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Principal Code Reviewer & Architecture Auditor

Conduct rigorous architectural code reviews identifying memory leaks, race conditions, and typing holes.

Compatibility & Specs

Compatible AI Models
ClaudeChatGPTMicrosoft Copilot
Last UpdatedMar 4, 2026
Customizable Variables4 parameters

How to Use This Prompt

Follow this 3-step workflow to extract high-signal responses from any compatible AI model.

01

1. Tailor the Parameters

Use the interactive customizer above to substitute the bracketed placeholders with your exact context, requirements, and constraints.

02

2. Send to AI Model

Copy the prompt and paste it into Claude, ChatGPT, Gemini, or Copilot. These models follow structured multi-step constraints reliably.

03

3. Review and Iterate

Review the output against the verified benchmark below. Follow up in the conversation to stress-test edge cases or refine tone.

Prompt Variables & Parameters

Reference breakdown of every dynamic variable embedded in this prompt template.

PlaceholderParameter NameTypeStatusDescription & Guidance
[language_stack]Language & StacktextRequiredThe technology stack of the provided codeDefault: TypeScript / Next.js 16 / React 19
[primary_objective]Primary ObjectivetextRequiredWhat this piece of code is intended to accomplishDefault: Safe asynchronous state synchronization with localStorage and cross-tab event listeners
[strictness_level]Strictness LevelselectRequiredDepth of critiqueDefault: Production Hardened (Staff Level)
[code_to_review]Code to ReviewtextareaRequiredPaste the raw code snippet or componentDefault: export function useStoredToken() { const [token, setToken] = useState(null); useEffect(() => { setToken(localStorage.getItem('token')); window.addEventListener('storage', () => { setToken(localStorage.getItem('token')); }); }, []); return token; }

Example Execution & Benchmark Output

Sample input arguments and the verified AI response demonstrating expected quality and formatting.

Sample VariablesInputs
[language_stack]
TypeScript / React 19
[primary_objective]
Safe asynchronous state synchronization with localStorage and cross-tab event listeners
[strictness_level]
Production Hardened (Staff Level)
[code_to_review]
export function useStoredToken() { ... }
Expected AI ResponseVerified Result
### 1. Executive Summary **Verdict: UNACCEPTABLE FOR PRODUCTION.** The implementation introduces SSR hydration mismatches, leaks global event listeners on unmount, and suffers from cascading re-renders. ### 2. Critical Bugs & Flaws - **Memory Leak**: `window.addEventListener('storage')` is registered without a cleanup function returning from `useEffect`. - **SSR Hydration Failure**: Accessing `localStorage` directly causes mismatch between server HTML and client initial render. ### 3. Refactored Implementation (Using React 19 `useSyncExternalStore`) ```typescript import { useSyncExternalStore } from 'react'; function subscribe(callback: () => void) { window.addEventListener('storage', callback); return () => window.removeEventListener('storage', callback); } function getSnapshot(): string | null { return typeof window !== 'undefined' ? localStorage.getItem('token') : null; } function getServerSnapshot(): string | null { return null; } export function useStoredToken(): string | null { return useSyncExternalStore(subscribe, getSnapshot, getServerSnapshot); } ```

Best Use Cases

Scenarios and roles where this prompt produces maximum leverage.

Engineering leads reviewing high-risk pull requests
Developers refactoring legacy modules into modern architecture
Auditing third-party libraries or internal shared packages

Tips for Best Results

Techniques to elevate response fidelity

  • •Provide rich background context rather than one-sentence inputs to receive deep, non-generic responses.
  • •Engage in multi-turn conversation: use the initial output as a baseline, then ask the AI to sharpen specific sections.
  • •Prompt the model to highlight any hidden assumptions or missing trade-offs in its recommendations.

Common Mistakes to Avoid

Frequent failure modes and anti-patterns

  • •Giving minimal context and expecting nuanced, expert-level strategic output.
  • •Not validating factual references, citations, or statistical claims with verified primary sources.
  • •Skipping the customization step and pasting raw bracketed template variables into the AI chat.

Part of Curated Collections

This prompt is sequenced as part of these goal-oriented workflows

View all collections

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