skills.rest
ruskibeats
ruskibeatsCOMMUNITY
Explore all skills by this author →
GitHub

graph-edge-confidence-provenance

Integrate confidence components and detector versioning into graph edge provenance tracking.

Updated May 15, 2026
Search Tags:#developer tool#confidence scoring#graph database#provenance tracking#edge detector
One-click install
npx skills add https://github.com/ruskibeats/t1d --skill graph-edge-confidence-provenance
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: graph-edge-confidence-provenance
Source: https://github.com/ruskibeats/t1d/tree/main/.pi/skills-archive/graph-edge-confidence-provenance
Command: npx skills add https://github.com/ruskibeats/t1d --skill graph-edge-confidence-provenance

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires ConfidenceComponents, detector_versions, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill aids in building and extending graph edge provenance tracking, ensuring accurate and transparent confidence scores for edge detectors.

Core Features & Use Cases

  • Provenance Tracking: Integrates confidence components, detector versioning, and provenance payload construction.
  • Edge Detector Support: Facilitates the creation of new graph edge detectors and the addition of provenance tracking to existing ones.
  • Confidence Components: Decomposes confidence scores into interpretable components for better understanding and analysis.
  • Use Case: When developing a new edge detector for a graph database, this Skill can help in implementing a robust confidence scoring system.

Quick Start

Add the new edge detector 'exercise_impact' with confidence components and provenance tracking.

Frequently Asked Questions about graph-edge-confidence-provenance

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

FAQPage Schema
How do I track provenance for graph database edges with confidence scores?▼

Graph edge provenance tracking integrates confidence components and detector versioning into edge properties, constructing provenance payloads that decompose scores into interpretable parts for accurate tracking.

How do I add provenance tracking to an existing graph edge detector?▼

You add provenance tracking by integrating confidence components and detector versioning into existing edge detectors, constructing payloads that decompose confidence scores into interpretable elements for analysis.

What are confidence components in graph edge provenance tracking?▼

Confidence components decompose overall edge confidence scores into interpretable parts, allowing developers to better understand and analyze detector outputs within graph database provenance payloads.

Do I need Python to implement confidence scoring for graph edge detectors?▼

Yes, Python is required to implement confidence scoring and provenance tracking for graph edge detectors, alongside knowledge of graph database edge properties for payload construction.

How do I decompose confidence scores into interpretable components for graph edges?▼

You decompose confidence scores by breaking them down into confidence components within provenance payloads, providing interpretable elements that enhance understanding and analysis of graph edge detector outputs.

When should I use detector versioning for graph edge provenance?▼

Use detector versioning when building or improving edge detectors to ensure accurate, transparent confidence scores, tracking detector versions within provenance payloads for reliable graph analysis.

Related Skills

Explore More Software Engineering Skills→
ruskibeatsruskibeats

graph-provenance-testing

Validate provenance fields and evidence JSON consistency on graph edges.

Community
Advanced
ruskibeatsruskibeats

graph-confidence-scoring

Score graph edge confidence using decomposed components and threshold categorization.

Community
Advanced
Dorpeer95Dorpeer95

edge-signal-aggregator

Aggregate outputs from multiple edge-finding skills into a weighted conviction dashboard.

Community
Advanced
gbasingbasin
46

balls

Decompose complex problems into verifiable sub-questions with confidence scoring.

Community
Advanced