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code-quality-assessment

@vinnie357/claudio
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Activate when analyzing code quality through linting, formatting, testing, coverage analysis, and maintainability metrics

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SKILL.md

name code-quality-assessment
description Activate when analyzing code quality through linting, formatting, testing, coverage analysis, and maintainability metrics
license MIT

Code Quality Assessment

Guide for comprehensive code quality analysis through automated linting, formatting verification, test execution, coverage analysis, and maintainability assessment.

When to Use This Skill

Activate when:

  • Running code quality checks across a codebase
  • Analyzing test coverage and identifying gaps
  • Measuring code complexity and maintainability
  • Enforcing coding standards and style guides
  • Generating quality reports for CI/CD pipelines
  • Assessing technical debt and prioritizing improvements

Quality Assessment Process

Phase 1: Tool Discovery

  1. Analyze project structure for technology stack
  2. Detect configuration files (eslint, prettier, pytest, etc.)
  3. Identify available quality tools
  4. Plan execution order and dependencies

Phase 2: Linting and Static Analysis

  1. Execute available linting tools
  2. Parse output for errors, warnings, style violations
  3. Categorize issues by severity
  4. Identify patterns in violations
  5. Generate recommendations

Phase 3: Code Formatting

  1. Run formatting tools
  2. Identify inconsistencies
  3. Assess auto-fix impact
  4. Generate formatting reports

Phase 4: Test Suite and Coverage

  1. Execute test suites (unit, integration, e2e)
  2. Analyze results for failures and performance
  3. Generate coverage reports:
    • Line coverage
    • Branch coverage
    • Function coverage
    • Statement coverage
  4. Identify coverage gaps
  5. Prioritize missing test scenarios

Phase 5: Complexity Analysis

  1. Calculate cyclomatic complexity
  2. Identify functions exceeding thresholds
  3. Assess maintainability metrics
  4. Calculate technical debt estimates
  5. Detect code duplication

Tool Detection by Project Type

JavaScript/TypeScript

  • Linting: ESLint, TSLint
  • Formatting: Prettier
  • Testing: Jest, Vitest, Mocha
  • Complexity: plato, jscpd

Python

  • Linting: pylint, flake8, ruff
  • Formatting: Black, isort
  • Testing: pytest
  • Complexity: radon, mccabe

Rust

  • Linting: clippy
  • Formatting: rustfmt
  • Testing: cargo test
  • Coverage: tarpaulin, llvm-cov

Elixir

  • Linting: credo
  • Static Analysis: dialyzer
  • Formatting: mix format
  • Testing: ExUnit
  • Security: sobelow

Go

  • Linting: golint, staticcheck
  • Formatting: gofmt
  • Testing: go test
  • Complexity: gocyclo

Quality Report Structure

# Code Quality Assessment Report

**Project**: [Name]
**Date**: [Date]
**Overall Score**: [Score/Grade]

## Executive Summary
- **Critical Issues**: [Number]
- **Test Coverage**: [Percentage]%
- **Quality Gate**: [Pass/Fail]

## Linting Results
### Critical Issues (Errors)
- [Issue with file:line]
- [Recommended fix]

### Warnings
- [Warning description]
- [Severity assessment]

## Test Results
### Execution Summary
- **Total Tests**: [Number]
- **Passed**: [Number] ([%])
- **Failed**: [Number] ([%])

### Coverage Analysis
- **Overall**: [%]%
- **Line**: [%]%
- **Branch**: [%]%
- **Function**: [%]%

### Critical Coverage Gaps
- [Uncovered critical functionality]
- [Security-sensitive code without tests]

## Complexity Metrics
### Cyclomatic Complexity
- **Average**: [X]
- **Max**: [Y]
- **Functions >10**: [Number]

### Maintainability
- **Index**: [Score]/100
- **Duplication**: [%]%
- **Technical Debt**: [Hours]

## Action Items
### Critical (Immediate)
1. [Fix with instructions]

### High Priority (This Sprint)
1. [Improvement]

### Medium Priority (Next Sprint)
1. [Enhancement]

## Quality Gate Status
- [ ] Linting: Passed
- [ ] Coverage: >80%
- [ ] Security: Passed

Complexity Thresholds

  • Simple (1-5): Acceptable complexity
  • Moderate (6-10): Monitor for growth
  • Complex (11-20): Consider refactoring
  • Very Complex (>20): Refactor required

Key Principles

  • Tool-Agnostic: Detect and use available tools
  • Actionable Results: Provide specific recommendations
  • Prioritized Output: Organize by criticality
  • Context-Aware: Consider project type and stage
  • Trend Analysis: Compare against baselines
  • CI/CD Ready: Generate machine-readable outputs