VTL Image Analysis

Use this skill whenever a user asks to analyze, diagnose, or improve a
generated image's composition. Also invoke it proactively after image
generation if the user has requested better compositional quality.

When to Use

• - User says "analyze this image", "why does this look generic/flat/boring"
• User asks to improve a generated image's composition
• After generating an image with openai-image-gen or similar skills
• User asks why their prompts aren't producing interesting layouts

Step 1 — Measure

Run the probe script on the image:

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This returns JSON. Example:
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HARD STOP — Refusal Gate

Before reporting any results, check valid and mask_status.

If valid is false OR mask_status is "FAIL":

"VTL measurement failed: [error message]. The image does not have sufficient

structural signal for reliable compositional analysis. Try a different image

or one with more defined edges and contrast."

Stop here. Do not report coordinates. Do not generate re-prompts.

If mask_status is "WARN":

"VTL measurement returned low-confidence results (sparse structural signal).

Coordinates are reported but treat them as indicative, not definitive."

Then continue with the caveat attached to all outputs.

This refusal is non-negotiable. Fabricating a compositional reading from a
failed measurement produces false diagnosis. The framework is deterministic
by design — an uncertain measurement is reported as uncertain, not smoothed over.

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Step 2 — Report Coordinates

Report the five coordinates plainly:

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Step 3 — Generate Re-Prompt (if flags present)

Run the regen script with the user's original prompt and the metrics output:

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This selects operators from operators.yaml based on which flags fired and
returns up to 3 prompt variants. Report the selected variant as the primary
recommendation and offer the alternatives.

If no flags fired, report: "No default-mode patterns detected. Coordinates are
within normal range."

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Operator Logic

Operators live in operators.yaml. They are rule-based — triggers are evaluated
deterministically against the metric values. The AI does not invent or modify
operators. If a trigger fires, the patch is applied. If not, it isn't.

Do not override operator logic. Do not substitute your own re-prompt language
for what the operator specifies. The operators are the prescription layer —
they are the operator's responsibility, not the AI's improvisation.

If the user wants to modify re-prompt behavior, direct them to edit operators.yaml.

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Notes

• - Metrics describe compositional coordinates, not quality. CENTER_LOCK is not

"bad" — it's a signal that the model defaulted. A portrait photographer choosing center composition is authorship. An AI doing it on every prompt regardless of content is prior behavior. VTL measures the difference.

• - dRC requires radial eligibility. If mass centroid is very close to frame

center, dRC is labeled "dual-center" — report the label, not a number interpretation.

• - Full metric definitions: references/vtl-metrics.md
• Full framework: https://github.com/rusparrish/Visual-Thinking-Lens
• Author: Russell Parrish — https://artistinfluencer.com