Summarize entity lines, patterns, and population structure in geometric spheres.
npx skills add https://github.com/hypertopos/hypertopos-skills --skill gds-explorer
Please help me install this Agent Skill. Skill: gds-explorer Source: https://github.com/hypertopos/hypertopos-skills/tree/main/gds-explorer Command: npx skills add https://github.com/hypertopos/hypertopos-skills --skill gds-explorer
GDS explorer helps AI agents quickly orient themselves in unfamiliar geometric spheres by summarizing entity lines, identifying patterns, and outlining population structure to guide deeper investigation.
Run the orientation workflow on a target sphere by invoking sphere_overview(detail="summary"), get_sphere_info(), and find_clusters to identify archetypes, then hand off to the appropriate skill for deeper investigation.
High-intent search queries and answers about installing and using this skill.
To segment geometric sphere populations, profile raw dimensions and assess clustering results to identify archetypes. This subpopulation profiling guides targeted analysis and determines which specialist skill to use for ground truth validation.
Exploring entity lines and mapping subpopulations in a geometric sphere involves summarizing population structure to reveal patterns and archetypes. This initial phenotype generation coordinates handoffs to specialized skills for deeper analysis.
Discovering archetypes in a geometric sphere requires a multi-step orientation workflow that leverages sphere_overview, get_sphere_info, and find_clusters. This sequence generates an initial phenotype and identifies population structure for targeted analysis.
You need to profile raw dimensions and assess clustering results when orienting in an unfamiliar geometric sphere. This profiling step segments populations, reveals anomalies, and determines the appropriate specialist skill for ground truth validation.
You can hand off sphere orientation results to gds-investigator or gds-detective for ground truth validation and deeper analysis. The orientation workflow generates an initial phenotype and coordinates these handoffs for comprehensive investigation.
hypertoposIdentify anomalies in geometric data spheres via targeted scans.
levnikolaevichExecute a single Story Finalizer test task labeled 'tests' through implementation to To Review.
hypertoposMonitor GDS sphere health and detect temporal drift and regime changes.
sanand0Perform machine learning on time series data with a scikit-learn compatible API.
