Code Architecture Standards
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Writing robot code is more than just making things move — it’s about building a maintainable system that your team can understand, modify, and debug efficiently. Clean architecture ensures that your software scales with complexity, prevents hidden bugs, and makes it easier for new team members to contribute. Good coding practices turn a one-person effort into a team-wide advantage.

Standards
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- Module organization  
  Organize code into logical modules and packages that mirror your robot's subsystems. Separate drivetrain, intake, lift, and sensor logic into distinct directories or classes. This structure allows each module to be tested independently and makes upgrades easier without affecting unrelated code.

- Naming conventions  
  Use consistent, descriptive names for classes, functions, and variables. For example, `LiftMotor`, `driveForward()`, or `isBallDetected()`. Avoid ambiguous or single-letter names. Clear naming improves readability, reduces errors, and helps teammates understand the purpose of each element quickly.

- Comment and documentation expectations  
  Comments should explain why code exists, not what it does. Every class, method, and critical function should have concise documentation describing its purpose, inputs, outputs, and assumptions. README files or internal wikis provide overviews of system architecture, dependencies, and setup instructions.

- State machine or control flow patterns  
  Use consistent control patterns to manage robot behavior. State machines, command-based frameworks, or event-driven architectures help separate decision-making from actuation. Predictable control flow reduces bugs and makes it easier to reason about robot behavior under different conditions.

- Example structure:  

  org/  
    team/  
      robot/  
        subsystems/  
        commands/  
        constants/  

Best Practices
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Writing great code is a team sport. Regular code reviews allow teammates to catch errors early, share insights, and ensure consistency. Pair programming is especially valuable for mentoring new members and solving complex problems collaboratively. Encourage knowledge sharing through wikis, tutorials, and internal documentation. Teams that document design decisions, track issues, and discuss trade-offs consistently produce more reliable, maintainable, and scalable robot code.