How does this skill handle file and line number citations?

The skill enforces strict formatting requirements, compelling both agents to provide specific file paths and line numbers (e.g., file.ts:42-58) for every finding to ensure all results are manually verifiable.

What is the consult-codex skill?

It is a specialized Claude Code skill that triggers parallel analysis from both OpenAI Codex GPT-5.2 and Claude's code-searcher to provide a comprehensive dual-perspective code review.

Why should I use two different AI models for code analysis?

Using multiple models helps catch subtle bugs one model might miss, provides diverse perspectives on system architecture, and increases the reliability of findings through cross-model verification.

What happens if one of the AI agents fails to respond?

The skill includes built-in error handling. If one agent fails or times out, it will still present the successful agent's findings while noting the specific failure in the comparison analysis section.

Dual-AI Code Consultant

Name: Dual-AI Code Consultant
Author: centminmod

bycentminmod

•

1,727

Developer Tools

Orchestrates parallel code analysis between OpenAI Codex and Claude's code-searcher to provide comprehensive, multi-perspective technical reviews.

About

This skill enhances Claude's analytical capabilities by simultaneously consulting OpenAI's Codex GPT-5.2 and Claude’s internal code-searcher subagent. It wraps user queries in structured analysis guidelines that demand high-precision evidence, such as specific file paths and line-number citations. By executing these tools in parallel and synthesizing the results into a side-by-side comparison, it allows developers to verify findings across different AI architectures, making it indispensable for complex debugging, deep architectural reviews, and navigating large-scale codebases.

Key Features

Synthesis of corroborated insights with confidence level assessments
Parallel execution of OpenAI Codex and Claude code-searcher for faster results
1,727 GitHub stars
Strict enforcement of file:line number citations for verifiable evidence
Cross-platform support for macOS and Linux CLI environments
Automated side-by-side comparison table highlighting unique findings

Use Cases

Locating and validating specific implementations across large repositories
Performing thorough code reviews and architectural impact assessments
Debugging complex logic issues that require diverse technical perspectives

About

Key Features

Synthesis of corroborated insights with confidence level assessments
Parallel execution of OpenAI Codex and Claude code-searcher for faster results
1,727 GitHub stars
Strict enforcement of file:line number citations for verifiable evidence
Cross-platform support for macOS and Linux CLI environments
Automated side-by-side comparison table highlighting unique findings

Use Cases

Locating and validating specific implementations across large repositories
Performing thorough code reviews and architectural impact assessments
Debugging complex logic issues that require diverse technical perspectives