graph-confidence-scoring

What problem does it solve?

This Skill implements transparent confidence scoring for graph edges, ensuring explainable and accurate assessments of observational relationships between health metrics.

Core Features & Use Cases

• Confidence Components: Decomposed components for weighted aggregation, including pattern strength, temporal alignment, effect magnitude, and data quality.
• Threshold Categorization: Categorize confidence scores into 'low', 'medium', or 'high'.
• Provenance Tracking: Build provenance payload for graph edge scoring.
• Use Case: Enhance pattern detection methods for health metrics by providing transparent confidence scores.

Quick Start

Calculate confidence for a graph edge using the graph-confidence-scoring skill.