Red team tribunal

Skill aegntic/cldcde/.claude/skills/red-team-tribunal

Multi-agent adversarial verification system. Use when you need thorough code review, security auditing, or quality validation. Spawns three specialized agents (Skeptic, User Proxy, Optimizer) that must reach consensus before code can be approved.From its SKILL.md

Install

npx -y skills add aegntic/cldcde --skill red-team-tribunal

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

11 stars11 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

4.8 KB, as published. Nobody here has run it

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

# 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:

APPROVED (All agents pass)
- Code meets all quality standards
- Ready to merge
CONDITIONAL (Concerns raised)
- Minor issues found
- Address concerns before merge
- Can proceed with fixes
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:

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:

{
  "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%)

📊 CONSENSUS: CONDITIONAL - Address Concerns

💡 RECOMMENDATIONS:
  1. Add null check at line 45
  2. Implement memoization for expensive calc
  3. Add rate limiting to prevent brute force

CI/CD Integration

Add to GitHub Actions:

- name: Red Team Tribunal Review
  run: |
    python3 red-team-tribunal.py --pr ${{ github.event.pull_request.number }}

Success Metrics

Detection Rate: % of real issues found
False Positive Rate: % of invalid concerns
Time to Review: Average review duration
Consensus Time: Time to reach agreement

Troubleshooting

Agents Not Spawning

Check Agent Teams feature is enabled:

export CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1

Timeout Issues

Increase timeout for complex reviews:

subprocess.run(..., timeout=120)  # 2 minutes

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