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Audit a repository or broad subsystem for security, correctness, and maintenance risks. Trace how inputs reach affected behavior, verify suspected defects, and report confirmed findings with severity, triggers, impact, and practical remedies.
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Review resolved dependency versions and relevant code for security vulnerabilities. Get findings with evidence, impact, and targeted remedies, distinguishing published dependency warnings from exploit paths demonstrated in the project.
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Review a specified Marin pull request for newly introduced defects, repository instruction violations, and concrete maintenance problems. Receive findings backed by code evidence, with optional posting of review comments to GitHub.
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Default code-quality route for broad code review, PR review, maintainability, correctness, and regression-risk checks. Do not use as the primary route for dedicated OWASP/security audits, review-feedback handling, completion verification, AI-code cleanup, or TDD/test-first work.
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Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use when architecting complex backend systems or refactoring existing applications for better maintainability.
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E2E Testing Standards Guide. Use this rule when you need to write or execute E2E tests, including test naming conventions, test workflows, and test behavior templates. This rule should be used for end-to-end integration testing scenarios to ensure test standardization and maintainability. Example: When writing new E2E tests, use this rule to ensure proper test format and workflow compliance.
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Review code changes for correctness, security, and maintainability. Supports local git diffs (staged or working tree) and remote Pull Requests (by ID or URL). Use when the user asks to review code, check a PR, audit changes, or wants feedback on code quality before merging.
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Multi-dimensional code assessment across security, quality, performance, and maintainability with confidence-gated reporting (>=80%) and Router Contract generation.
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Multi-dimensional code review across correctness, security, performance, and maintainability with confidence-gated reporting and remediation loops.
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You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance.
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Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use when architecting complex backend systems or refactoring existing applications for better maintainability.
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Master software architect specializing in modern architecture patterns, clean architecture, microservices, event-driven systems, and DDD. Reviews system designs and code changes for architectural integrity, scalability, and maintainability. Use PROACTIVELY for architectural decisions.
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You are a code refactoring expert specializing in clean code principles, SOLID design patterns, and modern software engineering best practices. Analyze and refactor the provided code to improve its quality, maintainability, and performance.
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Tech debt assessment that identifies, classifies, and prioritizes maintainability problems — helping teams build a refactoring roadmap — drawing on twelve classic engineering books. Triggers when: user asks about tech debt, refactoring priorities, what to clean up first, or asks "why is this so hard to change?", "what should we fix first?", or "how do I justify refactoring to management?". Do NOT trigger for: server health checks, HTTP /health endpoints, Kubernetes probes, database health, or application uptime — "health" in those contexts is infrastructure, not code quality. Also not for single-function refactoring questions.
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PR code review that surfaces decay risks, design smells, and maintainability issues with concrete Symptom → Source → Consequence → Remedy findings, drawing on twelve classic engineering books. Triggers when: user asks to review code, check a PR, shares a diff or pastes code asking "does this look right?" / "any issues here?" / "ready to merge?", or asks for feedback on a function, class, or file. Also triggers when user mentions: code smells / refactoring / clean architecture / DDD / SOLID principles / Hyrum's Law / deep modules / tactical programming / conceptual integrity / Brooks's Law / Mythical Man-Month / second system effect. Do NOT trigger for: questions about how to write code from scratch, language syntax questions, or framework/tool questions where no existing code is shared.
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Behavioral guardrails for Codex coding work based on common user complaints. Use when Codex is asked to implement, modify, debug, review, test, or operate on a codebase and should avoid unsafe scope expansion, stale edits, fake completion, brittle edits, shallow debugging, superficial patch-on fixes, one-off special-case code, short-term design choices that hurt maintainability, blindly following incorrect user assumptions, poor dependency choices, generic product or UI output, over-mocked tests, noisy approvals, verbose status reports, or leaking internal reasoning into project artifacts or user-facing UI.
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Review MATLAB code for quality, performance, maintainability, and adherence to MathWorks coding standards. Uses check_matlab_code and matlab_coding_guidelines. Use when reviewing code, checking style, finding code smells, assessing quality, or preparing code for handoff or publication.
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Refactor code to improve structure and maintainability
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Simplifies and refines code for clarity, consistency, and maintainability while preserving all functionality. Use when asked to "simplify code", "clean up code", "refactor for clarity", "improve readability", or review recently modified code for elegance. Focuses on project-specific best practices.
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Analyze a software codebase for algorithmic complexity and performance hotspots, then propose or implement safe optimizations without breaking behavior. Use when Codex is asked to scan many files, find inefficient loops, nested iteration, repeated scans, costly rendering/recomputation, N+1 queries, avoidable O(n^2) or O(n) operations, or reduce complexity such as O(n^2) to O(n log n) / O(n), while preserving tests, APIs, outputs, and maintainability.
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Performs comprehensive codebase analysis covering architecture, code quality, security, performance, testing, and maintainability. Use when user wants to audit code quality, identify technical debt, find security issues, assess test coverage, or get a codebase health check.
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Clean-code engineering standards for writing, refactoring, and reviewing code in any programming language. Use this whenever the user asks to write clean code, follow clean-code principles, refactor for clarity, improve naming, reduce complexity or duplication, separate concerns, tighten error handling, work test-first or do TDD, or otherwise raise code quality, readability, and maintainability. Also use when writing or reviewing Operately APIs or Ecto queries. Apply these rules by default when producing or changing code for a quality-conscious user.
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Audits cross-cutting codebase health, security and maintainability; not a single-change review or specialist audit.
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Rigorously review code or pull requests for correctness, security, accessibility, maintainability, tests, and edge cases. Use when users request a critical code review, want a guided walkthrough of findings, need implementer-facing feedback, or want to prepare, create, or submit a GitHub pull request review.
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