Agent Skills: Language-Agnostic Coding Principles

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. Explicit over Implicit: Make intentions clear through code structure and naming
4. Delete over Comment: Remove unused code instead of commenting it out

Code Quality

Continuous Improvement

• Refactor related code within each change set — address style, naming, or structure issues in the files being modified
• Improve code structure incrementally
• Keep the codebase lean and focused
• Delete unused code immediately

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

• 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
• Keep nesting to a maximum of 3 levels; use early returns or extracted functions to flatten deeper nesting

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 referencing only 2-3 nearby files → THEN confirm the pattern is representative by checking usage across the repository 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 distribution for the same dependency; if the appropriate version cannot be determined from repository state alone, escalate
• 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
• 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

Comment Scope

• Comment the "what" and "why"; the code itself communicates the "how"
• Record historical context in version control commit messages, not in comments
• Delete commented-out code (retrieve from git history when needed)
• Write comments that add information beyond what the code states

Comment Quality

• Write comments that remain accurate regardless of future code changes; avoid references to 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: Don't aim for perfection immediately

Refactoring Triggers

• Code duplication (DRY principle)
• Functions > 50 lines
• Complex conditional logic
• Unclear naming or structure

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

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
• 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)

Knowledge Cutoff Supplement (2026-03)

• OWASP Top 10:2025 shifted from symptoms to root causes; added "Software Supply Chain Failures" (A03) and "Mishandling of Exceptional Conditions" (A10)
• Recent research indicates AI-generated code shows elevated rates of access control gaps — treat authentication and authorization as high-priority review targets
• OpenSSF published "Security-Focused Guide for AI Code Assistant Instructions" — recommends language-specific, actionable constraints over generic advice
• For detailed detection patterns, see references/security-checks.md

Documentation

Code Documentation

• Document public APIs and interfaces
• Include usage examples for complex functionality
• Maintain README files for modules
• Update documentation in the same commit that changes the corresponding behavior

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)
• Commit only production-ready code; store secrets in environment variables or secret managers

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