vtl-image-analysisMeasure compositional structure in AI-generated images using the Visual Thinking Lens (VTL) framework. Detects default-mode bias (center lock, radial collaps...
Install via ClawdBot CLI:
clawdbot install rusparrish/vtl-image-analysisGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Potentially destructive shell commands in tool definitions
eval(Calls external URL not in known-safe list
https://github.com/rusparrish/Visual-Thinking-LensAI Analysis
The skill executes local Python scripts for image analysis and does not send user data to external servers. The primary risk is the potential for unsafe shell command execution via `eval()` or similar patterns in the referenced external repository, but the skill definition itself shows no credential harvesting, data exfiltration, or hidden instructions.
Audited Apr 18, 2026 · audit v1.0
Generated Mar 21, 2026
Art studios and digital content creators use this skill to automatically analyze images produced by AI tools, identifying compositional defaults like center lock and low tension. It helps ensure diverse and intentional layouts, improving the artistic quality of outputs for projects like marketing visuals or digital art collections.
Educators and students in AI or design courses employ this skill to diagnose why certain prompts yield generic images. By measuring compositional metrics and generating re-prompts, it teaches effective prompt crafting, enhancing learning outcomes in creative AI applications.
Social media managers and influencers use this skill to analyze and refine AI-generated images for posts, ensuring engaging compositions that stand out. It helps avoid repetitive layouts, boosting visual appeal and audience engagement across platforms like Instagram or TikTok.
Game developers integrate this skill into pipelines for generating environment or character art, analyzing compositional structure to prevent bland defaults. It aids in creating more dynamic and varied visual assets, improving game aesthetics and player immersion.
Professional photographers and graphic designers use this skill to evaluate AI-assisted image edits or composites, diagnosing compositional issues like radial collapse. It supports refining layouts for projects such as portfolios or commercial designs, ensuring high-quality outputs.
Offer this skill as a cloud-based service where users upload images for analysis and re-prompt generation via an API. Charge monthly or per-analysis fees, targeting studios and enterprises needing scalable compositional quality control for AI-generated content.
License the skill to AI image generation platforms or software tools as an add-on module. Provide SDKs or plugins for seamless integration, earning revenue through upfront licensing fees or royalties based on user adoption and feature usage.
Provide expert services to organizations for customizing operators, analyzing large image datasets, or training teams on VTL framework usage. Offer workshops and ongoing support, generating revenue from project-based contracts and hourly consulting rates.
💬 Integration Tip
Ensure Python dependencies are installed and scripts are accessible; integrate after image generation steps in workflows for proactive analysis.
Scored Jun 19, 2026
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