Code Review Accelerator (Fast, Fair, Actionable)
This prompt helps you review code critically without being nitpicky, surface architectural issues early, and communicate feedback that helps developers improve rather than frustrate them. It balances rigor with speed.
GPT / Claude5 variables
Prompt
Review this code carefully and fairly. Separate real issues from style opinions.
CODE PURPOSE: {PURPOSE}
PROGRAMMING LANGUAGE: {LANGUAGE}
CONTEXT: {CONTEXT}
CODE TO REVIEW:
{CODE}
RELEVANT TESTS:
{TESTS}
Review this code for:
1) CORRECTNESS (does it do what it's supposed to?)
- Does it handle edge cases?
- Are there any logic errors?
- Does it match the branch description?
2) PERFORMANCE & SCALABILITY
- Any obvious performance issues?
- Will it scale with expected load?
- Could it be optimized?
3) MAINTAINABILITY & CLARITY
- Is it clear what it does?
- Could future developers understand this?
- Is there duplication that could be extracted?
4) SECURITY & SAFETY
- Any security vulnerabilities?
- Proper error handling?
- Any dangerous assumptions?
5) TESTING
- Is test coverage adequate?
- Do tests cover edge cases?
- Are tests clear?
PROVIDED FEEDBACK FORMAT:
SUMMARY
[One paragraph overview]
CRITICAL ISSUES (block approval)
- Issue: [What the problem is]
Why: [Why this matters]
Fix: [Specific suggestion]
IMPORTANT IMPROVEMENTS (should fix before merge)
- Suggestion: [What to improve]
Why: [Why this is important]
Example: [How to implement]
NICE-TO-HAVE OBSERVATIONS (consider for next refactor)
- Note: [Observation]
Alternative: [Different approach]
STYLE & STANDARDS
[Any consistency notes]
CONFIDENCE LEVEL
[Your confidence in this code going to production]
Approval: [approve / request changes / comment]
Purpose: {PURPOSE}
Language: {LANGUAGE}Quick brief
Purpose
Conduct thorough code reviews efficiently, catching real issues while respecting developers' time with clear, actionable feedback.
Expected output
A structured code review with: brief summary of what the code does, any critical issues blocking approval, architectural or performance concerns to address, specific improvement suggestions with examples, stylistic observations and standards enforcement, and the reviewer's confidence level in the code.
Customize before copying
Replace these placeholders with your own context before you run the prompt.
{PURPOSE}{LANGUAGE}{CONTEXT}{CODE}{TESTS}
Works well with
GPT
Claude
Variations
Focus only on architecture, ignore implementation details.
Make it educational—explain the 'why' behind each suggestion.
Add security-focused review focusing on vulnerabilities.
Create a checklist-style review for consistency.
What this prompt helps you do
This prompt helps you review code critically without being nitpicky, surface architectural issues early, and communicate feedback that helps developers improve rather than frustrate them. It balances rigor with speed.
When to use it
Use when reviewing pull requests, especially in fast-moving teams where reviews can become bottlenecks. Most valuable when you want consistency in standards and clear communication.
How it works
The prompt reviews code for correctness, performance, maintainability, and security. It separates critical issues from nice-to-haves, explains the 'why' behind suggestions, and proposes concrete improvements. It avoids pedantic style comments in favor of meaningful feedback.
Best practices
Read for function first, style second. Ask 'why' if something looks odd rather than assuming it's wrong. Suggest improvements, don't demand them. Acknowledge good solutions and learning progress. Check tests before diving into code. Keep feedback constructive and actionable.
Common mistakes
Bikeshedding on variable names instead of logic. Reviewing style instead of substance. Long feedback threads that overwhelm the developer. Not acknowledging context (maybe there's a good reason for the approach). Blocking on improvement suggestions that aren't bugs.
What you should expect back
A structured code review with: brief summary of what the code does, any critical issues blocking approval, architectural or performance concerns to address, specific improvement suggestions with examples, stylistic observations and standards enforcement, and the reviewer's confidence level in the code.
Limitations
Can't see the full system context or understand long-term architectural plans. Doesn't replace pair programming for complex logic. Can't evaluate user-facing impact without seeing it. Suggestions need to be weighed against project momentum.
Model notes
Works best with GPT-4 and Claude for code-heavy reviews. Provide complete code in markdown blocks with language specified. Mention the code's purpose and any relevant context. Include test code if available.
Real-world applications
Senior engineers use this to review junior developers' code while being educational. Teams use this for consistent code quality standards. Open-source projects use this to review community contributions fairly. Technical leads use this for architectural consistency.
How to tell if it worked
Code quality improves over time. Developers feel feedback is fair and educational. Review cycle time decreases. Fewer bugs make it to production from these components.
Where to go next
Use Technical Documentation Writer to document complex code. Combine with Bug Hunter if reviewing error handling. Reference Testing Framework Designer for robust test coverage.