| name | coding-principles |
| description | Language-agnostic coding principles for maintainability, readability, and quality. Use when implementing features, refactoring code, or reviewing code quality. |
Language-Agnostic Coding Principles
Core Philosophy
- Maintainability over Speed: Prioritize long-term code health over initial development velocity
- Simplicity First: Choose the simplest solution that meets requirements (YAGNI principle)
- Explicit over Implicit: Make intentions clear through code structure and naming
- Delete over Comment: Remove unused code instead of commenting it out
Code Quality
Continuous Improvement
- Refactor as you go - don't accumulate technical debt
- Improve code structure incrementally
- Keep the codebase lean and focused
- Delete unused code immediately
Readability
- Use meaningful, descriptive names for variables and functions
- Avoid abbreviations unless they are widely recognized
- Keep code self-documenting where possible
- Write code that humans can easily understand
Function Design
Parameter Management
- Recommended: 0-2 parameters per function
- For 3+ parameters: Use objects, structs, or dictionaries to group related parameters
- Example (conceptual):
// Instead of: createUser(name, email, age, city, country) // Use: createUser(userData)
Single Responsibility
- Each function should do one thing well
- Keep functions small and focused (typically < 50 lines)
- Extract complex logic into separate, well-named functions
- Functions should have a single level of abstraction
Function Organization
- Pure functions when possible (no side effects)
- Separate data transformation from side effects
- Use early returns to reduce nesting
- Avoid deep nesting (maximum 3 levels)
Error Handling
Error Management Principles
- Always handle errors: Log with context or propagate explicitly
- Log appropriately: Include context for debugging
- Protect sensitive data: Mask or exclude passwords, tokens, PII from logs
- Fail fast: Detect and report errors as early as possible
Error Propagation
- Use language-appropriate error handling mechanisms
- Propagate errors to appropriate handling levels
- Provide meaningful error messages
- Include error context when re-throwing
Dependency Management
Loose Coupling
- Inject external dependencies as parameters
- Avoid direct imports within functions when possible
- Depend on abstractions, not concrete implementations
- Minimize inter-module dependencies
Parameterized Dependencies
- Pass dependencies explicitly through function parameters
- Use constructor parameter injection for class-based languages
- Use function parameters for functional/procedural approaches
- Facilitate testing through mockable dependencies
Performance Considerations
Optimization Approach
- Measure first: Profile before optimizing
- Focus on algorithms: Algorithmic complexity > micro-optimizations
- Use appropriate data structures: Choose based on access patterns
- Resource management: Handle memory, connections, and files properly
When to Optimize
- After identifying actual bottlenecks
- When performance issues are measurable
- Not prematurely during initial development
Code Organization
Structural Principles
- Group related functionality: Keep related code together
- Separate concerns: Domain logic, data access, presentation
- Consistent naming: Follow project conventions
- Module cohesion: High cohesion within modules, low coupling between
File Organization
- One primary responsibility per file
- Logical grouping of related functions/classes
- Clear folder structure reflecting architecture
- Avoid "god files" (files > 500 lines)
Commenting Principles
When to Comment
- Document "what": Describe what the code does
- Explain "why": Clarify reasoning behind decisions
- Note limitations: Document known constraints or edge cases
- API documentation: Public interfaces need clear documentation
When NOT to Comment
- Avoid describing "how" (the code shows that)
- Don't include historical information (use version control)
- Remove commented-out code (use git to retrieve old code)
- Avoid obvious comments that restate the code
Comment Quality
- Keep comments concise and timeless
- Update comments when changing code
- Use proper grammar and formatting
- Write for future maintainers
Refactoring Approach
Safe Refactoring
- Small steps: Make one change at a time
- Maintain working state: Keep tests passing
- Verify behavior: Run tests after each change
- Incremental improvement: Don't aim for perfection immediately
Refactoring Triggers
- Code duplication (DRY principle)
- Functions > 50 lines
- Complex conditional logic
- Unclear naming or structure
Clean Code Guidelines
Naming Conventions
- Use domain language in names
- Be specific and descriptive
- Avoid single-letter names (except loop counters)
- Follow language-specific conventions (camelCase, snake_case, etc.)
Code Structure
- Prefer early returns over nested conditionals
- Extract magic numbers into named constants
- Remove debug statements before committing
- Keep functions at a single level of abstraction
Code Smells to Avoid
- Long parameter lists
- Deeply nested conditionals
- Duplicated code blocks
- Unclear variable names
- Large modules, classes, or functions
Testing Considerations
Testability
- Write testable code from the start
- Avoid hidden dependencies
- Keep side effects explicit
- Design for parameterized dependencies
Test-Driven Development
- Write tests before implementation when appropriate
- Keep tests simple and focused
- Test behavior, not implementation
- Maintain test quality equal to production code
Security Principles
General Security
- Store secrets in environment variables or secret managers
- Validate all external input
- Use parameterized queries for databases
- Follow principle of least privilege
Data Protection
- Encrypt sensitive data at rest and in transit
- Sanitize user input
- Avoid logging sensitive information
- Use secure random generators for security-critical operations
Documentation
Code Documentation
- Document public APIs and interfaces
- Include usage examples for complex functionality
- Maintain README files for modules
- Keep documentation in sync with code
Architecture Documentation
- Document high-level design decisions
- Explain integration points
- Clarify data flows and boundaries
- Record trade-offs and alternatives considered
Version Control Practices
Commit Practices
- Make atomic, focused commits
- Write clear, descriptive commit messages
- Commit working code (passes tests)
- Avoid committing debug code or secrets
Code Review Readiness
- Self-review before requesting review
- Keep changes focused and reviewable
- Provide context in pull request descriptions
- Respond to feedback constructively
Language-Specific Adaptations
While these principles are language-agnostic, adapt them to your specific programming language:
- Static typing: Use strong types when available
- Dynamic typing: Add runtime validation
- OOP languages: Apply SOLID principles
- Functional languages: Prefer pure functions and immutability
- Concurrency: Follow language-specific patterns for thread safety
Continuous Learning
- Stay updated with language-specific best practices
- Learn from code reviews
- Study well-maintained open source projects
- Regularly refactor and improve existing code