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Found 17 Skills
Guide for AI-powered penetration testing tools, red teaming frameworks, and autonomous security agents.
Enterprise Skill for advanced development
Enterprise Skill for advanced development
Enterprise Skill for advanced development
Multi-cloud security assessment skill for AWS, Azure, and GCP. This skill should be used when performing cloud security audits, scanning for misconfigurations, testing IAM policies, auditing storage permissions, and identifying privilege escalation paths. Triggers on requests to audit cloud security, scan AWS/Azure/GCP, check cloud misconfigurations, or perform cloud penetration testing.
Comprehensive API security testing skill for REST, GraphQL, gRPC, and WebSocket APIs. This skill should be used when performing API penetration testing, testing for OWASP API Top 10 vulnerabilities, fuzzing API endpoints, testing authentication/authorization, and analyzing API specifications. Triggers on requests to test API security, pentest REST APIs, test GraphQL endpoints, analyze OpenAPI/Swagger specs, or find API vulnerabilities.
Security audit for LLM and GenAI applications using OWASP Top 10 for LLM Apps 2025. Assess prompt injection, data leakage, supply chain, and 7 more critical vulnerabilities.
Use when reviewing code for security vulnerabilities, implementing authentication/authorization, handling user input, or discussing web application security. Covers OWASP Top 10:2025, ASVS 5.0, and Agentic AI security (2026).
AI agent configuration policy and security guide. Project description file writing, Hooks/Skills/Plugins setup, security policy, team shared workflow definition.
MCP architecture patterns, security, and memory management. Auto-loads when building MCP servers, implementing tools/resources, discussing MCP security, or working with FastMCP.
Use when building secure AI pipelines or hardening LLM integrations. Defense-in-depth implements 8 validation layers from edge to storage with no single point of failure.
OWASP Top 10 for LLM Applications - prevention, detection, and remediation for LLM and GenAI security. Use when building or reviewing LLM apps - prompt injection, information disclosure, training/supply chain, poisoning, output handling, excessive agency, system prompt leakage, vectors/embeddings, misinformation, unbounded consumption.