ai-workflow-red-team-lite

What This Skill Does

The ai-workflow-red-team-lite skill is designed as a specialized defensive assessment tool for developers and AI engineers to stress-test their automated AI workflows. Instead of acting as an attacker, this skill functions as a professional 'Red Teamer' that analyzes your workflow architecture to identify potential vulnerabilities, logic flaws, and data exposure risks before they reach production. It focuses on identifying edge-case failures, improper input handling, and potential misuse paths, providing you with actionable insights to harden your AI agents and automation chains.

Installation

To install this skill, run the following command in your terminal: clawhub install openclaw/skills/skills/52yuanchangxing/ai-workflow-red-team-lite

Use Cases

This skill is ideal for teams building complex AI agent workflows who need to verify security and stability. Common scenarios include:

• Validating input validation logic against prompt injection or malformed data injection.
• Analyzing boundary failures in multi-step agents where data might leak between internal states.
• Reviewing permission boundaries for agents that handle sensitive user data or access internal tools.
• Auditing the resilience of autonomous processes to ensure they do not perform unauthorized external actions.

Example Prompts

1. "I have an AI agent that retrieves user CRM data and summarizes it for a manager. Please perform a red-team analysis on the workflow logic to check for potential data leakage between user sessions."
2. "Here is the YAML specification for my automated customer service agent. Can you identify potential misuse paths where the agent might be tricked into giving discounts outside of the defined policy?"
3. "Please review this automated workflow for handling user feedback. I am concerned about boundary failures—what could go wrong if the LLM receives unexpected or adversarial input?"

Tips & Limitations

• Defensive Focus Only: This skill is strictly for security research and hardening. It is programmed to refuse requests that involve generating malicious exploit code or attacking external systems.
• Dry-Run Default: When dealing with high-risk operations, the skill prioritizes providing audit-ready, review-only documentation rather than executing changes.
• Information Quality: The quality of the red-team analysis is directly proportional to the detail provided in your workflow description. Always include the input structure, output format, and the explicit permission boundaries the AI is supposed to respect.
• Human-in-the-Loop: Always interpret the 'Attacker Surface' and 'Mitigation' sections as starting points. Security in AI systems requires continuous monitoring and regular re-evaluation as workflow logic evolves.