export default moai-essentials-review

name	moai-essentials-review
version	4.0.0
created	"2025-11-11T00:00:00.000Z"
updated	"2025-11-12T00:00:00.000Z"
status	stable
description	Enterprise comprehensive code review automation with AI-powered quality analysis, TRUST 5 enforcement, multi-language support, Context7 integration, security scanning, performance analysis, test coverage validation, and automated review feedback generation
keywords	["code-review","quality-analysis","TRUST-5-validation","security-scanning","performance-analysis","test-coverage","ai-review","context7-integration","review-automation","enterprise-quality"]
allowed-tools	["Read","Write","Edit","Glob","Bash","AskUserQuestion","mcp__context7__resolve-library-id","mcp__context7__get-library-docs","WebFetch"]

Enterprise Code Review Automation

Skill Metadata

Field	Value
Skill Name	moai-essentials-review
Version	4.0.0 Enterprise (2025-11-12)
Core Framework	TRUST 5 principles automation
AI Integration	✅ Context7 MCP, AI quality analysis
Auto-load	On code commit or PR creation
Languages	25+ languages with specialized analysis
Lines of Content	880+ with 16+ production examples
Progressive Disclosure	3-level (automation, analysis, advanced)

---

What It Does

Automates comprehensive code review process with AI-powered quality checks, TRUST 5 principle validation, security vulnerability detection, performance analysis, test coverage verification, and detailed review feedback generation.

---

3-Phase Automated Review

Phase 1: Automated Checks (5 minutes)

Syntax & Linting:
  ✓ Run linters (pylint, eslint, golint, etc.)
  ✓ Check code formatting (black, prettier, gofmt)
  ✓ Type checking (mypy, TypeScript, go vet)

Security Scanning:
  ✓ Dependency vulnerabilities (safety, npm audit, cargo audit)
  ✓ Credential detection (git-secrets, detect-secrets)
  ✓ OWASP Top 10 checks

Test Coverage:
  ✓ Coverage ≥85%
  ✓ Critical paths covered
  ✓ Edge cases tested

Phase 2: AI Quality Analysis (15 minutes)

TRUST 5 Validation:
  ✓ T - Tests present and comprehensive
  ✓ R - Code readable and maintainable
  ✓ U - Unified with codebase patterns
  ✓ S - Security best practices

Design Analysis:
  ✓ SOLID principles
  ✓ Design patterns appropriate
  ✓ Scalability concerns
  ✓ Performance implications

Phase 3: Human Review (20 minutes)

Architectural Review:
  ✓ Does solution fit architecture?
  ✓ Any alternatives considered?
  ✓ Trade-offs documented?

Business Logic:
  ✓ Does it solve the problem?
  ✓ Any edge cases missed?
  ✓ User experience impact?

Documentation:
  ✓ README updated
  ✓ API docs current
  ✓ Examples provided

---

AI-Powered Quality Checks

Code Quality Metrics

class CodeQualityAnalyzer:
    """AI-powered code quality analysis."""
    
    async def analyze(self, code: str) -> QualityReport:
        metrics = {
            "complexity": calculate_cyclomatic(code),      # Should be <10
            "testability": assess_testability(code),        # Should be >0.85
            "maintainability": calculate_maintainability(code),  # Should be >80
            "readability": assess_readability(code),         # Should be clear
            "security_issues": scan_for_vulnerabilities(code),   # Should be 0
            "performance_concerns": detect_patterns(code),   # Should be minimal
        }
        
        return QualityReport(metrics)

TRUST 5 Automated Checks

T - Test First:
  ├─ Coverage ≥85%? ✓
  ├─ Happy path covered? ✓
  ├─ Edge cases tested? ✓
  └─ Error scenarios? ✓

R - Readable:
  ├─ Functions <50 lines? ✓
  ├─ Meaningful names? ✓
  ├─ Comments explain WHY? ✓
  └─ Complexity <10? ✓

U - Unified:
  ├─ Follows team patterns? ✓
  ├─ Consistent style? ✓
  ├─ Error handling aligned? ✓
  └─ Logging strategy consistent? ✓

S - Secured:
  ├─ Inputs validated? ✓
  ├─ No hardcoded secrets? ✓
  ├─ SQL injection prevention? ✓
  └─ XSS prevention? ✓

T - Trackable:
  ├─ SPEC referenced? ✓

---

Security Vulnerability Detection

Critical Checks:
  ✓ Hardcoded credentials (API keys, passwords)
  ✓ SQL injection vectors
  ✓ XSS vulnerabilities
  ✓ CSRF token absence
  ✓ Unsafe deserialization
  ✓ Privilege escalation paths

High Priority:
  ✓ Missing input validation
  ✓ Weak cryptography
  ✓ Insecure randomness
  ✓ Race conditions
  ✓ Dependency vulnerabilities

Medium Priority:
  ✓ Missing error messages
  ✓ Insufficient logging
  ✓ Memory leaks
  ✓ Resource exhaustion risks

---

Performance Analysis

Detection Patterns:
  ✓ O(n²) algorithms in O(n) context
  ✓ Unnecessary file I/O in loops
  ✓ Blocking operations in async code
  ✓ Memory allocations in hot paths
  ✓ Inefficient string concatenation
  ✓ Database queries without indexing

Optimization Suggestions:
  ✓ Use more efficient algorithm
  ✓ Cache results
  ✓ Batch operations
  ✓ Use async/await properly
  ✓ Index database columns

---

Automated Review Report

# Code Review Report

## Summary
✅ **Status**: APPROVED (with 2 minor notes)
- Test Coverage: 87% ✓
- Security: ✓ Clean
- Performance: ✓ No concerns
- Design: ✓ Good
- TRUST 5: All checks passed

## TRUST 5 Assessment

### T - Test First: ✓
Coverage: 87% (target ≥85%)
- Happy path: ✓ Covered
- Edge cases: ✓ 5 tests
- Error scenarios: ✓ 3 tests

### R - Readable: ✓
All functions <50 lines, clear names

### U - Unified: ✓
Consistent with team patterns

### S - Secured: ✓
- No credentials: ✓
- Input validation: ✓
- Error messages safe: ✓

### T - Trackable: ✓
- SPEC-042 referenced
- 5 tests linked
- Code linked to PR

## Detailed Findings

### Strengths
1. ✅ Excellent test coverage (87%)
2. ✅ Clean, readable code
3. ✅ Proper error handling
4. ✅ Security best practices followed

### Minor Notes
1. ⚠️ Function `calculate_discount` could use type hints
2. ⚠️ Consider adding cache for frequently called API

### Recommendations
1. Add type hints to improve IDE support
2. Consider Redis caching for API calls

## Approval
✅ **Ready to merge** - All TRUST 5 checks passed

---

Integration with Context7

Live Security Patterns: Get latest vulnerability detection from official databases
Performance Optimization: Context7 provides version-specific optimization patterns
Language Updates: Context7 includes latest language/framework best practices

---

Best Practices

DO

• ✅ Run automated checks before human review
• ✅ Provide specific, actionable feedback
• ✅ Explain WHY improvements are needed
• ✅ Link to official documentation
• ✅ Flag security issues immediately
• ✅ Enforce TRUST 5 consistently
• ✅ Update based on new findings
• ✅ Track metrics over time

DON'T

• ❌ Block on automated issues alone (let linters handle)
• ❌ Miss security vulnerabilities
• ❌ Accept coverage <85%
• ❌ Ignore deprecated patterns
• ❌ Skip performance analysis
• ❌ Approve without TRUST 5 validation
• ❌ Add comments that code already explains

---

Related Skills

• moai-alfred-code-reviewer (Manual review guidance)
• moai-essentials-debug (Debugging techniques)

---

For detailed analysis guidelines: reference.md
For real-world examples: examples.md
Last Updated: 2025-11-12
Status: Production Ready (Enterprise )