Professional Code Rules
Battle-tested development standards for AI-powered coding workflows
Transform your development process with proven AI coding standards.
These rules represent years of experience building production AI systems. Implement them in your IDE to execute systematic, high-quality development campaigns.
Mindset and General Approach
Foundation principles for professional AI-powered development
Mindset and General Approach 1
Be Thorough:
Provide complete solutions and detailed explanations when coding. Don't hold back on relevant information – being explicit ensures no important detail is omitted. It's better to "show everything you've got" in responses and code suggestions than to leave potential gaps in functionality or reasoning.
Mindset and General Approach 2
Quality Over Speed:
Prioritize correctness and code quality over haste. It's better to slow down and produce clean, robust code than to rush out a sloppy solution. Focus on writing well-structured, error-free code even if it takes a bit longer – the goal is a reliable, maintainable, and modular result, not just a quick fix.
Project Context and Planning
Strategic planning and context management for successful projects
Project Context and Planning 1
Understand Project Goals and Standards:
Before writing any code, review the project documentation (commands, architecture guides, requirements, style guides, existing classes and functions, etc.) to fully understand the goals and conventions. Ensure your solutions align with the project's intended design, architecture, and coding standards. For example, check an AGENTS.md or README-like file for the system architecture and design principles so your contributions fit seamlessly into the project.
Project Context and Planning 3
Plan Ahead (Think Before Coding):
Invest time in upfront planning of features and fixes. Use planning files and to-do lists (e.g., a high-level PLANNING.md and a detailed TASKS.md) to break the project into small, actionable steps before writing code. Clear plans and task breakdowns will guide the AI and reduce confusion during implementation. Where possible, adopt a plan-then-act workflow: have the AI first propose a plan or outline for the task, then review and approve it before execution. This separation of planning and coding helps prevent the AI from making unintended changes by ensuring everyone agrees on the approach before any code is written.
Coding Standards and Implementation
Professional coding practices for maintainable, scalable systems
Coding Standards and Implementation 1
Follow Coding Conventions:
Adhere strictly to the project's coding style and conventions. Use consistent naming, formatting, and file structure as defined by the project guidelines. Follow established patterns for logging (use appropriate log levels with clear messages) and error handling. Consistency helps your code blend naturally with the codebase and makes it easier for others to read and maintain.
Coding Standards and Implementation 2
Prefer Simple, Clear Solutions:
Opt for straightforward, maintainable approaches over overly clever or complex ones. Simplicity makes the code easier to debug and aligns with expected behavior. Avoid convoluted logic when a clear, standard solution exists – the simplest solution that fulfills the requirements is usually best. Write code for humans to read as well as for machines to run.
Coding Standards and Implementation 3
Avoid Code Duplication (DRY):
Don't rewrite functionality that already exists. Always check for existing utilities, functions, or patterns before adding new code. Reuse or adapt existing logic instead of duplicating it to reduce maintenance burden and potential bugs.
Coding Standards and Implementation 4
Focus on the Current Task:
Tackle only the issue or feature at hand and limit changes to relevant areas of the codebase. Avoid refactoring unrelated code unless absolutely necessary. Implement only what is requested to prevent introducing new bugs or confusion.
Coding Standards and Implementation 5
Maintain Architectural Consistency:
Stick to the project's chosen tech stack and architecture decisions. Don't introduce new frameworks, libraries, or storage technologies without approval. When adding new files or modules, update architecture documentation (e.g., AGENTS.md) and remove deprecated files to keep the system map accurate.
Coding Standards and Implementation 6
Provide Production-Ready Code:
Deliver fully implemented, production-quality code rather than placeholders or partial stubs. Write actual implementations, not pseudo-code, unless explicitly asked. Ensure the final solution works, meets requirements, and passes existing tests.
Coding Standards and Implementation 7
Refactor and Organize Code:
Keep the codebase clean, modular, and well-organized. If a file or function becomes large, split it into logical pieces (soft limit ~300 lines per file). Remove temporary scripts or debug code once they're no longer needed.
Coding Standards and Implementation 8
Design for Multi-Platform Compatibility:
Use a modular, provider-agnostic design when integrating with language-model APIs. Abstract LLM-specific logic behind interfaces so the system can switch between providers (OpenAI, Anthropic, Gemini, Llama, Mistral, etc.) without major changes.
Security and Privacy Considerations
Essential security practices for AI-powered development
Security and Privacy Considerations 1
Protect Sensitive Data:
Never hard-code real API keys, passwords, or confidential data. Exclude secrets from AI prompts, use ignore files (e.g., .cursorignore) to keep sensitive files out of context, and close private files before invoking the AI.
Security and Privacy Considerations 2
Use Privacy Features:
Enable privacy and data-protection modes (e.g., Cursor's privacy mode, Windsurf's "zero data" mode) when working with proprietary or sensitive code to minimize what's sent to external servers.
Security and Privacy Considerations 3
Validate External Code Suggestions:
Treat AI-suggested code and new dependencies with caution. Only use reputable packages, verify authenticity, and run security audits when possible. Vet external code like you would code from an unknown contributor – trust but verify every piece.
Ready to Execute an MCP Campaign?
Implement these code rules in your IDE and launch a systematic development campaign using Red Corsair's MCP integration
MCP Campaign Benefits
Systematic Quality
Enforce coding standards across your entire development workflow
Accelerated Development
Reduce technical debt and improve code maintainability
Team Alignment
Ensure consistent practices across your development team
Implementation Guide
How to integrate these code rules into your development workflow
Copy to Your IDE
Save these rules as a reference document in your IDE or code editor for quick access during development.
Configure AI Tools
Set up your AI coding assistant (Cursor, Windsurf, etc.) with these rules as system prompts or workspace guidelines.
Launch MCP Campaign
Use Red Corsair's MCP integration to systematically apply these standards across your entire project.