, across 2 files
  • Output findings in terse file:line formatin 41 of 936, across 3 files
  • Ask user which files to review if none specifiedin 41 of 936, across 3 files
  • Read specified files or prompt user for filesin 39 of 936, across 1 file
  • generate the audit reportin 39 of 936, across 36 files
  • assign a severity to every findingin 25 of 936
  • run automated accessibility scansin 23 of 936, across 13 files
  • map findings to WCAG criteriain 20 of 936, across 10 files
  • confirm audit scopein 19 of 936, across 9 files
  • check title tags and meta descriptions for uniquenessin 19 of 936, across 5 files

Said here and by no other author read

  • evaluate artifacts type severity and diagnostic impact
  • evaluate dose against reference levels
  • evaluate protocol adherence and deviations
  • evaluate diagnostic adequacy

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.