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demo-ui

Prototype and validate a tri-state UI for AI, Real, and Abstain predictions.

Updated Feb 18, 2026
Search Tags:#calibration#inference#ui-development#demo-ui#tri-state#image-prediction#artifact-backed
One-click install
npx skills add https://github.com/rilical/OpenWorld-AI-Image-Detection --skill demo-ui
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: demo-ui
Source: https://github.com/rilical/OpenWorld-AI-Image-Detection/tree/main/.agents/skills/demo-ui
Command: npx skills add https://github.com/rilical/OpenWorld-AI-Image-Detection --skill demo-ui

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables rapid prototyping and validation of a local demo UI for tri-state image predictions (AI, Real, Abstain), calibration visualization, and residual exploration using saved run artifacts. It is not intended for model training, dataset ingestion, or report generation.

Core Features & Use Cases

  • Route all predictions through shared inference code and display tri-state labels, confidence, and abstention state.
  • Leverage saved run artifacts to compare calibration and selective-risk behavior across UI variations.
  • Use during UI development, testing, and demonstrations without touching training pipelines or data ingestion.

Quick Start

Launch the demo UI locally and load saved run artifacts to visualize tri-state predictions.

Frequently Asked Questions about demo-ui

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

FAQPage Schema
How do I visualize tri-state image predictions for AI, Real, and Abstain classifications?▼

To visualize tri-state image predictions, you can use a local demo UI that routes predictions through shared inference code to display AI, Real, and Abstain labels. It connects to artifact-backed data to show confidence and abstention states.

Can I use this demo UI for model training or dataset ingestion?▼

No, this demo UI is strictly for UI development, testing, and demonstrations. It separates visualization from training pipelines, dataset ingestion, and report generation to focus entirely on validating tri-state predictions.

How do I display confidence and abstention states using saved run artifacts?▼

You display confidence and abstention states by loading saved run artifacts into the local demo UI. The UI leverages artifact-backed data to visualize selective-risk behavior and tri-state prediction outputs.

Does the tri-state demo UI require GPU execution to run locally?▼

No, the tri-state demo UI defaults to CPU-first execution for local prototyping. It applies to UI developers and evaluators building and adjusting demos without needing GPU resources.

Why does my calibration visualization not update when switching between saved runs?▼

Calibration visualization depends on properly loaded saved run artifacts. Ensure the UI is correctly connected to artifact-backed data and shared inference code to compare calibration and selective-risk behavior across UI variations.

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