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autoconference:debate

Run adversarial two-researcher debates with judge-scored rounds and structured reports.

5|Updated Mar 18, 2026
Search Tags:#multi-agent#debate#adversarial reasoning#pro-con#argument evaluation#judge scoring#literature thinking
One-click install
npx skills add https://github.com/wjgoarxiv/autoconference-skill --skill autoconference-debate
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: autoconference:debate
Source: https://github.com/wjgoarxiv/autoconference-skill/tree/main/skills/debate
Command: npx skills add https://github.com/wjgoarxiv/autoconference-skill --skill autoconference-debate

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps you evaluate competing viewpoints by running a structured pro/con debate with an impartial judge, turning disagreement into scored, actionable critique.

Core Features & Use Cases

  • Adversarial 2-researcher debate: Runs two independent agents arguing opposite positions with strict role separation.
  • Round-based challenge + rebuttal: Forces precise attacks on the opponent’s weakest claim each round, then requires targeted rebuttals.
  • Judge-scored outcomes: Produces per-round scores, winner/tie, convergence detection, and a final verdict with unresolved disputes and follow-up recommendations.
  • Use Case: Decide which policy, strategy, or technical claim is more convincing by having one side argue for the proposition and the other side argue against it under adjustable evidence strictness.

Quick Start

Tell the AI: "Run an autoconference:debate for the question 'X' with 3 rounds and strict judging, and save the report as debate-report.md."

Frequently Asked Questions about autoconference:debate

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

FAQPage Schema
How do I run an adversarial debate to evaluate a decision from opposing perspectives?▼

An adversarial debate evaluates decisions by running two independent agents arguing opposite positions through challenge-rebuttal rounds. An impartial judge scores each round, detects convergence, and produces a final verdict with unresolved disputes and follow-up recommendations.

Can I use multi-agent argument evaluation for strategic and philosophical questions?▼

Multi-agent argument evaluation applies to philosophical, strategic, or empirical questions. It requires interactive setup for topic, position assignment, round count, judge strictness, and output path to generate a full transcript and structured report.

What is round-based pro-con argumentation with judge scoring?▼

Round-based pro-con argumentation forces precise attacks on an opponent’s weakest claim each round, requiring targeted rebuttals. An adjustable strictness judge scores the rounds, declaring a winner or tie based on argument quality.

How do I set up a two-researcher debate and save the transcript?▼

Setting up a two-researcher debate involves defining a topic, assigning positions, setting round counts, and choosing judge strictness. The system generates a full transcript and saves a structured report at debate-report.md.

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