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code-quality-analyzer

Automação Autor: a5c-ai

Análise estática de código, avaliação de dívida técnica e métricas de velocidade de engenharia.

1.4kestrelas
Atualizado há 4 meses
Ver no GitHub ↗ Licença: MIT

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Code Quality Analyzer

Overview

The Code Quality Analyzer skill provides detailed code-level analysis for technical due diligence. It performs static code analysis, assesses technical debt, and evaluates engineering team velocity to understand code health and development productivity.

Capabilities

Static Code Analysis

  • Run automated code quality checks
  • Identify code smells and anti-patterns
  • Measure code complexity metrics
  • Detect potential bugs and vulnerabilities

Technical Debt Assessment

  • Quantify technical debt backlog
  • Identify high-priority refactoring needs
  • Assess test coverage and quality
  • Evaluate documentation completeness

Engineering Velocity Metrics

  • Measure deployment frequency
  • Track lead time for changes
  • Analyze cycle time and throughput
  • Assess sprint velocity trends

Code Health Indicators

  • Analyze code churn patterns
  • Review pull request metrics
  • Assess code review practices
  • Evaluate dependency management

Usage

Analyze Code Quality

Input: Repository access, analysis parameters
Process: Run static analysis, aggregate metrics
Output: Code quality report, issue summary

Assess Technical Debt

Input: Codebase access, debt categorization
Process: Inventory debt, estimate remediation
Output: Technical debt assessment, prioritization

Measure Engineering Velocity

Input: Git history, project management data
Process: Calculate velocity metrics
Output: Velocity report, trend analysis

Review Code Health

Input: Repository data, team practices
Process: Analyze patterns, compare benchmarks
Output: Code health scorecard, recommendations

Key Metrics

| Metric | Description | Target Range | |--------|-------------|--------------| | Test Coverage | % of code covered by tests | 70-90% | | Code Complexity | Cyclomatic complexity average | < 10 | | Tech Debt Ratio | Debt remediation time / dev time | < 5% | | Deployment Frequency | Deployments per week | Daily to weekly | | Change Failure Rate | % of deployments causing issues | < 15% |

Integration Points

  • Technical Due Diligence: Detailed code analysis for DD
  • Tech Stack Scanner: Complement architecture review
  • Technical Assessor (Agent): Support agent analysis
  • IP Patent Analyzer: Code-level IP assessment

Analysis Tools Integration

  • SonarQube for code quality
  • CodeClimate for maintainability
  • GitHub/GitLab analytics
  • Jira/Linear for velocity data
  • Custom scripts for specific checks

Best Practices

  1. Calibrate expectations by company stage
  2. Focus on trends over absolute numbers
  3. Consider context of rapid iteration
  4. Balance debt against velocity needs
  5. Assess relative to team size and resources
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