Generic Code Review Instructions
**Customization Tip**: Change to your preferred language by replacing "English" with "Portuguese (Brazilian)", "Spanish", "French", etc.
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.ordersArchitecture 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]