red-team-tribunal

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# Red Team Tribunal: Adversarial Verification

## Overview

The Red Team Tribunal uses Opus 4.6 Agent Teams to create an adversarial review loop that prevents "confident mistakes." Three specialized sub-agents work in parallel to find issues from different perspectives.

## The Tribunal Structure

### 🤔 The Skeptic (Security/Logic)
- **Role**: Security auditor and logic validator
- **Goal**: Find at least one valid issue (must find something)
- **Focus**: Security flaws, logic errors, edge cases, race conditions
- **Confidence Target**: >80%

### 👤 The User Proxy (UX/Edge Cases)
- **Role**: End-user simulator
- **Goal**: Break the feature from a user's perspective
- **Focus**: Usability, invalid inputs, confusing flows, accessibility
- **Tools**: Browser automation, form fuzzing

### ⚡ The Optimizer (Performance)
- **Role**: Performance engineer
- **Goal**: Identify efficiency bottlenecks
- **Focus**: Algorithmic complexity, memory usage, database queries, caching
- **Metrics**: O(n) complexity, response times, resource usage

## When to Use

Activate the Tribunal for:
- Critical code changes (auth, payments, security)
- Before merging pull requests
- When adding new features
- Security-sensitive implementations
- Performance-critical code
- Code that affects multiple users

## Usage

### Trigger Tribunal Review
```bash
# Review a file
python3 /a0/usr/plugins/red-team-tribunal/red-team-tribunal.py --target <file>

# Review a PR
python3 /a0/usr/plugins/red-team-tribunal/red-team-tribunal.py --pr <number>

# Review a commit
python3 /a0/usr/plugins/red-team-tribunal/red-team-tribunal.py --diff <hash>
```

### Understanding Verdicts

**CONSENSUS OPTIONS:**

1. **APPROVED** (All agents pass)
   - Code meets all quality standards
   - Ready to merge

2. **CONDITIONAL** (Concerns raised)
   - Minor issues found
   - Address concerns before merge
   - Can proceed with fixes

3. **REJECTED** (Critical issues)
   - Security vulnerabilities or major flaws
   - Must fix before reconsideration
   - Returns detailed recommendations

## Review Process

### Step 1: Agent Assembly
Three agents spawn in parallel:
```python
agents = ["skeptic", "user_proxy", "optimizer"]
tasks = [spawn_agent(agent, target) for agent in agents]
results = await asyncio.gather(*tasks)
```

### Step 2: Individual Analysis
Each agent analyzes from their specialty:
- **Skeptic**: Scans for vulnerabilities, logic gaps
- **User Proxy**: Attempts to break UX, finds edge cases
- **Optimizer**: Reviews complexity, resource usage

### Step 3: Consensus Building
Agents debate and produce unified verdict:
- Unanimous approval required for pass
- Any rejection blocks merge
- Concerns must be addressed

### Step 4: Report Generation
JSON output includes:
```json
{
  "consensus": "APPROVED|CONDITIONAL|REJECTED",
  "verdicts": [
    {"agent": "skeptic", "verdict": "pass", "confidence": 0.85},
    {"agent": "user_proxy", "verdict": "pass", "confidence": 0.90},
    {"agent": "optimizer", "verdict": "concerns", "confidence": 0.75}
  ],
  "recommendations": [
    "Add input validation",
    "Optimize database query",
    "Add caching layer"
  ]
}
```

## Sample Output

```
🏛️  RED TEAM TRIBUNAL
Target: src/auth/login.ts

📋 AGENT VERDICTS:
  🤔 Skeptic:     ⚠️  CONCERNS (85%)
  👤 User Proxy:  ✅ PASS (90%)
  ⚡ Optimizer:   ⚠️  CONCERNS (75%)

From aegntic/cldcde, MIT.

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