A sophisticated multi-model debate engine that leverages collaborative roles to architect, implement, and stress-test technical solutions through adversarial reasoning.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install adversarial-engine
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install adversarial-engine using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Adversarial Engine v2.0?
The Adversarial Engine is a high-performance framework designed for Openclaw Skills that orchestrates four distinct LLM roles—Architect, Engineer, Security Officer, and Arbitrator—to reach a technical consensus. By simulating real-world debate and technical friction, it ensures that proposed solutions are not only functional but also secure and architecturally sound.
This skill bridges the gap between simple code generation and robust system design. It integrates a live Python code sandbox for execution-based validation and vector retrieval-augmented generation (RAG) to eliminate hallucinations, providing a rigorous environment for complex problem-solving.
Adversarial Engine v2.0 Use Cases
- High-concurrency system architecture design and peer review.
- Automated security vulnerability scanning and exploit simulation.
- Multi-agent code generation with real-time sandbox execution and validation.
- Technical decision-making through structured adversarial debate and arbitration.
How Adversarial Engine v2.0 Works
- Knowledge Enhancement: The engine performs vector retrieval to ground the discussion in verified domain knowledge.
- Architect Proposal: The qwen3.5-plus model generates a high-level system architecture or solution strategy.
- Engineering Implementation: The qwen3-coder-plus model implements the code and executes it within a secure Python sandbox.
- Security Audit: The kimi-k2.5 model acts as a Security Officer, attempting to find vulnerabilities or flaws in the implementation.
- Arbitration: The MiniMax-M2.5 model evaluates the debate progress and judges if the solution has reached convergence.
- Iteration or Finalization: If the solution is not optimal, the process loops back to the Architect; otherwise, results are persisted to the knowledge base.
Adversarial Engine v2.0 Setup
To integrate the Adversarial Engine into your Openclaw Skills environment, initialize the engine and configure your local or remote WebSocket server.
# Install dependencies (ensure Python 3.8+ is available)
pip install adversarial-engine
# Configure the engine in your project
python -c "from adversarial_engine import AdversarialEngine; engine = AdversarialEngine()"
Adversarial Engine v2.0 Data Schema & Taxonomy
The Adversarial Engine organizes its operations through a modular file structure and persistent database for long-term learning:
| File/Component | Purpose |
|---|---|
engine.py |
Core logic for multi-model orchestration |
code_sandbox.py |
Secure execution environment for Python validation |
vector_enhancer.py |
Manages RAG processes and vector search |
ws_server.py |
Handles real-time progress updates via WebSocket |
database.py |
Manages session persistence and knowledge solidification |
Adversarial Engine v2.0 Advanced Features
- Real-time WebSocket Streaming: Monitor every round of debate and execution logs live via a dedicated WS endpoint.
- Automated Circuit Breaking: The Arbitrator role prevents infinite loops by detecting convergence or stalemate conditions.
- Breakpoint Resumption: Ability to pause and resume complex adversarial debates without losing context.
- Sandbox Validation: Direct integration with Python execution to verify the technical truth of generated code snippets.
SKILL.md
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