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EndUser123
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discover

Identify and map recurring patterns, architectures, and technical debt across codebases.

Updated Apr 23, 2026
Search Tags:#semantic-search#architecture-analysis#gpu-acceleration#discover#codebase-patterns#ml-enhanced
One-click install
npx skills add https://github.com/EndUser123/cc-skills-utils --skill discover-enduser123
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: discover
Source: https://github.com/EndUser123/cc-skills-utils/tree/main/skills/discover
Command: npx skills add https://github.com/EndUser123/cc-skills-utils --skill discover-enduser123

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables teams to discover and analyze recurring patterns, architectures, and technical debt in large codebases using ML-enhanced pattern detection and GPU acceleration.

Core Features & Use Cases

  • ML-enhanced pattern detection for large-scale codebases
  • GPU-accelerated exploration to accelerate analysis
  • Semantic search and integration with existing discovery workflows
  • On-demand discovery with configurable thoroughness levels
  • Technical debt identification and architecture insights

Quick Start

Run /discover with a query to begin a rapid codebase discovery.

Frequently Asked Questions about discover

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

FAQPage Schema
How do I identify recurring architecture patterns and technical debt in a large codebase?▼

You can identify recurring architecture patterns and technical debt in a large codebase by running an ML-enhanced discovery command with a query to map structures and detect refactoring opportunities.

What is semantic search for codebase patterns and how does it work?▼

Semantic search for codebase patterns uses ML-enhanced detection to map and analyze recurring structures across large repositories, enabling on-demand discovery with configurable thoroughness levels.

Can I use GPU acceleration for codebase architecture exploration?▼

Yes, you can use GPU-accelerated exploration to accelerate the analysis of large-scale codebases, rapidly processing semantic search queries and mapping technical debt during discovery.

Does this codebase pattern detection require any specific dependencies or environment setup?▼

No specific dependencies are required to run codebase pattern detection, as the tool operates independently with configurable thoroughness levels to integrate into existing discovery workflows.

What is the best way to find refactoring opportunities across large repositories?▼

The best way to find refactoring opportunities across large repositories is using ML-enhanced pattern detection to map technical debt and identify recurring architectures on demand.

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