debate

Name: debate
Availability: InStock
Author: miiraheart

Orchestrate multi-agent adversarial debates for product research with Python state management.

Updated Mar 5, 2026

Search Tags:#multi-agent system #recommendation engine #adversarial ai #llm agents #product research #ai debate

Ready to run

npx skills add https://github.com/miiraheart/claude-debate-skill --skill debate-miiraheart

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: debate
Source: https://github.com/miiraheart/claude-debate-skill/tree/main
Command: npx skills add https://github.com/miiraheart/claude-debate-skill --skill debate-miiraheart

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates in-depth product research by orchestrating a multi-agent debate system, providing structured recommendations with verified sources and eliminating the need for manual research and comparison.

Core Features & Use Cases

Multi-Agent Research: 5 specialized AI agents conduct independent research in parallel.
Adversarial Debate: Agents engage in structured debate rounds with elimination, converging on the best product.
Structured Recommendations: Produces a final report with comparison tables, buy links, and verified sources.
Use Case: Get the best recommendation for "best ergonomic keyboard under $200 for programming" by having AI agents research, debate, and synthesize findings.

Quick Start

Use the debate skill to find the best noise-cancelling headphones under $350.

Frequently Asked Questions about debate

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

FAQPage Schema

How does AI adversarial debate improve product research?▼

AI adversarial debate improves product research by deploying five specialized LLM agents to independently research, argue, and eliminate weaker product options, synthesizing a final verified recommendation. This structured approach reduces manual comparison bias.

How do I get structured recommendations for a specific product query?▼

To get structured recommendations, input a product query like "best noise-cancelling headphones under $350" and the system generates a report with comparison tables, buy links, and verified sources. Five agents research in parallel, debate, and synthesize the final output.

Can I use multi-agent systems for complex product comparisons?▼

Yes, you can use this multi-agent system for complex product comparisons. Five independent AI agents execute parallel research, engage in structured debate rounds with elimination, and converge on the best product using state management and vote tallying.

What is the best way to automate in-depth product research using LLM agents?▼

The best way to automate in-depth product research is orchestrating LLM agents to conduct parallel research and structured debates. This system eliminates manual comparison by using convergence detection and vote tallying to synthesize final recommendations.

Does the multi-agent recommendation engine require external web search tools?▼

Yes, the multi-agent recommendation engine requires Claude Code tools for web search and data fetching. Agents utilize these tools during independent research and debate rounds to gather verified data before synthesizing final product recommendations.

What format do the final product recommendations follow after the AI debate?▼

The final product recommendations follow a structured report format containing comparison tables, buy links, and verified sources. This output is generated after the multi-agent debate system completes convergence detection and final synthesis.