ui-extractor

Extract foreground UI elements from images with OpenCV-based background removal.

11|4|Updated Mar 3, 2026
One-click install
npx skills add https://github.com/zrong/skills --skill ui-extractor
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ui-extractor
Source: https://github.com/zrong/skills/tree/main/ui-extractor
Command: npx skills add https://github.com/zrong/skills --skill ui-extractor

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires opencv-python-headless, numpy, pillow, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill solves the time-consuming challenge of manually cropping UI components from design mockups or screenshots, especially when those elements are trapped against complex backgrounds like checkerboards or green screens.

Core Features & Use Cases

  • Intelligent Background Removal: Automatically detects and removes checkerboard, green, blue, white, or black backgrounds using advanced computer vision.
  • Component Isolation: Automatically identifies, crops, and saves individual UI elements as transparent PNGs with associated metadata.
  • Perspective Correction: Fixes tilted or skewed UI screenshots to ensure elements are perfectly aligned for design systems.

Quick Start

Use the ui-extractor skill to remove the background from the image at path design-mockup.png and save the individual components.

Frequently Asked Questions about ui-extractor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automatically remove a checkerboard background from a UI screenshot?

Automated background removal from UI screenshots detects checkerboard, chroma key, white, or black backgrounds using OpenCV pipelines, isolating elements to generate transparent PNG assets without manual cropping.

How does computer vision segmentation isolate individual UI elements from design mockups?

Computer vision segmentation isolates UI elements from design mockups by detecting foreground components against complex backgrounds, automatically cropping and saving each element as an individual transparent PNG with structural metadata.

Can I fix tilted or skewed UI screenshots before extracting components?

Yes, perspective correction fixes tilted or skewed UI screenshots to ensure elements are perfectly aligned for design systems before isolating and saving them as transparent PNGs.

Does OpenCV work with Python for automated background detection and removal?

Yes, OpenCV with Python supports automated background detection and removal, utilizing computer vision pipelines to process static images, remove complex backgrounds, and extract UI elements.

What is the best way to extract multiple UI components from a single static image?

The best way to extract multiple UI components is using automated computer vision pipelines that detect backgrounds, apply perspective correction, and segment individual elements into transparent PNGs with associated metadata.

Why does background removal fail on UI elements with complex chroma key backgrounds?

Background removal may fail on complex chroma key backgrounds if the automated detection cannot distinguish foreground UI elements from the background, limiting the effectiveness of the OpenCV segmentation pipeline.