How long does a typical diff review take?

MCP-based reviews are very efficient, typically completing in 5 to 30 seconds. Reviews using source context or the Bash fallback may take between 1 and 3 minutes.

Can I customize the focus of the code review?

Yes, you can prompt for specific review patterns such as security-focused audits, performance-heavy checks, or general style and consistency reviews.

Does this skill have permission to modify my files?

No. The skill is designed to run in a read-only sandbox mode, ensuring that the Codex agent only provides feedback and cannot make any direct changes to your source code.

What tools are required to use this skill?

This skill requires either the Codex MCP plugin or the Codex CLI to be installed and accessible by Claude Code to process the git diff files.

How does Codex Diff Review improve my development workflow?

It provides an automated second perspective on your git changes, helping you catch logic errors, security risks, and performance issues that might be missed during manual coding or initial AI generation.

Codex Diff Review

Name: Codex Diff Review
Author: robbyt

byrobbyt

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협업 도구

Performs automated peer reviews of git changes to identify bugs, security vulnerabilities, and performance bottlenecks before committing code.

소개

Codex Diff Review acts as an AI-powered code auditor for your development workflow, providing a critical second opinion on staged git changes. By leveraging Codex's analytical capabilities, this skill performs deep-dive inspections into code logic, security flaws (such as XSS or SQL injection), and performance regressions like N+1 queries. It integrates seamlessly with Claude Code via MCP or CLI, ensuring high code quality standards by providing immediate, non-interactive feedback while maintaining a strictly read-only environment to protect your codebase's integrity.

주요 기능

Automated AI peer review for staged git changes
31 GitHub stars
Security-specific auditing for injection and data exposure risks
Performance analysis to detect inefficient algorithms and memory leaks
Dual-mode support for both MCP tools and standard shell environments
Strictly read-only execution mode to prevent unauthorized file modifications

사용 사례

Conducting a final quality check on complex feature branches before pushing to remote
Performing targeted security audits on sensitive authentication or data-handling modules
Identifying performance regressions and inefficient logic in new code additions

소개

주요 기능

Automated AI peer review for staged git changes
31 GitHub stars
Security-specific auditing for injection and data exposure risks
Performance analysis to detect inefficient algorithms and memory leaks
Dual-mode support for both MCP tools and standard shell environments
Strictly read-only execution mode to prevent unauthorized file modifications

사용 사례

Conducting a final quality check on complex feature branches before pushing to remote
Performing targeted security audits on sensitive authentication or data-handling modules
Identifying performance regressions and inefficient logic in new code additions