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How to Do Code Review with Codex CLI

Cowrie
Cowrie
Dev @ Bswen

Code review workflow

Problem

When I used Codex CLI to generate code, I assumed the output was correct. I skipped proper review because “AI wrote it, it should be fine.”

But then I started finding problems in production:

  • Hardcoded API keys in config files
  • Missing error handling that crashed on edge cases
  • Inconsistent patterns across files
  • Regression bugs that broke existing features

Traditional code review processes aren’t designed for AI-generated code, which has unique failure modes.

What happened?

AI-generated code has specific problems that humans might not catch:

1. Subtle logic errors that pass initial review 2. Hardcoded values scattered throughout 3. Missing error handling in “happy path” code 4. Inconsistent patterns when Codex invents solutions differently each time 5. Regression bugs when new code breaks existing functionality

I was relying on Codex to self-review, which doesn’t work well. AI cannot effectively critique its own output.

How to solve it?

I implemented a multi-layered code review workflow specifically for AI-generated code.

Layer 1: Use Subagents for Dedicated Reviews

Instead of asking Codex to review its own code, I create specialized subagents focused exclusively on code review:

Code reviewer skill template
## Purpose
Perform rigorous code review focused on AI-generated code anti-patterns.


## Review Checklist


### AI-Specific Issues
- [ ] No hardcoded values (API keys, URLs, timeouts)
- [ ] Proper error handling (not just console.log)
- [ ] No unused imports or dead code
- [ ] Consistent naming conventions
- [ ] No mutation where immutability expected


### Security
- [ ] Input validation present
- [ ] No SQL injection vulnerabilities
- [ ] No XSS vulnerabilities
- [ ] Secrets from environment variables


### Code Quality
- [ ] Functions under 50 lines
- [ ] Files under 800 lines
- [ ] No deep nesting (>4 levels)
- [ ] Clear, descriptive names


## Instructions
Be critical - this is not the time for polite feedback.
Flag issues as CRITICAL, HIGH, MEDIUM, or LOW priority.
Stop and require fixes for CRITICAL and HIGH issues.

Layer 2: Use Multiple AI Models

Different models have different strengths:

  • Claude: Strong at reasoning about edge cases and security
  • Gemini: Good at catching logical inconsistencies
  • Codex (self-review): Understands generated code context

Using multiple models creates a “jury” effect where weaknesses in one model get caught by others.

Layer 3: Milestone-Based Review Workflow

Break development into milestones and review at each checkpoint:

Milestone review flow
Milestone 1: Initial Implementation
  ↓
[Subagent Review] → Fix Issues
  ↓
Milestone 2: Additional Features
  ↓
[Subagent Review] → Fix Issues
  ↓
Final Review + Unit Tests

This prevents compounding errors where bad code becomes foundation for more code.

Layer 4: Generate Unit Tests

Have Codex create unit tests alongside functionality:

Unit test generation prompt
Write comprehensive unit tests for the authentication module:
- Test successful login
- Test invalid credentials
- Test token expiration
- Test password reset flow
- Mock all external dependencies
- Achieve 80%+ coverage

Tests lock in correct behavior and catch regressions.

The reason

I think the key reason is that AI-generated code has patterns humans don’t naturally look for.

When I review human code:

  • I expect logical thinking
  • I expect consistent patterns
  • I expect learned best practices

When I review AI code:

  • There might be hallucinated imports
  • There might be confident but wrong logic
  • There might be inconsistent solutions across files
  • There might be “it works on my machine” assumptions

Dedicated subagents specifically look for these AI anti-patterns. Multiple models catch different types of issues. Milestone reviews prevent error accumulation.

Common mistakes to avoid

1. Relying solely on self-review

AI cannot effectively critique its own output. Use separate review agents.

2. Reviewing only at the end

Errors compound. A bad foundation at milestone 1 becomes a disaster by milestone 10.

3. Skipping unit tests

“It works” isn’t enough. Lock in behavior with tests to prevent regressions.

4. Using only one model

Different AI models catch different issues. Diversify your review perspectives.

5. Not fixing issues immediately

Gaps found at milestone 1 should be fixed before milestone 2 starts.

Summary

In this post, I showed how to implement effective code review for Codex CLI-generated code. The key point is using dedicated subagents, multiple AI model perspectives, milestone-based checkpoints, and automated unit testing.

Start by creating a code-reviewer skill and use it at every development milestone. Your future self will thank you when production bugs don’t appear.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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