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image-quality-audit

Skill1 starsChanged 22 days agoMITView on GitHub

Use when scoring an image for composition quality, clarity, and visual hierarchy.

1 other project uses a version of this.

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The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill to produce numeric quality scores for an image.

    line 8

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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Files
SKILL.md

Image Quality Audit

Use this skill to produce numeric quality scores for an image.

Evals

Should trigger

  • "score this image's composition"
  • "rate the visual hierarchy of this photo"
  • "is this image well composed?"
  • "grade the clarity of this render"

Should not trigger

  • "turn this image into a prompt" → use reverse-prompt
  • "review this app screenshot for usability" → use screenshot-ui-review
  • "what objects are in this image?" → use visual-reasoning

Core workflow

  1. Run visual-reasoning pipeline
  2. Score each dimension independently
  3. Compute overall score
  4. Return structured score report

Scores (0.0 – 1.0)

DimensionWhat it measures
composition_scoreRule-of-thirds alignment, balance, visual weight
clarity_scoreSharpness, depth coherence, noise level
visual_hierarchy_scoreClear subject, supporting elements, background
style_consistencyPalette coherence, tonal consistency
overallWeighted average

Output

{
  "composition_score": 0.0,
  "clarity_score": 0.0,
  "visual_hierarchy_score": 0.0,
  "style_consistency": 0.0,
  "overall": 0.0,
  "notes": ["string"]
}

Use cases

  • Comparing generated image batches
  • Flagging low-quality outputs before delivery
  • Detecting style drift across a series

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