codex

name	codex
description	Get code reviews and second opinions using OpenAI Codex CLI (GPT-5.3). Use for reviewing code changes, getting alternative perspectives on implementations, or validating approaches with another AI model.

Codex Review & Second Opinion Skill

Use OpenAI's Codex CLI to get code reviews and second opinions. This provides an independent AI perspective using GPT-5.3 or o3 models.

When to Use This Skill

Trigger when user:

• Uses /codex command explicitly
• Asks for a "code review"
• Wants a "second opinion" on code or approach
• Says "review this", "check this code", "validate this"
• Asks "what do you think about this implementation"
• Wants to "double check" code changes
• Requests "another perspective" on code

Prerequisites

Codex CLI must be installed and authenticated:

# Check installation
which codex

# Login if needed
codex login

Workflow

Step 1: Determine What to Review

ALWAYS ask the user what they want reviewed:

Use AskUserQuestion to clarify:

1. Scope: What should be reviewed?

   • Uncommitted changes (staged/unstaged)
   • Specific file(s)
   • Changes against a branch
   • A specific commit
   • General approach/implementation question

2. Model: Which model to use?

   • gpt-5.3-codex - Fast, good quality (default)

Step 2: Execute Review

IMPORTANT: Always use --full-auto to avoid confirmation prompts.

Mode 1: Review Uncommitted Changes

codex review --full-auto --uncommitted -m MODEL "CUSTOM_INSTRUCTIONS"

Mode 2: Review Against Branch

codex review --full-auto --base BRANCH -m MODEL "CUSTOM_INSTRUCTIONS"

Mode 3: Review Specific Commit

codex review --full-auto --commit SHA -m MODEL "CUSTOM_INSTRUCTIONS"

Mode 4: Second Opinion on Specific Files

codex exec --full-auto -m MODEL "Review these files for issues, improvements, and best practices: FILE1 FILE2"

Mode 5: General Second Opinion

codex exec --full-auto -m MODEL "I need a second opinion on: USER_QUESTION"

Step 3: Save Output

After review completes, save output to markdown file:

• Location: Same directory as reviewed code or current working directory
• Filename pattern: codex_review_YYYYMMDD_HHMMSS.md

Command Reference

Code Review Commands

# Review uncommitted changes (no confirmations)
codex review --full-auto --uncommitted -m gpt-5.3-codex

# Review with custom instructions
codex review --full-auto --uncommitted -m gpt-5.3-codex "Focus on security vulnerabilities and performance"

# Review against main branch
codex review --full-auto --base main -m gpt-5.3-codex

# Review specific commit
codex review --full-auto --commit abc123 -m gpt-5.3-codex

# Review with title context
codex review --full-auto --uncommitted --title "Add user authentication" -m gpt-5.3-codex

Second Opinion Commands

# Ask about specific code
codex exec --full-auto -m gpt-5.3-codex "Review this implementation approach and suggest improvements"

# Get opinion on architecture
codex exec --full-auto -m o3 "Evaluate this architecture decision: USER_CONTEXT"

# Review specific files
codex exec --full-auto -m gpt-5.3-codex -C /path/to/project "Review src/auth.py for security issues"

Output Capture

# Save last message to file (preferred method)
codex exec --full-auto -m gpt-5.3-codex -o review_output.md "Review prompt here"

# Or capture full output
codex review --full-auto --uncommitted -m gpt-5.3-codex 2>&1 | tee review.md

Usage Examples

Example 1: Review Before Commit

User: Review my changes before I commit

Claude asks: What model? (gpt-5.3-codex / o3)
User: gpt-5.3-codex

Claude executes:
codex review --full-auto --uncommitted -m gpt-5.3-codex -o codex_review_20260111_140000.md

Reports: Review saved to codex_review_20260111_140000.md

Example 2: Second Opinion on Approach

User: Get a second opinion on my authentication implementation

Claude asks: Which files? What specific concerns?
User: src/auth.py - is my JWT handling secure?

Claude executes:
codex exec --full-auto -m gpt-5.3-codex -o codex_review_auth.md "Review src/auth.py focusing on JWT security. Is the implementation secure? Any vulnerabilities?"

Example 3: Compare with Main

User: Review my feature branch against main

Claude executes:
codex review --full-auto --base main -m gpt-5.3-codex -o codex_review_feature.md "Review all changes for code quality and potential bugs"

Available Models

Model	Best For	Speed
gpt-5.3-codex	General reviews, quick feedback	Fast
o3	Deep analysis, complex reasoning	Slower

AskUserQuestion Template

When triggered, ask:

Question 1: "What would you like Codex to review?"
Options:
- Uncommitted changes (git working tree)
- Changes against a branch
- Specific file(s)
- General question/second opinion

Question 2: "Which model should I use?"
Options:
- gpt-5.3-codex (faster)
- o3 (more thorough)

Output Handling

1. Use -o filename.md to save output directly (cleaner than tee)
2. Save to: codex_review_YYYYMMDD_HHMMSS.md in current directory
3. Report file location to user after completion
4. Display key findings summary

Error Handling

Error	Solution
Not logged in	Run codex login
Not in git repo	Use -C /path or --skip-git-repo-check
Model not available	Fall back to gpt-5.3-codex
Timeout	Try with smaller scope or faster model

Tips

1. Be specific: Provide context about what to focus on
2. Review incrementally: Review smaller changes more frequently
3. Custom instructions: Add focus areas like "security", "performance", "readability"
4. Working directory: Codex needs to be run from the project directory
5. Always use --full-auto: Prevents interactive confirmation prompts

Limitations

• Requires internet connection and OpenAI API access
• Rate limits may apply
• Large codebases may need scoped reviews
• Cannot review files outside git repository by default

Non-Git Directories

When running outside a git repository, use --skip-git-repo-check:

codex exec --full-auto --skip-git-repo-check -m gpt-5.3-codex "Your prompt here"

This is useful for:

• Reviewing standalone scripts
• Getting opinions on code snippets
• General programming questions

Alternative: Codex MCP Server

For tighter integration without shell overhead, Codex can run as an MCP server. See the codex-mcp MCP server configuration for native tool integration.