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agent-debateAgent 辩论

Agent Skill

agent-debate 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

11,268

周安装

484

GitHub Stars

1

下载量

3,949
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:agent-debate(Agent 辩论)
来源仓库:https://github.com/0xrichyrich/agent-debate
安装命令:
openclaw skills install agent-debate
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install agent-debate

简介

协调多个代理独立争论相互竞争的方法,然后综合他们的立场,以确定复杂决策的最佳解决方案。

SKILL.md

Agent Debate Skill

Spawn multiple sub-agents to debate approaches and converge on the best solution.

Overview

Uses parallel sub-agents with file-based coordination to simulate adversarial debate. Each agent investigates independently, writes findings, then a synthesis agent reviews all positions and picks the winner.

Pattern: Competing Hypotheses

Best for: architecture decisions, debugging, strategy, trade analysis

How It Works

  1. Lead defines the question and spawns 2-4 debate agents
  2. Each agent writes their position to plans/debate-{id}/agent-{n}.md
  3. A synthesis agent reads all positions and produces a verdict
  4. Lead reviews and acts on the verdict

File Structure

plans/debate-{topic}/
├── question.md          # The question being debated
├── agent-1.md           # Agent 1's position
├── agent-2.md           # Agent 2's position
├── agent-3.md           # Agent 3's position (optional)
├── rebuttal-1.md        # Agent 1's rebuttal (round 2)
├── rebuttal-2.md        # Agent 2's rebuttal (round 2)
├── synthesis.md         # Final synthesis and verdict
└── decision.md          # Lead's final decision

Usage

Single Round (Fast)

3 agents, one round, synthesis. ~5 minutes.

1. Write question to plans/debate-{topic}/question.md
2. Spawn 3 agents simultaneously:
   - Agent A: "Argue FOR approach X. Read plans/debate-{topic}/question.md. Write your position with evidence to plans/debate-{topic}/agent-1.md"
   - Agent B: "Argue FOR approach Y. Read plans/debate-{topic}/question.md. Write your position with evidence to plans/debate-{topic}/agent-2.md"
   - Agent C: "Argue FOR approach Z. Read plans/debate-{topic}/question.md. Write your position with evidence to plans/debate-{topic}/agent-3.md"
3. Wait for all to complete
4. Spawn synthesis agent:
   "Read all positions in plans/debate-{topic}/. Score each on: feasibility (1-10), risk (1-10), speed (1-10), quality (1-10). Write verdict to plans/debate-{topic}/synthesis.md"

Two Round (Thorough)

3 agents, position + rebuttal, synthesis. ~10 minutes.

Round 1: Same as single round (positions)
Round 2: Each agent reads others' positions and writes rebuttals
  - "Read all agent-*.md files. Write a rebuttal challenging the other positions. Save to rebuttal-{n}.md"
Round 3: Synthesis reads everything and decides

Red Team (Adversarial)

1 builder + 1 attacker. Best for security/robustness.

1. Builder: "Design/implement X. Write to plans/debate-{topic}/proposal.md"
2. Attacker: "Read proposal.md. Find every flaw, vulnerability, and edge case. Write to plans/debate-{topic}/attack.md"
3. Builder: "Read attack.md. Address each issue. Write to plans/debate-{topic}/defense.md"
4. Synthesis: "Score the final defense. Is it production-ready?"

Model Assignment

  • Debate agents: Opus 4.6 (needs deep reasoning)
  • Synthesis agent: Opus 4.6 (needs to weigh nuanced arguments)
  • Simple positions: Sonnet 4.5 (if cost matters and topic is straightforward)

When To Use

✅ Architecture decisions with multiple valid approaches ✅ Debugging with unclear root cause ✅ Trading strategy evaluation ✅ Security review (red team pattern) ✅ Hackathon approach selection

❌ Simple implementation tasks ❌ Tasks with one obvious answer ❌ Sequential work with dependencies

Example Prompts

Architecture Debate

Question: "Should Nudge use Turso (SQLite) or Supabase (Postgres) for production?"
Agent 1: Argue for Turso — edge computing, simplicity, cost
Agent 2: Argue for Supabase — ecosystem, realtime, auth
Agent 3: Devil's advocate — what about a hybrid approach?

Trading Strategy

Question: "Is ETH undervalued at current levels given macro conditions?"
Agent 1: Bull case — on-chain metrics, upcoming catalysts
Agent 2: Bear case — macro headwinds, technical levels
Agent 3: Neutral — range-bound thesis with key levels to watch

Debug Investigation

Question: "App crashes on iOS 17 but not 18. What's the root cause?"
Agent 1: Investigate API deprecation changes
Agent 2: Investigate SwiftUI rendering pipeline differences
Agent 3: Investigate memory management changes

Integration with Swarm

The debate pattern works across the swarm:

  • Sprint can debate hackathon approaches
  • Quant can run bull/bear/neutral analysis
  • Architect can evaluate design patterns
  • Any agent can spawn a debate when facing a non-obvious decision

Future: Native Agent Teams

When OpenClaw supports Claude Code's agent teams natively:

  • Agents will message each other directly (no file coordination)
  • Shared task list replaces file-based progress tracking
  • Lead can delegate without implementing
  • This skill becomes a lightweight wrapper around native teams

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

96.62%
按下载量换算3,816

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

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权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install agent-debate 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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