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red-team红队

Agent Skill

red-team 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。

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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:red-team(红队)
来源仓库:https://github.com/retrodigio/red-team
安装命令:
openclaw skills install red-team
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 red-team

简介

用于查找、检索和筛选相关信息,适合根据关键词快速定位结果。

  • 可对决策、想法和策略进行对抗性压力测试。red-team 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 协调多个具有冲突世界观的 AI Agent 辩论。
  • 通过 clawhub 安装,建议确认权限范围和维护状态。
  • 需注意是否会触发联网、命令执行或文件读写操作。

SKILL.md

name
red-team
description
>

Red Team — Adversarial Debate Engine

Stress-test any decision by having AI agents with conflicting worldviews debate it.

Prerequisites

One of these coding agent CLIs (uses your existing subscription — no API key needed):

  • Claude Code (default): claudenpm i -g @anthropic-ai/claude-code
  • Codex: codexnpm i -g @openai/codex
  • Gemini: gemininpm i -g @google/gemini-cli

No Python dependencies beyond the standard library.

Quick Start

# Basic 3-persona debate (uses Max subscription via claude CLI)
python3 ~/.openclaw/skills/red-team/scripts/red-team.py \
  --question "Should we do X?" \
  --personas "bull,bear,operator"

# Full debate with context and output file
python3 ~/.openclaw/skills/red-team/scripts/red-team.py \
  -q "Should we invest $50k in this deal?" \
  -p "bull,bear,cash-flow,local-realist" \
  -r 3 \
  -c /path/to/deal-data.md \
  -o /tmp/red-team-result.md

# Use a different model
python3 ~/.openclaw/skills/red-team/scripts/red-team.py \
  -q "Should we launch this product?" \
  -p "bull,customer,operator" \
  -m opus

# List all available personas
python3 ~/.openclaw/skills/red-team/scripts/red-team.py --list-personas

How to Use (as OpenClaw Agent)

When the user asks you to "red team" something, "stress test" an idea, play "devil's advocate", or asks "what could go wrong":

  1. Identify the question/decision from the user's message
  2. Choose appropriate personas (default: bull,bear,operator — adjust based on domain)
  3. Run the script and save output
  4. Summarize the key findings to the user, share the full report if requested

Persona selection guide:

  • Investment/financial decisions → bull, bear, cash-flow, economist
  • Product/startup ideas → bull, customer, operator, technologist
  • Legal/compliance questions → regulator, bear, operator
  • Strategy/direction → contrarian, economist, historian, bull
  • General "should we do X?" → bull, bear, operator (good default)

Available Personas

KeyNameWorldview
bullThe BullOptimistic, opportunity-focused
bearThe BearRisk-averse, capital preservation
contrarianThe ContrarianOppositional, consensus-challenging
operatorThe OperatorExecution-focused pragmatist
economistThe EconomistMacro trends, opportunity cost
local-realistThe Local RealistGround truth, local specifics
cash-flowThe Cash Flow AnalystIncome, carrying costs, IRR
regulatorThe RegulatorCompliance, legal risk
technologistThe TechnologistAutomation, scalability
customerThe CustomerEnd-user demand, willingness to pay
ethicistThe EthicistMoral implications, stakeholder impact
historianThe HistorianHistorical patterns, precedent

Custom Personas

Create a JSON file:

{
  "my-persona": {
    "name": "The Skeptic",
    "description": "Questions everything, trusts nothing",
    "system": "You are The Skeptic — you question every assumption..."
  }
}

Use with --custom-personas /path/to/file.json. Custom personas merge with built-ins.

CLI Options

FlagDefaultDescription
--question, -qrequiredThe question to debate
--personas, -pbull,bear,operatorComma-separated persona keys
--rounds, -r2Number of critique rounds
--output, -ostdoutOutput file path
--context-file, -cnoneAdditional context file
--custom-personasnoneCustom personas JSON
--model, -msonnetModel alias (sonnet, opus, haiku, gpt-4o, etc.)
--backend, -bclaudeCLI backend: claude, codex, or gemini
--list-personasList personas and exit

Output Structure

The output is a markdown document with:

  1. Initial Proposals — Each agent's independent take
  2. Critique Rounds — Agents critique each other
  3. Refinement — Agents update positions based on critiques
  4. Conviction Scores — Each agent scores all positions (0-100)
  5. Synthesis & Decision Brief — Neutral agent produces:

- Executive summary - Consensus points - Key disagreements - Risk matrix - Conviction score summary - Synthesized recommendation - Next steps

When to Use

Good for: Important decisions, investment analysis, product strategy, "go/no-go" calls, pre-mortems, challenging groupthink

Not for: Simple factual questions, time-sensitive emergencies, decisions already made, emotional/personal choices

Integration Tips

  • Save output to memory files for future reference
  • Create BEADS tasks from the "Next Steps" section
  • Feed context files from Obsidian or project docs
  • Re-run with different personas for different perspectives
  • Use --rounds 1 for quick takes, --rounds 3 for deep analysis

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

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按下载量换算3,556

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安装前确认

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