diegosouzapw Generic Code Review Instructions
Comprehensive code review guidelines for GitHub Copilot that can be adapted to any project. These instructions follow best practices from prompt engineering and provide a structured approach to code quality, security, testing, and architecture review.
Review Language
When performing a code review, respond in English (or specify your preferred language).
Customization Tip: Change to your preferred language by replacing "English" with "Portuguese (Brazilian)", "Spanish", "French", etc.
Review Priorities
When performing a code review, prioritize issues in the following order:
🔴 CRITICAL (Block merge)
- Security: Vulnerabilities, exposed secrets, authentication/authorization issues
- Correctness: Logic errors, data corruption risks, race conditions
- Breaking Changes: API contract changes without versioning
- Data Loss: Risk of data loss or corruption
🟡 IMPORTANT (Requires discussion)
- Code Quality: Severe violations of SOLID principles, excessive duplication
- Test Coverage: Missing tests for critical paths or new functionality
- Performance: Obvious performance bottlenecks (N+1 queries, memory leaks)
- Architecture: Significant deviations from established patterns
🟢 SUGGESTION (Non-blocking improvements)
- Readability: Poor naming, complex logic that could be simplified
- Optimization: Performance improvements without functional impact
- Best Practices: Minor deviations from conventions
- Documentation: Missing or incomplete comments/documentation
General Review Principles
When performing a code review, follow these principles:
- Be specific: Reference exact lines, files, and provide concrete examples
- Provide context: Explain WHY something is an issue and the potential impact
- Suggest solutions: Show corrected code when applicable, not just what's wrong
- Be constructive: Focus on improving the code, not criticizing the author
- Recognize good practices: Acknowledge well-written code and smart solutions
- Be pragmatic: Not every suggestion needs immediate implementation
- Group related comments: Avoid multiple comments about the same topic
Code Quality Standards
When performing a code review, check for:
Clean Code
- Descriptive and meaningful names for variables, functions, and classes
- Single Responsibility Principle: each function/class does one thing well
- DRY (Don't Repeat Yourself): no code duplication
- Functions should be small and focused (ideally < 20-30 lines)
- Avoid deeply nested code (max 3-4 levels)
- Avoid magic numbers and strings (use constants)
- Code should be self-documenting; comments only when necessary
Examples
// ❌ BAD: Poor naming and magic numbers
function calc(x, y) {
if (x > 100) return y * 0.15;
return y * 0.10;
}
// ✅ GOOD: Clear naming and constants
const PREMIUM_THRESHOLD = 100;
const PREMIUM_DISCOUNT_RATE = 0.15;
const STANDARD_DISCOUNT_RATE = 0.10;
function calculateDiscount(orderTotal, itemPrice) {
const isPremiumOrder = orderTotal > PREMIUM_THRESHOLD;
const discountRate = isPremiumOrder ? PREMIUM_DISCOUNT_RATE : STANDARD_DISCOUNT_RATE;
return itemPrice * discountRate;
}
Error Handling
- Proper error handling at appropriate levels
- Meaningful error messages
- No silent failures or ignored exceptions
- Fail fast: validate inputs early
- Use appropriate error types/exceptions
Examples
# ❌ BAD: Silent failure and generic error
def process_user(user_id):
try:
user = db.get(user_id)
user.process()
except:
pass
# ✅ GOOD: Explicit error handling
def process_user(user_id):
if not user_id or user_id <= 0:
raise ValueError(f"Invalid user_id: {user_id}")
try:
user = db.get(user_id)
except UserNotFoundError:
raise UserNotFoundError(f"User {user_id} not found in database")
except DatabaseError as e:
raise ProcessingError(f"Failed to retrieve user {user_id}: {e}")
return user.process()
Security Review
When performing a code review, check for security issues:
- Sensitive Data: No passwords, API keys, tokens, or PII in code or logs
- Input Validation: All user inputs are validated and sanitized
- SQL Injection: Use parameterized queries, never string concatenation
- Authentication: Proper authentication checks before accessing resources
- Authorization: Verify user has permission to perform action
- Cryptography: Use established libraries, never roll your own crypto
- Dependency Security: Check for known vulnerabilities in dependencies
Examples
// ❌ BAD: SQL injection vulnerability
String query = "SELECT * FROM users WHERE email = '" + email + "'";
// ✅ GOOD: Parameterized query
PreparedStatement stmt = conn.prepareStatement(
"SELECT * FROM users WHERE email = ?"
);
stmt.setString(1, email);
// ❌ BAD: Exposed secret in code
const API_KEY = "sk_live_abc123xyz789";
// ✅ GOOD: Use environment variables
const API_KEY = process.env.API_KEY;
Testing Standards
When performing a code review, verify test quality:
- Coverage: Critical paths and new functionality must have tests
- Test Names: Descriptive names that explain what is being tested
- Test Structure: Clear Arrange-Act-Assert or Given-When-Then pattern
- Independence: Tests should not depend on each other or external state
- Assertions: Use specific assertions, avoid generic assertTrue/assertFalse
- Edge Cases: Test boundary conditions, null values, empty collections
- Mock Appropriately: Mock external dependencies, not domain logic
Examples
// ❌ BAD: Vague name and assertion
test('test1', () => {
const result = calc(5, 10);
expect(result).toBeTruthy();
});
// ✅ GOOD: Descriptive name and specific assertion
test('should calculate 10% discount for orders under $100', () => {
const orderTotal = 50;
const itemPrice = 20;
const discount = calculateDiscount(orderTotal, itemPrice);
expect(discount).toBe(2.00);
});
Performance Considerations
When performing a code review, check for performance issues:
- Database Queries: Avoid N+1 queries, use proper indexing
- Algorithms: Appropriate time/space complexity for the use case
- Caching: Utilize caching for expensive or repeated operations
- Resource Management: Proper cleanup of connections, files, streams
- Pagination: Large result sets should be paginated
- Lazy Loading: Load data only when needed
Examples
# ❌ BAD: N+1 query problem
users = User.query.all()
for user in users:
orders = Order.query.filter_by(user_id=user.id).all() # N+1!
# ✅ GOOD: Use JOIN or eager loading
users = User.query.options(joinedload(User.orders)).all()
for user in users:
orders = user.orders
Architecture and Design
When performing a code review, verify architectural principles:
- Separation of Concerns: Clear boundaries between layers/modules
- Dependency Direction: High-level modules don't depend on low-level details
- Interface Segregation: Prefer small, focused interfaces
- Loose Coupling: Components should be independently testable
- High Cohesion: Related functionality grouped together
- Consistent Patterns: Follow established patterns in the codebase
Documentation Standards
When performing a code review, check documentation:
- API Documentation: Public APIs must be documented (purpose, parameters, returns)
- Complex Logic: Non-obvious logic should have explanatory comments
- README Updates: Update README when adding features or changing setup
- Breaking Changes: Document any breaking changes clearly
- Examples: Provide usage examples for complex features
Comment Format Template
When performing a code review, use this format for comments:
**[PRIORITY] Category: Brief title**
Detailed description of the issue or suggestion.
**Why this matters:**
Explanation of the impact or reason for the suggestion.
**Suggested fix:**
[code example if applicable]
**Reference:** [link to relevant documentation or standard]
Example Comments
Critical Issue
**🔴 CRITICAL - Security: SQL Injection Vulnerability**
The query on line 45 concatenates user input directly into the SQL string,
creating a SQL injection vulnerability.
**Why this matters:**
An attacker could manipulate the email parameter to execute arbitrary SQL commands,
potentially exposing or deleting all database data.
**Suggested fix:**
```sql
-- Instead of:
query = "SELECT * FROM users WHERE email = '" + email + "'"
-- Use:
PreparedStatement stmt = conn.prepareStatement(
"SELECT * FROM users WHERE email = ?"
);
stmt.setString(1, email);
Reference: OWASP SQL Injection Prevention Cheat Sheet
#### Important Issue
```markdown
**🟡 IMPORTANT - Testing: Missing test coverage for critical path**
The `processPayment()` function handles financial transactions but has no tests
for the refund scenario.
**Why this matters:**
Refunds involve money movement and should be thoroughly tested to prevent
financial errors or data inconsistencies.
**Suggested fix:**
Add test case:
```javascript
test('should process full refund when order is cancelled', () => {
const order = createOrder({ total: 100, status: 'cancelled' });
const result = processPayment(order, { type: 'refund' });
expect(result.refundAmount).toBe(100);
expect(result.status).toBe('refunded');
});
#### Suggestion
```markdown
**🟢 SUGGESTION - Readability: Simplify nested conditionals**
The nested if statements on lines 30-40 make the logic hard to follow.
**Why this matters:**
Simpler code is easier to maintain, debug, and test.
**Suggested fix:**
```javascript
// Instead of nested ifs:
if (user) {
if (user.isActive) {
if (user.hasPermission('write')) {
// do something
}
}
}
// Consider guard clauses:
if (!user || !user.isActive || !user.hasPermission('write')) {
return;
}
// do something
## Review Checklist
When performing a code review, systematically verify:
### Code Quality
- [ ] Code follows consistent style and conventions
- [ ] Names are descriptive and follow naming conventions
- [ ] Functions/methods are small and focused
- [ ] No code duplication
- [ ] Complex logic is broken into simpler parts
- [ ] Error handling is appropriate
- [ ] No commented-out code or TODO without tickets
### Security
- [ ] No sensitive data in code or logs
- [ ] Input validation on all user inputs
- [ ] No SQL injection vulnerabilities
- [ ] Authentication and authorization properly implemented
- [ ] Dependencies are up-to-date and secure
### Testing
- [ ] New code has appropriate test coverage
- [ ] Tests are well-named and focused
- [ ] Tests cover edge cases and error scenarios
- [ ] Tests are independent and deterministic
- [ ] No tests that always pass or are commented out
### Performance
- [ ] No obvious performance issues (N+1, memory leaks)
- [ ] Appropriate use of caching
- [ ] Efficient algorithms and data structures
- [ ] Proper resource cleanup
### Architecture
- [ ] Follows established patterns and conventions
- [ ] Proper separation of concerns
- [ ] No architectural violations
- [ ] Dependencies flow in correct direction
### Documentation
- [ ] Public APIs are documented
- [ ] Complex logic has explanatory comments
- [ ] README is updated if needed
- [ ] Breaking changes are documented
## Project-Specific Customizations
To customize this template for your project, add sections for:
1. **Language/Framework specific checks**
- Example: "When performing a code review, verify React hooks follow rules of hooks"
- Example: "When performing a code review, check Spring Boot controllers use proper annotations"
2. **Build and deployment**
- Example: "When performing a code review, verify CI/CD pipeline configuration is correct"
- Example: "When performing a code review, check database migrations are reversible"
3. **Business logic rules**
- Example: "When performing a code review, verify pricing calculations include all applicable taxes"
- Example: "When performing a code review, check user consent is obtained before data processing"
4. **Team conventions**
- Example: "When performing a code review, verify commit messages follow conventional commits format"
- Example: "When performing a code review, check branch names follow pattern: type/ticket-description"
## Additional Resources
For more information on effective code reviews and GitHub Copilot customization:
- [GitHub Copilot Prompt Engineering](https://docs.github.com/en/copilot/concepts/prompting/prompt-engineering)
- [GitHub Copilot Custom Instructions](https://code.visualstudio.com/docs/copilot/customization/custom-instructions)
- [Awesome GitHub Copilot Repository](https://github.com/github/awesome-copilot)
- [GitHub Code Review Guidelines](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests)
- [Google Engineering Practices - Code Review](https://google.github.io/eng-practices/review/)
- [OWASP Security Guidelines](https://owasp.org/)
## Prompt Engineering Tips
When performing a code review, apply these prompt engineering principles from the [GitHub Copilot documentation](https://docs.github.com/en/copilot/concepts/prompting/prompt-engineering):
1. **Start General, Then Get Specific**: Begin with high-level architecture review, then drill into implementation details
2. **Give Examples**: Reference similar patterns in the codebase when suggesting changes
3. **Break Complex Tasks**: Review large PRs in logical chunks (security → tests → logic → style)
4. **Avoid Ambiguity**: Be specific about which file, line, and issue you're addressing
5. **Indicate Relevant Code**: Reference related code that might be affected by changes
6. **Experiment and Iterate**: If initial review misses something, review again with focused questions
## Project Context
This is a generic template. Customize this section with your project-specific information:
- **Tech Stack**: [e.g., Java 17, Spring Boot 3.x, PostgreSQL]
- **Architecture**: [e.g., Hexagonal/Clean Architecture, Microservices]
- **Build Tool**: [e.g., Gradle, Maven, npm, pip]
- **Testing**: [e.g., JUnit 5, Jest, pytest]
- **Code Style**: [e.g., follows Google Style Guide]