// Enterprise systematic code review orchestrator with TRUST 5 principles, multi-language support, Context7 integration, AI-powered quality checks, SOLID principle validation, security vulnerability detection, and maintainability analysis across 25+ programming languages; activates for code reviews, quality standard validation, TRUST 5 enforcement, architectural audits, and automated review automation
| name | moai-core-code-reviewer |
| version | 4.0.0 |
| created | "2025-11-11T00:00:00.000Z" |
| updated | "2025-11-12T00:00:00.000Z" |
| status | stable |
| description | Enterprise systematic code review orchestrator with TRUST 5 principles, multi-language support, Context7 integration, AI-powered quality checks, SOLID principle validation, security vulnerability detection, and maintainability analysis across 25+ programming languages; activates for code reviews, quality standard validation, TRUST 5 enforcement, architectural audits, and automated review automation |
| keywords | ["code-review","TRUST-5","quality-assurance","SOLID-principles","security-scanning","maintainability-analysis","ai-code-review","context7-integration","automated-review","enterprise-quality"] |
| allowed-tools | ["Read","Write","Edit","Glob","Bash","AskUserQuestion","mcp__context7__resolve-library-id","mcp__context7__get-library-docs","WebFetch"] |
| Field | Value |
|---|---|
| Skill Name | moai-alfred-code-reviewer |
| Version | 4.0.0 Enterprise (2025-11-12) |
| Core Framework | TRUST 5 principles, Context7 integration |
| AI Integration | โ Context7 MCP, AI quality checks, pattern matching |
| Auto-load | When conducting code reviews or quality checks |
| Languages | 25+ (Python, JavaScript, Go, Rust, Java, etc.) |
| Lines of Content | 950+ with 15+ production examples |
| Progressive Disclosure | 3-level (framework, patterns, advanced) |
Provides systematic guidance for enterprise-grade code review processes applying TRUST 5 principles, validating SOLID principles, identifying security issues, ensuring maintainability, and automating quality gates across all programming languages.
Focus: Test coverage, quality, comprehensiveness
Key Questions:
Tools: pytest coverage, jest --coverage, go test -cover, cargo test
Examples:
# โ Bad: Tests implementation, not behavior
def test_add():
assert add(2, 2) == 4
# โ
Good: Tests behavior with edge cases
def test_add_positive_numbers():
assert add(2, 2) == 4
assert add(-1, 1) == 0
assert add(0, 0) == 0
def test_add_boundary():
assert add(int.max, 1) == overflow_behavior()
Focus: Code clarity, self-documentation, maintainability
Key Questions:
Tools: linters, code formatters, readability checkers
Examples:
# โ Bad: Unclear intent
def calc(x, y, z):
return x * (1 + y / 100) - z * 0.05
# โ
Good: Clear intent with constants
DISCOUNT_RATE = 0.05
TAX_RATE = 0.05
def calculate_final_price(base_price: float, tax_percent: float, discount: float) -> float:
"""Calculate final price after tax and discount.
Args:
base_price: Original product price
tax_percent: Tax percentage (0-100)
discount: Discount amount to subtract
"""
with_tax = base_price * (1 + tax_percent / 100)
return with_tax - (discount * DISCOUNT_RATE)
Focus: Consistency, patterns, architectural cohesion
Key Questions:
Tools: style guides, architectural patterns, linters
Examples:
# โ Bad: Inconsistent error handling
def get_user(user_id):
try:
return fetch_from_db(user_id)
except Exception as e:
return None # Inconsistent with rest of codebase
# โ
Good: Consistent error handling
def get_user(user_id: int) -> User:
"""Get user by ID.
Raises:
UserNotFoundError: If user doesn't exist
DatabaseError: If database connection fails
"""
try:
return self.user_repository.find_by_id(user_id)
except DatabaseConnectionError as e:
logger.error(f"Database error: {e}")
raise DatabaseError(str(e)) from e
except Exception as e:
logger.error(f"Unexpected error: {e}")
raise
Focus: Security vulnerabilities, input validation, secret handling
Key Questions:
Tools: bandit, safety, npm audit, go vet, security scanners
Examples:
# โ Bad: SQL injection vulnerability
def get_user(user_id):
query = f"SELECT * FROM users WHERE id = {user_id}" # Vulnerable!
return db.execute(query)
# โ
Good: Parameterized query
def get_user(user_id: int) -> User:
query = "SELECT * FROM users WHERE id = ?"
return db.execute(query, [user_id])
Key Questions:
Examples:
# โ Bad: No traceability
def calculate_discount(price, customer_type):
if customer_type == "vip":
return price * 0.8
return price
# โ
Good: Full traceability
def calculate_discount(price: float, customer_type: str) -> float:
"""Calculate discount based on customer type.
Implements SPEC-042: VIP customers receive 20% discount
Linked to:
- SPEC-042: VIP pricing requirements
- TEST-042-001: VIP discount validation
- PR #1234: Feature implementation
"""
VIP_DISCOUNT_RATE = 0.20
if customer_type == "vip":
return price * (1 - VIP_DISCOUNT_RATE)
return price
| Principle | Focus | Review Question |
|---|---|---|
| Single Responsibility | One reason to change | Does this class/function do one thing? |
| Open/Closed | Open for extension, closed for modification | Can behavior be extended without modifying? |
| Liskov Substitution | Substitutable subtypes | Can derived classes replace base without breaking? |
| Interface Segregation | Minimal, specific interfaces | Are clients forced to depend on methods they don't use? |
| Dependency Inversion | Depend on abstractions, not concretions | Do high-level modules depend on low-level implementations? |
โ Linting & formatting
โ Security scanning (bandit, safety, npm audit)
โ Dependency vulnerabilities
โ Test coverage โฅ85%
โ Type checking (mypy, TypeScript, etc.)
โ SOLID principles
โ Design patterns appropriate?
โ Consistency with codebase
โ Scalability implications?
โ Performance implications?
โ Input validation
โ No hardcoded secrets
โ Authentication/authorization correct?
โ SQL injection prevention
โ XSS prevention
โ CSRF tokens present
โ TRUST 5 checklist
โ Edge cases handled?
โ Error messages helpful?
โ Documentation complete?
| Change Type | Severity | Automation | Review Time | Focus Areas |
|---|---|---|---|---|
| Security fix | ๐ด Critical | Full scan | 30+ min | Vulnerabilities, test coverage, audit trail |
| Core architecture | ๐ด Critical | Partial | 45+ min | Design patterns, scalability, consistency |
| Feature (new) | ๐ก Major | Full scan | 30 min | Completeness, TRUST 5, documentation |
| Bug fix | ๐ข Minor | Partial | 15 min | Root cause, test coverage, regressions |
| Documentation | ๐ข Minor | Basic | 5 min | Accuracy, completeness, examples |
| Configuration | ๐ก Medium | Full | 10 min | Security, best practices, side effects |
| Refactoring | ๐ข Minor | Full | 15 min | Behavior preservation, performance |
Live Security Scanning: Get latest vulnerability patterns from official databases
Best Practice Integration: Apply latest security recommendations from official docs
Version-Aware Checks: Context7 provides version-specific security guidance
Automated Fix Suggestions: Context7 patterns for common vulnerability fixes
moai-alfred-practices (Code patterns and best practices)moai-essentials-refactor (Refactoring strategies)For detailed review checklists: reference.md
For real-world examples: examples.md
Last Updated: 2025-11-12
Status: Production Ready (Enterprise )