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gds-scanner

Detect cross-pattern discrepancies, trajectory anomalies, neighbor contamination, and segment shifts.

Updated Apr 9, 2026
Search Tags:#trajectory#anomaly-detection#gds#cross-pattern#neighbor-contamination#segment-shift
One-click install
npx skills add https://github.com/hypertopos/hypertopos-skills --skill gds-scanner
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gds-scanner
Source: https://github.com/hypertopos/hypertopos-skills/tree/main/gds-scanner
Command: npx skills add https://github.com/hypertopos/hypertopos-skills --skill gds-scanner

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Detects advanced anomalies that escape standard scans by integrating cross-pattern discrepancy checks, geometric neighbor contamination signals, non-linear temporal trajectories, and population-segment shifts in multi-pattern spheres.

Core Features & Use Cases

  • Cross-pattern discrepancy: surface entities anomalous in one pattern but normal in another.
  • Trajectory anomaly: identify non-linear temporal paths with near-zero displacement that simple drift misses.
  • Neighbor contamination: detect normal entities whose neighbors are anomalous, revealing diffuse risks.
  • Population segment shift: detect shifts in aggregated segments (nation, region, category) across the sphere.
  • Alias-aware scans: perform alias-specific anomaly checks when aliases exist to avoid missed signals.

Use cases include analyzing a sphere where several patterns overlap, identifying hidden relationships across time, space, and categories, and guiding follow-up investigations with targeted entity lists.

Quick Start

Run a cross-pattern and trajectory scan on a multi-pattern sphere to surface hidden anomalies.

Frequently Asked Questions about gds-scanner

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

FAQPage Schema
How do I detect cross-pattern discrepancies in a multi-pattern sphere?▼

Cross-pattern discrepancy scans surface entities anomalous in one pattern but normal in another. Run a scan to identify hidden risks in overlapping patterns and generate targeted entity lists for follow-up investigation.

What is trajectory anomaly detection and how does it find non-linear temporal paths?▼

Trajectory anomaly detection identifies non-linear temporal paths with near-zero displacement that simple drift misses. Perform a trajectory scan to surface hidden relationships across time sequences in multi-pattern spheres.

Can I detect neighbor contamination where normal entities have anomalous neighbors?▼

Neighbor contamination detection identifies normal entities whose neighbors are anomalous, revealing diffuse risks. Run a neighbor contamination scan to surface these hidden signals in your overlapping pattern data.

How do I detect population segment shifts across nations, regions, or categories?▼

Population segment shift scans detect shifts in aggregated segments like nations, regions, or categories across the sphere. Configure segment parameters to identify these aggregate-level anomalies in categorical attributes.

Does this anomaly detection support alias-specific checks to avoid missed signals?▼

Alias-aware scans perform alias-specific anomaly checks when aliases exist to avoid missed signals. Configure your scan parameters to account for aliases and surface discrepancies across all overlapping patterns.

What are the limitations of standard anomaly scans compared to cross-pattern discrepancy detection?▼

Standard scans miss advanced anomalies like cross-pattern discrepancies, neighbor contamination, and trajectory non-linearity. Use cross-pattern discrepancy detection to surface entities anomalous in one pattern but normal in another.

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