gds-investigator

Diagnose anomalies in GDS patterns and trace root causes to evidence-backed reports.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Anomaly investigation, root-cause tracing, and entity deep-dive within GDS geometric analysis to produce actionable findings supported by evidence chains.

Core Features & Use Cases

  • Investigate anomalies by identifying driving dimensions, reconstructing incident narratives, and linking findings back to detections.
  • Perform root-cause tracing with explain_anomaly, dive_solid, and trace_root_cause to expose witness dimensions, contagion paths, and hub patterns.
  • Generate structured reports with remediation steps and an evidence chain to guide incident response and decision making.

Quick Start

Initiate an anomaly investigation by tracing root causes and generating an evidence-backed report.

Frequently Asked Questions about gds-investigator

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

FAQPage Schema
How do I trace the root cause of an anomaly in geometric data?

Root-cause tracing in geometric data spaces identifies driving dimensions and reconstructs incident narratives using explain_anomaly and trace_root_cause. It exposes witness dimensions and contagion paths to link findings back to original detections.

What is an entity deep-dive in geometric analysis?

An entity deep-dive in geometric analysis is a diagnostic process performed with the dive_solid and entity_flow tools. It exposes hub patterns and counterparties within geometric data spaces to produce actionable findings supported by evidence chains.

How do I investigate anomalies to generate an evidence-backed report?

Investigating anomalies involves applying tools like explain_anomaly and find_counterparties to diagnose geometric data spaces. This generates a structured report containing an evidence chain, incident narratives, and remediation steps to guide decision making.

Do I need the hypertopos MCP server to perform root-cause tracing?

Yes, you need access to the hypertopos MCP server to perform root-cause tracing. The server provides the required tools, including dive_solid, trace_root_cause, and entity_flow, which are necessary to execute the investigation.

Can I find counterparties and contagion paths when investigating geometric data anomalies?

Yes, you can find counterparties and contagion paths when investigating geometric data anomalies. The investigation uses find_counterparties and entity_flow to expose hub patterns and map how incidents spread through witness dimensions.