gds-detective

Detect multi-pattern anomalies in geometric data spaces using GDS detection recipes.

Updated Apr 9, 2026
One-click install
npx skills add https://github.com/hypertopos/hypertopos-skills --skill gds-detective
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: gds-detective
Source: https://github.com/hypertopos/hypertopos-skills/tree/main/gds-detective
Command: npx skills add https://github.com/hypertopos/hypertopos-skills --skill gds-detective

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Detect anomalies in geometric data spaces by applying structured detection recipes that surface multi-pattern signals beyond single-feature profiling, enabling faster root cause understanding and robust alerting.

Core Features & Use Cases

  • Event anomaly rate recipes to surface joint deviations across patterns
  • Composite subgroup analysis to catch Simpson's paradox
  • Temporal burst detection, trajectory and drift analyses, and cross-pattern investigations
  • Passive_scan for confirmation and multi-pattern risk assessment
  • Use Case: surfaces anomalies in sphere data and supplier networks to trigger investigations.

Quick Start

Invoke gds-detective on your current sphere to run detection recipes and surface cross-pattern anomalies.

Frequently Asked Questions about gds-detective

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

FAQPage Schema
How do I detect multi-pattern anomalies in geometric data spaces?

Detect temporal bursts and drift in geometric data by applying structured detection recipes that analyze trajectory shapes and temporal shifts. These recipes surface multi-pattern signals to trigger cross-pattern investigations and robust alerting.

What is the best way to investigate cross-pattern anomalies and neighbor contamination?

Catch Simpson's paradox using composite subgroup analysis recipes that evaluate joint deviations across patterns. These recipes surface composite anomalies in sphere data and supplier networks that single-feature profiling misses.

Do I need a specific MCP server to run GDS detection recipes?

Run sphere_overview alerts by invoking gds-detective on your current sphere to execute detection recipes. This surfaces cross-pattern anomalies, segment shifts, and trajectory shapes to trigger end-to-end investigation workflows.

How do I identify temporal bursts and drift in sphere data?

Identify temporal bursts and drift in sphere data by applying structured GDS detection recipes for trajectory analysis and temporal shifts. These recipes surface multi-pattern signals that trigger robust alerting and cross-pattern investigations.

Can I use composite subgroup analysis to catch Simpson's paradox in supplier networks?

Use composite subgroup analysis recipes to catch Simpson's paradox by evaluating joint deviations across patterns in supplier networks. This surfaces composite anomalies that single-feature profiling misses, enabling faster root cause understanding.