graph-confidence-scoring

Score graph edge confidence using decomposed components and threshold categorization.

Updated May 15, 2026

Search Tags:#data analysis #confidence scoring #graph analysis #provenance tracking #health metrics

Ready to run

npx skills add https://github.com/ruskibeats/t1d --skill graph-confidence-scoring

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: graph-confidence-scoring
Source: https://github.com/ruskibeats/t1d/tree/main/.pi/skills-archive/graph-confidence-scoring
Command: npx skills add https://github.com/ruskibeats/t1d --skill graph-confidence-scoring

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

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.

Frequently Asked Questions about graph-confidence-scoring

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

FAQPage Schema

How do I calculate confidence scores for graph edges in health metric analysis?▼

To calculate confidence scores for graph edges in health metric analysis, decompose components like pattern strength, temporal alignment, effect magnitude, and data quality for weighted aggregation. This approach categorizes scores into low, medium, or high thresholds to ensure transparent observational assessments.

What is provenance tracking for graph edge scoring?▼

Provenance tracking for graph edge scoring is the process of building a payload that records the decomposed components and weighted aggregation steps used. It ensures transparent and explainable confidence assessments for observational relationships between health metrics.

How do I categorize confidence scores into low, medium, or high thresholds?▼

You categorize confidence scores into low, medium, or high thresholds by applying threshold categorization rules to the weighted aggregation results. This decomposed component method ensures accurate and explainable assessments for health metric graph edges.

Does confidence scoring for health metrics require specific data quality components?▼

Yes, confidence scoring for health metrics requires implementing specific decomposed components, including pattern strength, temporal alignment, effect magnitude, and data quality. These components are necessary for the weighted aggregation process that determines the final graph edge score.

What's the best way to implement transparent confidence scoring for graph analysis?▼

The best way to implement transparent confidence scoring for graph analysis is to utilize decomposed components for weighted aggregation combined with provenance tracking. This method ensures explainable and accurate assessments of observational relationships between health metrics.

Why use decomposed components for weighted aggregation in graph edge scoring?▼

Using decomposed components for weighted aggregation in graph edge scoring ensures transparent and explainable assessments of observational relationships. Evaluating pattern strength, temporal alignment, effect magnitude, and data quality individually prevents opaque scoring for health metrics.