Metadata

name: red-team
description: Adversarial multi-agent debate engine for stress-testing decisions, ideas, and strategies. Orchestrates multiple AI agents with conflicting worldviews (bull, bear, operator, contrarian, etc.) to debate a question through structured rounds, then synthesizes results into a decision brief. Use for: red team analysis, adversarial debate, stress testing ideas, devil's advocate, "what could go wrong" analysis, decision validation, pre-mortem exercises.

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): claude — npm i -g @anthropic-ai/claude-code
Codex: codex — npm i -g @openai/codex
Gemini: gemini — npm 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":

Identify the question/decision from the user's message
Choose appropriate personas (default: bull,bear,operator — adjust based on domain)
Run the script and save output
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

Key	Name	Worldview
bull	The Bull	Optimistic, opportunity-focused
bear	The Bear	Risk-averse, capital preservation
contrarian	The Contrarian	Oppositional, consensus-challenging
operator	The Operator	Execution-focused pragmatist
economist	The Economist	Macro trends, opportunity cost
local-realist	The Local Realist	Ground truth, local specifics
cash-flow	The Cash Flow Analyst	Income, carrying costs, IRR
regulator	The Regulator	Compliance, legal risk
technologist	The Technologist	Automation, scalability
customer	The Customer	End-user demand, willingness to pay
ethicist	The Ethicist	Moral implications, stakeholder impact
historian	The Historian	Historical 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

Flag	Default	Description
`--question`, `-q`	required	The question to debate
`--personas`, `-p`	bull,bear,operator	Comma-separated persona keys
`--rounds`, `-r`	2	Number of critique rounds
`--output`, `-o`	stdout	Output file path
`--context-file`, `-c`	none	Additional context file
`--custom-personas`	none	Custom personas JSON
`--model`, `-m`	sonnet	Model alias (sonnet, opus, haiku, gpt-4o, etc.)
`--backend`, `-b`	claude	CLI backend: claude, codex, or gemini
`--list-personas`	—	List personas and exit

Output Structure

The output is a markdown document with:

Initial Proposals — Each agent's independent take
Critique Rounds — Agents critique each other
Refinement — Agents update positions based on critiques
Conviction Scores — Each agent scores all positions (0-100)
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

Red Team

安装技能