debate

Stress-test prior skill outputs through multi-round adversarial agent debate.

Updated Mar 24, 2026
One-click install
npx skills add https://github.com/hanh-nd/agent-kit --skill debate-hanh-nd
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: debate
Source: https://github.com/hanh-nd/agent-kit/tree/main/plugins/agent-kit/.gemini/skills/debate
Command: npx skills add https://github.com/hanh-nd/agent-kit --skill debate-hanh-nd

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? AI-generated outputs like code reviews, plans, and briefs can contain blind spots, unsupported claims, or missed methodology steps that go unnoticed without a second pass. This Skill challenges a prior skill output by orchestrating an attacker, a defender, and a judge so you can see which conclusions actually hold up under evidence-based scrutiny. ## Core Features & Use Cases - Adversarial multi-agent debate: Spawns Gilfoyle (attacker) and Dinesh (defender) in parallel, then a neutral Judge who audits citations, classifies findings as CONFIRMED, REFUTED, PARTIAL, or CONCEDED, and issues a final verdict. - Hallucination guardrails: Enforces a strict citation boundary — debaters may only cite inline source material you provide, and the Judge flags any hallucinated citation before weighing evidence. - Convergence-controlled rounds: Runs up to 3 rounds with explicit convergence rules, then delivers a structured verdict covering confirmed gaps, defended points, unresolved disputes, and methodology gaps. - Use Case: After running a PR review skill, invoke the debate on the review report with the actual diff pasted inline to discover which findings were wrong, which were missed, and whether the reviewer followed its own checklist. ## Quick Start Debate the most recent code review output using the actual PR diff as source material and show me the final verdict.

Frequently Asked Questions about debate

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

FAQPage Schema
How do I verify an AI-generated code review for mistakes?▼

Run a structured adversarial debate on the review output. An attacker agent challenges each finding with evidence from the actual diff, a defender supports correct conclusions, and a judge rules on every point, producing a verdict of confirmed gaps and defended claims.

How do I stress-test an AI agent's output before trusting it?▼

Provide the output plus its original source material inline, then let opposing agents argue over each claim. The judge audits every citation against the source, so conclusions are ranked by evidence quality rather than rhetorical strength.

Can the debate work without access to the original source files?▼

Yes, but with reduced reliability. If the source is inaccessible, the debate proceeds on the output alone and the verdict is flagged with LOW confidence, since debaters cannot verify claims against ground truth.

How does the debate prevent agents from hallucinating citations?▼

All source material is pasted inline as a strict citation boundary, and debaters may cite nothing outside it. The judge performs a citation audit first, marking any reference not found in the source as HALLUCINATED and downgrading that finding.

How many debate rounds run before a final verdict?▼

A maximum of three rounds. The debate stops early if a round produces no new confirmed or partial findings, and the judge force-synthesizes a final verdict if the round cap is reached with ongoing disagreement.