gds-sphere-designer

Name: gds-sphere-designer
Availability: InStock
Author: hypertopos

Design and calibrate geometric spheres from raw data to anomaly detection.

Updated Apr 9, 2026

Search Tags:#design #patterns #detection #geometry #anomaly #sphere

Ready to run

npx skills add https://github.com/hypertopos/hypertopos-skills --skill gds-sphere-designer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: gds-sphere-designer
Source: https://github.com/hypertopos/hypertopos-skills/tree/main/gds-sphere-designer
Command: npx skills add https://github.com/hypertopos/hypertopos-skills --skill gds-sphere-designer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Sphere design for AI agents exploring geometric data spaces is complex and error-prone without a structured workflow. The GDS Sphere Designer coordinates data discovery to pattern strategy, YAML generation, build, calibration, and iterative tuning, providing a repeatable path from raw data to navigable geometry.

Core Features & Use Cases

End-to-end design workflow: discover data, define patterns, generate sphere.yaml, build geometry, calibrate models, and verify results.
NB-Split support: isolate orthogonal concerns on separate entity lines to preserve signal and enable cross-pattern scoring.
Reference-driven domain patterns: reuse established design patterns from references to accelerate sphere creation and comparison.

Quick Start

Provide a sphere.yaml describing your data sources and patterns, then run hypertopos build to generate and verify the sphere.

Frequently Asked Questions about gds-sphere-designer

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

FAQPage Schema

How do I design geometric spheres for anomaly detection over relational data?▼

Sphere design for anomaly detection involves discovering data, defining patterns, generating a sphere.yaml configuration, building geometry, and calibrating models iteratively to achieve actionable anomaly detection results.

What is the best way to structure data discovery and pattern strategy for geometric data spaces?▼

Structuring data discovery and pattern strategy requires a phased workflow spanning discovery, design, build, and verification, leveraging reference materials for domain patterns to ensure navigable geometry.

How do I isolate orthogonal concerns when building geometry for cross-pattern scoring?▼

Isolating orthogonal concerns during geometry build uses NB-Split isolation strategies to separate concerns onto distinct entity lines, preserving signal integrity and enabling cross-pattern scoring.

Can I reuse established design patterns to accelerate sphere creation?▼

Yes, reusing established design patterns via reference-driven domain materials accelerates sphere creation, enabling rapid generation and comparison of geometric sphere configurations.

Do I need a sphere.yaml file to start building and verifying geometric spheres?▼

Yes, providing a sphere.yaml file describing your data sources and patterns is required to run the build process and generate, calibrate, and verify the geometric sphere.

When should I not use a single entity line for multiple geometric patterns?▼

Avoid using a single entity line for multiple geometric patterns when signal preservation and cross-pattern scoring are critical; instead, apply NB-Split isolation strategies to separate orthogonal concerns onto distinct entity lines.