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interpeer

Orchestrate multi-AI peer reviews of code with Claude, Codex, and Oracle models.

3|1|Updated Feb 15, 2026
Search Tags:#quality assurance#code review#development workflow#ai agents#multi-model#peer review
One-click install
npx skills add https://github.com/mistakeknot/Demarch --skill interpeer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: interpeer
Source: https://github.com/mistakeknot/Demarch/tree/main/.gemini/generated-skills/interpeer
Command: npx skills add https://github.com/mistakeknot/Demarch --skill interpeer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the code review process by leveraging multiple AI models to provide comprehensive and diverse feedback, ensuring higher code quality and faster iteration cycles.

Core Features & Use Cases

  • Multi-AI Review: Facilitates peer review across different AI models (e.g., Claude, Codex, Oracle) for varied perspectives.
  • Disagreement Extraction: Identifies and highlights points of contention or differing opinions among AI reviewers.
  • Use Case: When developing a new feature, use this Skill to have Claude and Codex review the code independently, then have an Oracle model provide a third opinion, and finally, extract any key disagreements for human review.

Quick Start

Use the interpeer skill to conduct a cross-AI review of the latest code changes.

Frequently Asked Questions about interpeer

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

FAQPage Schema
How do I run a multi-model AI peer review for my code changes?▼

Multi-model AI peer review is orchestrated by routing code changes to Claude, Codex, and Oracle models independently, aggregating their critiques into a comprehensive report highlighting points of disagreement.

What is cross-AI code review and how does it work?▼

Cross-AI code review leverages multiple AI models to independently analyze code quality and development philosophy, extracting differing opinions to provide diverse, multi-faceted feedback for human review.

Can I use multiple AI agents to identify disagreements in code critiques?▼

Yes, multi-AI agents can identify disagreements by independently reviewing the same code changes and having the orchestrator extract and highlight points of contention among the reviewers.

Does multi-AI peer review support iterative development workflows?▼

Multi-AI peer review supports iterative development by providing comprehensive, multi-faceted code critiques that streamline quality assurance and accelerate iteration cycles.

What is the best way to get diverse perspectives on code quality from AI?▼

The best way to get diverse AI perspectives is to facilitate peer review across different models like Claude and Codex, using a third model to provide a tie-breaking third opinion.

Why should I use multiple AI models for quality assurance instead of one?▼

Using multiple AI models for quality assurance ensures higher code quality by leveraging diverse feedback streams, highlighting disagreements that a single model might overlook.

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