cross-validation

Orchestrate adversarial plan reviews between Codex and Claude AI models.

Updated Aug 23, 2026

Search Tags:#validation #codex #claude #adversarial #ai review #plan critique

Ready to run

npx skills add https://github.com/itismyfield/rcc-pcd-bootstrap --skill cross-validation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: cross-validation
Source: https://github.com/itismyfield/rcc-pcd-bootstrap/tree/main/skills/cross-validation
Command: npx skills add https://github.com/itismyfield/rcc-pcd-bootstrap --skill cross-validation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill automates the rigorous cross-validation of AI-generated plans by pitting two powerful models (Codex and Claude) against each other in an adversarial review process, ensuring robust and well-vetted strategies.

Core Features & Use Cases

Adversarial Review: Uses one AI model as a critic and another as a defender to identify flaws in a plan.
Iterative Refinement: Supports up to 3 rounds of critique and defense to thoroughly vet a plan.
Structured Reporting: Generates a comprehensive Markdown report detailing accepted changes, rejected criticisms, and unresolved issues.
Use Case: You have Claude generate a complex system architecture plan. Use this Skill to have Codex critically review it, identify potential flaws, and then have Claude defend its plan, leading to a much more robust final design.

Quick Start

Cross-validate the plan in '~/my-plan.md' with Codex as the origin model.

Frequently Asked Questions about cross-validation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I cross-validate an AI-generated plan using adversarial review?▼

To cross-validate an AI-generated plan using adversarial review, orchestrate a critique and defense process between Codex and Claude models. This iterative cycle identifies flaws and generates a structured synthesis report detailing accepted changes, rejected criticisms, and unresolved issues.

What problem does adversarial AI review solve for system architecture planning?▼

Adversarial AI review solves the problem of unvetted strategies in system architecture planning by pitting two models against each other. One model acts as a critic to identify potential flaws while the other defends its plan, ensuring a much more robust final design.

Do I need specific CLIs installed to run the cross-validation process?▼

Yes, you need both the codex and claude CLIs installed and configured on your system to execute the cross-validation process. These command-line interfaces facilitate the iterative critique and defense rounds between the two AI models.

How many rounds of critique and defense does the plan validation support?▼

The plan validation supports up to 3 rounds of iterative critique and defense. This allows the models to thoroughly vet the plan, refine arguments, and resolve deep architectural issues before generating the final synthesis report.

What format does the final plan validation report use?▼

The final plan validation report uses a comprehensive Markdown format. This structured report details accepted changes, rejected criticisms, and unresolved issues, providing a clear synthesis of the adversarial review process.

Can I use Claude to defend a plan generated by Codex?▼

Yes, you can use Claude to defend a plan generated by Codex. The adversarial review process designates one AI model as the critic and the other as the defender, allowing you to cross-validate plans regardless of which model originally generated it.