debate

Route artifacts across AI models for structured P1/P2/P3 critiques.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/fancive/claude-skills --skill debate-fancive
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: debate
Source: https://github.com/fancive/claude-skills/tree/main/skills/debate
Command: npx skills add https://github.com/fancive/claude-skills --skill debate-fancive

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Orchestrates automated cross-model debates to surface issues, compare perspectives, and drive informed decisions on code, proposals, or documents.

Core Features & Use Cases

  • Multi-round critique routing and convergence between models.
  • Deterministic handling of mixed content (code + proposal) with phase-based evaluation.
  • Audit-ready session management with per-round inputs, critiques, and decisions.

Quick Start

Start a debate by typing /debate and providing an initial artifact or allowing auto-detection from the workspace.

Frequently Asked Questions about debate

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

FAQPage Schema
How do I run a cross-model debate to surface high-priority code review issues?

To run a cross-model debate, initiate the process and provide an initial artifact. The system auto-detects the artifact type and routes it through competing AI models to generate structured P1, P2, and P3 findings for your code review.

Can I use automated AI critique for mixed content containing both code and proposals?

Yes, automated AI critique supports mixed content with deterministic splitting. It separates code and proposal artifacts to evaluate them through phase-based evaluation, ensuring structured critique and policy-aligned improvements for both types.

What is multi-round critique routing and how does it help converge on document improvements?

Multi-round critique routing coordinates competing AI models to iteratively analyze documents. This process compares perspectives across rounds, enforcing a structured output rubric with a final decision to help teams converge on high-priority issues and improvements.

How do I start a multi-round critique session for a proposal artifact?

Start a critique session by invoking the debate command and providing your initial proposal artifact. The system supports auto-detection from the workspace, deterministically managing the artifact to produce audit-ready session inputs and decisions.

Does cross-model debate support audit-ready session management for code reviews?

Yes, cross-model debate enforces audit-ready session management. It tracks per-round inputs, critiques, and decisions, ensuring that code review sessions produce structured findings and policy-aligned improvements suitable for auditing.

What are the limitations of using automated AI critique for artifact evaluation?

Automated AI critique relies on deterministic splitting and phase-based evaluation for mixed content. While it enforces a structured P1/P2/P3 rubric, it requires clear artifact boundaries to effectively route and critique code, proposals, or documents.