What is the primary benefit of the Codex-Claude Engineering Loop?

It provides a 'checks and balances' system where Claude handles implementation while Codex provides an objective second opinion, significantly reducing architectural errors and security vulnerabilities.

How does the skill handle implementation errors?

The workflow stops execution upon a non-zero exit code, summarizes the error for the user, and requires explicit confirmation before attempting architectural changes or multi-file modifications.

Do I need a Git repository to use this skill?

Yes, Codex requires a Git repository for history tracking. The skill includes a pre-flight check to initialize Git or bypass the requirement using the --skip-git-repo-check flag.

Where are the review results stored?

All outputs, including the implementation plan, validation results, and code reviews, are saved in a local .codex-loop/ directory for easy reference and traceability.

Can I customize the intensity of the code review?

Yes, during the pre-flight phase, you can choose between different models and reasoning effort levels ranging from low to extra-high to suit the criticality of your task.

Codex-Claude Engineering Loop

Name: Codex-Claude Engineering Loop
Author: creator-hian

bycreator-hian

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Security & Testing

Orchestrates a multi-stage dual-AI workflow to plan, implement, and validate complex features with high-confidence code reviews.

About

This skill establishes a rigorous engineering loop that leverages Claude Code for architectural planning and implementation while utilizing Codex for objective validation and quality assurance. By separating the roles of creator and critic between two distinct AI systems, it minimizes logic errors in complex feature development, enhances security through multi-stage reviews, and maintains a detailed audit trail within a structured directory. It is specifically designed for high-stakes refactoring or implementing mission-critical features where security, performance, and architectural integrity are paramount.

Key Features

Categorized Post-Implementation Review: Triggers deep-dive code reviews that classify issues as Critical, Major, Minor, or Info.
Configurable Reasoning Effort: Allows users to toggle between different GPT-5 models and reasoning levels based on task complexity.
Structured Context Tracking: Automatically maintains a .codex-loop directory with plans, validation results, and iteration history.
2 GitHub stars
Dual-AI Orchestration: Assigns Claude as the architect and Codex as the validator for a checks-and-balances workflow.
Automated Plan Validation: Uses Codex to identify logic flaws, edge cases, and architectural risks before any code is written.

Use Cases

Large-scale codebase refactoring where maintaining system stability and performance is critical.
Developing complex features that require rigorous logic validation and high-security standards.
Implementing security-sensitive modules that benefit from an independent, secondary AI audit.

About

Key Features

Categorized Post-Implementation Review: Triggers deep-dive code reviews that classify issues as Critical, Major, Minor, or Info.
Configurable Reasoning Effort: Allows users to toggle between different GPT-5 models and reasoning levels based on task complexity.
Structured Context Tracking: Automatically maintains a .codex-loop directory with plans, validation results, and iteration history.
2 GitHub stars
Dual-AI Orchestration: Assigns Claude as the architect and Codex as the validator for a checks-and-balances workflow.
Automated Plan Validation: Uses Codex to identify logic flaws, edge cases, and architectural risks before any code is written.

Use Cases

Large-scale codebase refactoring where maintaining system stability and performance is critical.
Developing complex features that require rigorous logic validation and high-security standards.
Implementing security-sensitive modules that benefit from an independent, secondary AI audit.