---
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

1. **Maintainability over Speed**: Prioritize long-term code health over initial development velocity
2. **Simplicity First**: Choose the simplest solution that meets requirements (YAGNI principle)
3. **Design Convergence**: Deliver the current required outcome with the least new design surface. Selecting persistent state, public or cross-boundary contracts, behavioral modes, reusable abstractions, or component splits carries enough surface to justify the full convergence process first.
4. **Explicit over Implicit**: Make intentions clear through code structure and naming
5. **Delete over Comment**: Remove unused code instead of commenting it out

## Code Quality

### Continuous Improvement
- Refactor related code inside the accepted outcome and governing boundaries when it reduces the change's risk or maintenance cost
- Improve code structure incrementally
- Keep the codebase lean and focused
- Delete code proven obsolete by the requested change after checking its callers; report uncertain or out-of-scope cleanup separately

### Readability
- Use meaningful, descriptive names drawn from the problem domain
- Use full words in names; abbreviations are acceptable only when widely recognized in the domain
- Use descriptive names; single-letter names are acceptable only for loop counters or well-known conventions (i, j, x, y)
- Extract magic numbers and strings into named constants
- Keep code self-documenting where possible

## Function Design

### Parameter Management
- Group related positional parameters into an object, struct, or dictionary when call-site clarity or coordinated evolution requires it. Retain positional parameters when their order is conventional and the call remains clear, or an external/public signature requires them
- Preserve external/public signatures unless their migration is part of the accepted outcome or governing artifact

### Single Responsibility
- Each function should do one thing well
- Extract a function when independently changing responsibilities or obscured control flow make the current unit harder to understand, verify, or reuse; retain a cohesive domain flow when extraction would create artificial coupling
- 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
- Use early returns or extraction when nesting obscures state transitions or decision ownership; retain nested structure when it maps the domain decision more clearly

## 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 via Parameterized Dependencies
- Inject external dependencies as parameters (constructor injection for classes, function parameters for procedural/functional code)
- Depend on abstractions, not concrete implementations
- Minimize inter-module dependencies
- Facilitate testing through mockable dependencies

## Reference Representativeness

### Verifying References Before Adoption
When adopting patterns, APIs, or dependencies from existing code:
- **IF** a reference sample covers only nearby files → **THEN** confirm the pattern is representative by checking relevant repository usage before adopting
- **IF** multiple approaches coexist in the repository → **THEN** identify the majority pattern and make a deliberate choice — selecting whichever is nearest is insufficient
- **IF** adopting an external dependency (library, plugin, SDK) → **THEN** verify repository-wide usage and compatibility evidence; when that evidence cannot determine the required version, record the unresolved version decision and the evidence needed to settle it
- **IF** following an existing pattern → **THEN** state the reason for following it when an alternative exists (e.g., consistency with surrounding code, avoiding breaking changes, pending coordinated update)

### Principle
Nearby code is a starting point for investigation, not a sufficient basis for adoption. Verify that what you reference is representative of the repository's conventions and current best practices before using it as a model.

## 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 through profiling
- When performance issues are measurable
- Optimize only after measurable bottlenecks are identified, not 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
- Split a file when it contains independently changing responsibilities or creates material navigation, coupling, or verification cost; retain a cohesive file when splitting would add avoidable coupling or navigation cost

## Commenting Principles

### Default: code first
Names, types, and structure are the primary medium. A comment earns its place only by carrying information the code itself cannot express. When in doubt, improve the name instead of adding a comment.

### The test for every comment
A comment is justified only if it answers one of these:
- **Why**: reasoning, trade-off, or constraint behind a non-obvious decision
- **Limitation / edge case**: a boundary a reader cannot infer from the code
- **Public API contract**: behavior, inputs, outputs of an exported interface

One comment per decision. If a comment restates what the names and control flow already show, delete it and rename instead.

### Comment Scope
- Comment the why, limits, and public contracts (per the test above); let names and structure carry everything else, including the "how"
- Record historical context in version control commit messages, not in comments
- Delete commented-out code (retrieve from git history when needed)

### Comment Quality
- Base comments on stable rationale, limits, and contracts rather than dates, versions, or temporary state
- 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**: Make the smallest sufficient improvement in each increment

### Refactoring Triggers
- Code duplication (DRY principle)
- Functions that contain independently changing responsibilities or obscured control flow
- Complex conditional logic
- Unclear naming or structure

## Security Principles

### Secure Defaults
- Store credentials and secrets through environment variables or dedicated secret managers
- Use parameterized queries (prepared statements) for all database access
- Use established cryptographic libraries provided by the language or framework
- Generate security-critical values (tokens, IDs, nonces) with cryptographically secure random generators
- Encrypt sensitive data at rest and in transit using standard protocols

### Input and Output Boundaries
- Validate all external input at system entry points for expected format, type, and length. External input includes request data, external service responses, model or tool output, and stored data whose writer is untrusted or whose consumer needs a guarantee the store does not make
- When a change alters a boundary where external content or model output selects a tool's action, target, or destination, verify that those values cannot exceed the operation scope and access rights already granted to the caller
- Encode output appropriately for its rendering context (HTML, SQL, shell, URL)
- Return only information necessary for the caller in error responses; log detailed diagnostics server-side

### Access Control
- Apply authentication to all entry points that handle user data or trigger state changes
- Verify authorization for each resource access, not only at the entry point
- Grant only the permissions required for the operation (files, database connections, API scopes)
- For changes involving identity or protected resources, prioritize authentication and per-resource authorization review

For concrete detection patterns used by security review, see `references/security-checks.md`.