SkillHub

red-team

v1.0.0

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...

Sourced from ClawHub, Authored by retrodigio

Installation

Please help me install the skill `red-team` from SkillHub official store. npx skills add retrodigio/red-team

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

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