vtl-image-analysis

# 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: ```bash python3 scripts/vtl_probe.py <image_path> ``` This returns JSON. Example: ```json { "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: ```bash 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