An AI-powered risk mitigation assistant that models business transactions, maps data and fund flows, and identifies security vulnerabilities.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install anti-cheating-assistant
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 anti-cheating-assistant 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 Anti-Cheating and Business Risk Control Assistant?
The Anti-Cheating and Business Risk Control Assistant is an advanced AI agent designed to safeguard business logic, transactions, and user workflows from fraudulent activities, hijacking, and security exploits. Integrated seamlessly as one of the specialized Openclaw Skills, this agent systemizes risk assessment by analyzing the interactions between users, platforms, and third-party carriers.
By leveraging structured modeling methodologies, the assistant allows organizations to visualize operational vulnerabilities before they occur. It guides developers and business analysts through a rigorous, step-by-step security audit, translating ambiguous business patterns into actionable, threat-resistant architectures. It serves as a vital tool for fraud prevention, financial risk assessment, and platform integrity.
Anti-Cheating and Business Risk Control Assistant Use Cases
- Evaluating a new e-commerce or fintech business model for potential payment fraud or cash-out exploits.
- Auditing dispatch and delivery platforms (like ride-hailing or on-demand delivery) to prevent collusion between merchants and drivers.
- Modeling information, service, and capital flows to identify data hijacking, forgery, or direct attack vectors.
- Generating standard Mermaid diagrams and transaction tables to clearly communicate security vulnerabilities to stakeholders.
- Accessing dedicated RAG-based risk mitigation databases to retrieve industry-standard defense mechanisms.
How Anti-Cheating and Business Risk Control Assistant Works
- Business Clarification: The agent conducts interactive questioning (maximum of 3 targeted questions, one per turn) to reconstruct complete transaction scenarios involving roles, actions, and carriers.
- Three-Flow Diagram Modeling: Once the workflow is defined, the agent uses the Three-Flow Graph Analysis method to model flow directions (Information, Service, and Fund/Capital flows) and renders them via Mermaid charts and tabular mappings.
- Risk Analysis & Vulnerability Identification: The modeled flows are cross-referenced with a comprehensive risk reference table to expose security loopholes such as spoofing, hijacking, or transaction manipulation.
- Problem Specification: The user is prompted to highlight and prioritize the most critical threats identified during the analysis phase.
- RAG-Driven Mitigation Solutions: The agent invokes an MCP RAG interface using dedicated APIs to recommend targeted, production-ready anti-cheating measures and defensive patches.
- Expert Consult Escalation: For highly customized or deeply complex attack scenarios, the agent smoothly redirects users to official consultants for manual, high-level support.
Anti-Cheating and Business Risk Control Assistant Setup
To deploy this skill within your AI agent workspace, ensure you have the required Python environments and credentials configured.
Prerequisites
Install the necessary dependencies for graph modeling:
pip install requests mcp mermaid-cli
Configuration
- MCP API Configuration: This skill utilizes a Model Context Protocol (MCP) RAG server for retrieving risk solutions. You must configure your environment with the proper API Key.
Create a
.envfile or export the variable:
export RISK_SOLUTION_API_KEY=\"your_api_key_here\"
- Include Scripts: Ensure the helper script
scripts/three_flow_graph_analysis.pyis present in your workspace path to handle algorithmic flow-graph Generation. - Register the Skill: Add the skill definition to your agent platform configured with other Openclaw Skills to enable automatic classification of transaction risk profiles.
Anti-Cheating and Business Risk Control Assistant Data Schema & Taxonomy
The assistant organizes and outputs information according to a rigorous data schema. This structure ensures compatibility with visualization tools and automated compliance pipelines.
Structural Templates
| Placeholder Name | Content Description | Source Engine |
|---|---|---|
{业务描述} |
Natural language business workflow summary | Skill 1 (Clarification Output) |
{三流图内容} |
Mermaid-compatible Three-Flow visual chart URL | Skill 2 (Flow Graph Generator) |
{三流表内容} |
Markdown state table classifying Info, Service, and Fund flows | Skill 2 (Flow Table Output) |
{风险列表} |
Identified vulnerabilities, hijacking forms, and exploits | Skill 3 (Risk Analysis Engine) |
{具体防范措施} |
Step-by-step mitigation procedures retrieved via RAG | Skill 5 (RAG MCP API) |
Flow Table Format
The generated {三流表内容} organizes steps into a structured Markdown matrix:
- Columns: Step No, Carrier (Information Flow, Service Flow, Capital Flow), Roles involved (e.g., Merchant, Platform, Driver), and control flags (
Cfor Controllable,Vfor Visible).
Anti-Cheating and Business Risk Control Assistant Advanced Features
- Three-Flow Graph Modeling (三流图): Automates the visualization of transaction flows into structured Information, Service, and Capital flows.
- Stateful Skill Transitions: Implements a strict priority state machine, routing dynamically from business capture to modeling, threat assessment, and solution lookup.
- Automated RAG-MCP Client: Seamlessly connects to external knowledge bases via a Model Context Protocol (MCP) interface to retrieve vetted cybersecurity patterns.
- Mermaid Live Integration: Generates functional visualization URLs on the fly to help teams inspect platform assets in real-time.
- Context Preservation Safeguards: Strictly operates within user-provided transaction schemas without fabricating out-of-context data, preventing hallucination during threat mapping.
SKILL.md
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