What computer vision tasks can Claude perform with this skill?

Claude can perform object detection to find items in images, image classification to categorize scenes, and image segmentation to identify specific regions.

Do I need to write the computer vision code myself?

No, this skill allows Claude to generate the appropriate Python code and execute the /process-vision command based on your natural language requests.

Is this skill part of the Nixtla plugin suite?

Yes, it is designed to work as an adapter for the computer-vision-processor within the Nixtla plugins repository.

What are the trigger terms for this skill?

Common triggers include 'analyze image', 'object detection', 'computer vision', 'process image', or simply providing an image and asking for insights.

Computer Vision Adapter

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

byintent-solutions-io

Data Science & ML

Analyzes and processes images using advanced computer vision techniques like object detection, classification, and segmentation.

About

This skill empowers Claude to leverage sophisticated computer vision capabilities by automating the interaction with the computer-vision-processor plugin. It allows users to perform complex visual analysis tasks—such as identifying specific objects, categorizing images, and segmenting visual regions—through natural language commands. By generating and executing the necessary Python code and utilizing the /process-vision command, it streamlines visual data workflows and provides detailed, actionable insights directly within the development environment.

Key Features

Image classification with probabilistic confidence scores
Direct integration with the /process-vision command
Automated object detection with bounding box coordinates
Regional image segmentation for detailed visual analysis
0 GitHub stars
Seamless Python code generation for vision workflows

Use Cases

Extracting specific regions of interest from complex visual scenes for further processing
Automating the categorization of large image datasets for machine learning training
Identifying and counting specific objects or pedestrians in surveillance or stock imagery

About

Key Features

Image classification with probabilistic confidence scores
Direct integration with the /process-vision command
Automated object detection with bounding box coordinates
Regional image segmentation for detailed visual analysis
0 GitHub stars
Seamless Python code generation for vision workflows

Use Cases

Extracting specific regions of interest from complex visual scenes for further processing
Automating the categorization of large image datasets for machine learning training
Identifying and counting specific objects or pedestrians in surveillance or stock imagery