graph-aggregation-helpers

Aggregate health metrics graph data with reusable Python functions.

Updated May 15, 2026

Search Tags:#aggregation #data analysis #pattern detection #graph analysis #health metrics

Ready to run

npx skills add https://github.com/ruskibeats/t1d --skill graph-aggregation-helpers

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill addresses the challenge of efficiently analyzing health metrics graphs by providing reusable aggregation query patterns.

Core Features & Use Cases

Edge Count Aggregation: Count edges by type for a specified period.
Average Confidence Calculation: Determine the average confidence of edge types.
Recurring Pair Identification: Identify the most confident recurring edge pairs.
Edge Statistics: Retrieve aggregate statistics for graph edges.
Use Case: For a healthcare analytics platform, this Skill can be used to quickly generate insights from health metrics data by analyzing patterns and trends.

Quick Start

Use the graph-aggregation-helpers skill to get edge statistics for a user with ID 123.

Frequently Asked Questions about graph-aggregation-helpers

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

FAQPage Schema

How do I aggregate health metrics graph data to find edge statistics?▼

To aggregate health metrics graph data, you use reusable Python functions to retrieve edge statistics, calculate average confidence levels, and count edges by type for a specified period.

What is the best way to identify recurring patterns in health metrics graph edges?▼

Identifying recurring patterns in health metrics graph edges involves querying for the most confident recurring edge pairs, which highlights significant trends and relationships within the health analytics data.

Can I calculate the average confidence of edge types in a health analytics platform?▼

Yes, you can calculate the average confidence of edge types in a health analytics platform by applying specific aggregation query patterns designed to evaluate and measure edge confidence levels efficiently.

Do I need any external dependencies to use Python functions for graph analysis?▼

No, you do not need external dependencies to use these Python functions for graph analysis. The Skill operates independently without requiring additional packages or frameworks to perform data aggregation.

How do I count edges by type for a specified period in health metrics data?▼

You count edges by type for a specified period in health metrics data by executing targeted edge count aggregation queries, which provides a summary of edge type frequencies over your chosen timeframe.