vtl-image-analysis（VTL 图像分析）

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:

python3 scripts/vtl_probe.py <image_path>

This returns JSON. Example:

{
  "valid": true,
  "mask_status": "PASS",
  "delta_x": -0.027,
  "delta_y": 0.008,
  "r_v": 0.875,
  "rho_r": 12.4,
  "dRC": 0.40,
  "dRC_label": "mass-dominant",
  "k_var": 1.12,
  "infl_density": 0.16,
  "flags": ["CENTER_LOCK"]
}

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.

Step 2 — Report Coordinates

Report the five coordinates plainly:

VTL ANALYSIS
────────────────────────────────
Placement   Δx={delta_x}  Δy={delta_y}
Void        rᵥ={r_v}
Packing     ρᵣ={rho_r}
Radial      dRC={dRC}  [{dRC_label}]
Tension     k_var={k_var}

FLAGS: {flags or NONE}

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

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

python3 scripts/vtl_regen.py \
  --prompt "USER'S ORIGINAL PROMPT" \
  --metrics <path_to_metrics.json> \
  --out prompts.json

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."

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.

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