scoring-engine

What problem does it solve?

This Skill provides a robust system for ranking and comparing items across different categories, even when their underlying data distributions vary significantly. It addresses the challenge of making fair comparisons by normalizing scores and accounting for factors like data freshness and sample size.

Core Features & Use Cases

• Statistical Scoring: Utilizes z-scores, percentiles, and freshness decay for accurate item evaluation.
• Cross-Category Normalization: Enables fair comparison of items across categories with different baselines.
• Confidence Scoring: Incorporates sample size and score variance to provide a confidence level for each score.
• Use Case: Ranking blog posts by engagement, comparing product performance across different market segments, or identifying top-performing content in a media platform.

Quick Start

Use the scoring-engine skill to calculate the score and confidence for a given video's view count and age.