Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Measures compositional structure in AI-generated images using the Visual Thinking Lens (VTL) framework. Detects patterns like center lock, radial collapse, and low tension that indicate default-mode model behavior. When patterns are found, generates alternative prompts targeting specific compositional issues.
Uses deterministic metric coordinates instead of subjective feedback, so compositional diagnosis is repeatable and not influenced by the model's priors.
When to use it
- Diagnosing why a generated image looks generic or flat
- Getting re-prompt variants after a center-locked image output
- Checking visual tension and void distribution in AI art
- Iterating on prompts that consistently produce similar layouts
- Validating compositional quality before finalizing a generated image
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: 'Why does this image look so bland and centered?'
- 1Run vtl_probe.py on the image to extract compositional metrics
- 2Check valid and mask_status fields; halt and report failure if either fails
- 3Report the five VTL coordinates and any flags detected
- 4Run vtl_regen.py with the original prompt and metrics to generate re-prompts
- 5Present the selected prompt variant as the primary recommendation with alternatives
VTL coordinates showing CENTER_LOCK flag, plus up to 3 revised prompt variants targeting off-center placement and increased tension