Which image formats are supported by this skill?

The skill supports common web and development image formats, including PNG, JPEG, and WebP, provided they are accessible to the local environment.

Can I use this for real-time video processing?

While optimized for static image analysis, it can be used to process individual frames extracted from video sequences for sequential analysis.

Does this skill require external API keys?

This skill relies on the computer-vision-processor plugin; depending on your local setup and the specific vision model used, you may need relevant local or cloud credentials.

Can this skill be combined with other automation tasks?

Yes, the results from vision processing can be piped into other skills to generate reports, move files, or trigger specific development workflows based on image content.

How does the skill handle multiple objects in one image?

In object detection mode, the skill generates code that returns a comprehensive list of all detected objects along with their specific labels and spatial locations.

Computer Vision Processor

Name: Computer Vision Processor
Author: intent-solutions-io

byintent-solutions-io

Data Science & ML

Automates complex image analysis tasks including object detection, classification, and segmentation directly within the Claude Code environment.

About

The Computer Vision Processor skill empowers Claude to perform sophisticated visual analysis by leveraging specialized computer vision plugins. It translates natural language requests into executable Python code that can identify objects, categorize images, and segment visual data into meaningful regions. This skill is essential for developers and data scientists who need to integrate visual intelligence into their workflows, automate image-based auditing, or rapidly prototype AI-driven visual applications without leaving their terminal.

Key Features

Automated object detection with precise bounding box coordinates
Seamless integration with the /process-vision command
High-accuracy image classification with confidence scoring
Advanced image segmentation for granular region identification
Natural language to Python code generation for vision workflows
0 GitHub stars

Use Cases

Identifying specific components or objects within a massive dataset of images
Automating quality assurance by detecting anomalies in product photographs
Categorizing visual assets into predefined labels for content management

About

Key Features

Automated object detection with precise bounding box coordinates
Seamless integration with the /process-vision command
High-accuracy image classification with confidence scoring
Advanced image segmentation for granular region identification
Natural language to Python code generation for vision workflows
0 GitHub stars

Use Cases

Identifying specific components or objects within a massive dataset of images
Automating quality assurance by detecting anomalies in product photographs
Categorizing visual assets into predefined labels for content management