Skip to main content

​
Overview

Repolyze’s health scoring system provides a comprehensive quality assessment of any repository. Get actionable metrics across six key dimensions to understand code health at a glance.

​
Overall Score

The overall score (0-100) is displayed prominently with a circular progress gauge:
  • 90-100 - Excellent (green)
  • 75-89 - Good (green)
  • 50-74 - Fair (yellow)
  • 25-49 - Needs Work (orange)
  • 0-24 - Critical (red)
The overall score is calculated by averaging the six individual metric scores, weighted by their relative importance to project health.

​
Six Key Metrics

​
Code Quality

Evaluates the structure and cleanliness of the codebase:
  • Consistent patterns - Use of design patterns and architectural consistency
  • Code organization - Logical file and folder structure
  • Naming conventions - Clear, descriptive variable and function names
  • Code complexity - Cyclomatic complexity and nesting depth
  • Best practices - Language-specific idioms and conventions

​
Documentation

Assesses how well the project is documented:
  • README quality - Completeness and clarity of README.md
  • Code comments - Inline documentation and JSDoc/docstrings
  • API documentation - Endpoint descriptions and examples
  • Setup instructions - Installation and configuration guides
  • Contributing guidelines - CONTRIBUTING.md presence and quality

​
Security

Identifies potential security vulnerabilities:
  • Dependency vulnerabilities - Known CVEs in dependencies
  • Secret exposure - Hardcoded API keys or credentials
  • Authentication patterns - Proper auth implementation
  • Input validation - SQL injection and XSS prevention
  • Security headers - CORS, CSP, and other protective headers
A low security score indicates critical issues that should be addressed immediately. Review the AI Insights panel for specific recommendations.

​
Maintainability

Measures how easy it is to maintain and update the code:
  • Code duplication - DRY principle adherence
  • Modularity - Separation of concerns and decoupling
  • File size - Reasonable file lengths (not too long)
  • Function length - Single responsibility principle
  • Configuration management - Environment variables and config files

​
Test Coverage

Evaluates the testing infrastructure:
  • Test presence - Existence of test files
  • Test framework - Modern testing tools (Jest, Vitest, Pytest, etc.)
  • Test patterns - Unit, integration, and E2E tests
  • Coverage tools - Integration with coverage reporters
  • CI/CD integration - Automated test runs
Test coverage analysis is estimated based on test file presence and patterns. For exact coverage percentages, run your project’s coverage tools.

​
Dependencies

Analyzes dependency health and management:
  • Up-to-date packages - Recent versions vs. outdated dependencies
  • Dependency count - Appropriate number of dependencies
  • Lock files - Presence of package-lock.json, yarn.lock, etc.
  • Version pinning - Specific vs. loose version constraints
  • Deprecated packages - Use of deprecated or abandoned libraries

​
Score Breakdown Display

Each metric is displayed with:
  1. Icon - Visual indicator of the metric category
  2. Label - Metric name (e.g., “Code Quality”)
  3. Score - Numerical value (0-100)
  4. Progress bar - Visual representation with color coding:
    • High (≥70) - Full saturation primary color
    • Medium (40-69) - 70% saturation primary color
    • Low (<40) - 50% saturation primary color
The score card footer shows quick stats:
  • High - Number of metrics scoring ≥70
  • Medium - Number of metrics scoring 40-69
  • Low - Number of metrics scoring <40
Aim for at least 4 metrics in the “High” category for a healthy repository.

​
AI-Powered Insights

Below the score card, you’ll find AI-generated insights that provide context:
  • Strengths - What the repository does well
  • Weaknesses - Areas that need improvement
  • Suggestions - Specific, actionable recommendations
  • Warnings - Critical issues requiring immediate attention

​
Insight Priority Levels

Each insight has a priority:
  • Critical - Immediate action required (security issues, broken builds)
  • High - Important improvements (test coverage, documentation gaps)
  • Medium - Quality enhancements (refactoring opportunities)
  • Low - Nice-to-have improvements (code style, minor optimizations)

​
Affected Files

Insights include references to specific files when applicable: 
affectedFiles: [
  "src/components/score-card.tsx",
  "lib/types.ts"
]
Click on file paths to view them directly on GitHub.

​
Using Scores to Drive Improvements

1

Identify Low Scores

Focus on metrics scoring below 70, especially those below 40.
2

Review Insights

Read the AI insights related to low-scoring metrics for specific guidance.
3

Check Recommendations

Navigate to the Refactors and Automations tabs for actionable tasks.
4

Track Progress

Re-analyze after making improvements to see score increases.

​
Score Comparison

Compare scores across branches to:
  • Validate improvements - Did your PR increase code quality?
  • Catch regressions - Did a feature branch decrease test coverage?
  • Guide decisions - Which architectural approach has better maintainability?

​
Next Steps

Repository Analysis

Learn how repository analysis works

Architecture Diagrams

Visualize your system architecture

Build docs developers (and LLMs) love

Get started for freeTalk to us