detect-ui

Detect Unity UI elements from screenshots and output schema 5.0.0 detection JSON.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/igot-ai/os-twin --skill detect-ui
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: detect-ui
Source: https://github.com/igot-ai/os-twin/tree/main/.agents/skills/roles/game-ui-analyst/detect-ui
Command: npx skills add https://github.com/igot-ai/os-twin --skill detect-ui

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates the extraction of structured UI data from screenshots, returning a machine-readable JSON that feeds Unity UI builders and animation analyzers. This reduces manual UI breakdown work and improves consistency across teams.

Core Features & Use Cases

  • Background classification: detects dim overlays, fullscreen backdrops, and gameplay-only backgrounds.
  • Hierarchy and components: infers parent-child relationships and identifies common UI components (Image, Button, ScrollRect, Toggle, Slider, InputField, Mask, LayoutGroups, etc.).
  • Output format: emits a schema 5.0.0 JSON containing meta, canvas mapping, and a flat objects[] array suitable for downstream tooling.
  • Use cases: QA/UI review, asset planning, UI prototyping, and automated UI documentation.

Quick Start

Analyse a provided UI screenshot to generate a schema 5.0.0 detection JSON.

Frequently Asked Questions about detect-ui

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

FAQPage Schema
How do I extract UI elements from a screenshot for Unity UI builder?▼

Extract UI elements by analyzing a provided screenshot to generate a schema 5.0.0 detection JSON. This output contains meta, canvas mapping, and a flat objects array detailing parent-child relationships for Unity UI builders and animation analyzers.

What Unity UI components can be detected from a screenshot?▼

UI detection identifies common Unity UI components from screenshots, including Image, Button, ScrollRect, Toggle, Slider, InputField, Mask, and LayoutGroups. It also infers parent-child hierarchies and resolves sprites across these elements.

Can I automate UI hierarchy inference and background classification from screenshots?▼

Automate UI hierarchy inference and background classification by providing a screenshot and optional guidance. The detection process classifies dim overlays, fullscreen backdrops, and gameplay-only backgrounds while mapping the UI structure.

Does UI detection from screenshots require any specific dependencies or environments?▼

UI detection requires no external dependencies to process screenshots. You simply provide a detection image and optional background guidance to receive a structured JSON output suitable for automated UI analysis.

What is the output format for automated UI screen analysis?▼

The output format for automated UI screen analysis is a schema 5.0.0 detection JSON. It includes meta information, canvas mapping, and a flat objects array to enable downstream asset planning, QA review, and UI prototyping.

Why use automated UI detection instead of manual screenshot breakdown?▼

Automated UI detection replaces manual screenshot breakdown to reduce human error and improve consistency across teams. It outputs machine-readable JSON for automated UI documentation, enabling faster QA reviews and asset planning.