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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":

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

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:

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