Define your tech stack and get a comprehensive code review checklist, PR template, and team coding standards document.
Paste into any LLM with your stack details. Use the output as your team's code review bible.
You are a staff engineer who has established code review practices at high-performing engineering teams. Tech stack: [LANGUAGES AND FRAMEWORKS] Team size: [NUMBER OF DEVELOPERS] Current challenge: [WHAT'S GOING WRONG WITH CODE REVIEWS] Deployment frequency: [HOW OFTEN YOU SHIP] Build a complete code review system: **1. Code Review Checklist** For every PR, reviewers should check: - Correctness (does it do what it's supposed to?) - Security (OWASP top 10, input validation, auth checks) - Performance (N+1 queries, memory leaks, unnecessary computation) - Readability (naming, structure, comments where needed) - Testing (coverage, edge cases, test quality) - Error handling (graceful failures, logging, user-facing messages) - Architecture (separation of concerns, dependency direction) - Accessibility (if frontend) - Documentation (API changes, README updates) For each item: provide specific things to look for in [YOUR TECH STACK]. **2. PR Template** - What changed and why - How to test - Screenshots (if UI) - Checklist for the author - Deployment considerations - Related tickets/issues **3. Review Etiquette Guide** - How to give feedback (specific, kind, actionable) - How to receive feedback (without ego) - When to approve vs request changes vs comment - Nit vs blocking distinction - Response time expectations **4. Coding Standards** - Naming conventions (variables, functions, files, classes) - File and folder structure - Import ordering - Error handling patterns - Logging standards - Testing conventions (what to test, naming, structure) - Git commit message format - Branch naming convention **5. Automation** - Linting rules to enforce automatically - Pre-commit hooks - CI checks that should gate merging - Automated code quality metrics Make it specific to our stack. No generic advice.
"Code Review Checklist & Standards" applies research-backed prompting principles: audience specification and depth requirements. These are the same techniques used by professional prompt engineers to get predictable, high-quality results. You can expect production-quality code that handles edge cases and follows your stack conventions - the kind of result that normally requires several rounds of prompt refinement.
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When you use "Code Review Checklist & Standards" with ChatGPT, Claude, or Gemini, here is what to expect in the AI output.
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