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pattern-detector

Detect dominant architecture patterns with confidence scores across 7 programming languages.

3|2|Updated Jan 23, 2026
Search Tags:#codebase-analysis#confidence-scoring#architectural-governance#architecture-pattern-detection#dependency-graph-validation#technical-debt-audit
One-click install
npx skills add https://github.com/robotijn/ctoc --skill pattern-detector-robotijn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pattern-detector
Source: https://github.com/robotijn/ctoc/tree/main/skills/architecture/pattern-detector
Command: npx skills add https://github.com/robotijn/ctoc --skill pattern-detector-robotijn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the guesswork of identifying a codebase's architecture pattern, preventing inconsistent enforcement, onboarding friction for new contributors, and unplanned architectural drift that accumulates technical debt.

Core Features & Use Cases

  • Multi-Pattern Detection: Identifies 11 common architecture patterns (Layered, Hexagonal, Clean, Onion, Vertical Slice, Modular Monolith, Microservices, etc.) with calibrated confidence scores across 7 programming languages.
  • Drift & Risk Flagging: Surfaces mixed patterns, undocumented pattern choices, dependency rule violations, and drift toward anti-patterns like distributed monoliths or anemic domain models.
  • Use Case: When joining a new legacy project, use this Skill to quickly map the existing architecture, verify it is documented for team alignment, and catch hidden layer violations before they cause systemic technical debt.

Quick Start

Use the pattern-detector skill to analyze your project's directory structure and import graph to identify its dominant architecture pattern and associated confidence score.

Frequently Asked Questions about pattern-detector

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

FAQPage Schema
How do I detect the architecture pattern of a legacy codebase?▼

Architecture pattern detection analyzes directory structures and import graphs to identify dominant patterns like Layered, Hexagonal, or Microservices, providing calibrated confidence scores to eliminate guesswork during codebase onboarding.

Can architecture pattern detection identify violations and drift in a codebase?▼

Yes, pattern detection validates pattern claims against import graphs, flagging dependency rule violations, mixed patterns, undocumented architectural choices, and drift toward anti-patterns like distributed monoliths or anemic domain models.

Does architecture pattern detection work with Python and TypeScript codebases?▼

Architecture pattern detection supports seven programming languages including C#, Java, Python, C, C++, TypeScript, and SQL, identifying 11 common architecture patterns across these language ecosystems.

What is the best way to audit technical debt caused by architectural drift?▼

Auditing technical debt requires detecting architectural drift by cross-linking pattern analysis with dependency graph validation to surface undocumented pattern choices and hidden layer violations before they cause systemic issues.

How does confidence scoring work for codebase architecture pattern detection?▼

Confidence scoring calibrates the certainty of detected architecture patterns by validating import graphs against known pattern structures, ensuring teams receive quantifiable metrics rather than subjective architectural assessments.

When should I run an architectural governance check on my project?▼

Architectural governance checks should run during codebase onboarding, technical debt audits, and when verifying pattern documentation, ensuring team alignment and catching hidden dependency rule violations before they accumulate.

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