Skill UI

This skill provides comprehensive security and monitoring for Large Language Model (LLM) powered trading agents. It helps to audit trade decisions, ensure compliance with risk parameters, and mitigate potential risks associated with automated financial operations.

Automates CI/CD pipeline setup, from linting and type checks to integration, E2E, security, and deployment stages, plus feedback loops for AI agents and preview deployments.

This framework provides a rigorous, multi-dimensional guide for conducting code reviews. It evaluates five critical axes: correctness, readability, architecture, security, and performance. Use this process before merging any code change, especially when reviewing PRs, refactoring, or evaluating code produced by AI agents, to ensure continuous improvement and maintain high code health.

This guide details the process of creating and managing 'Agent Users' within Microsoft Entra ID. Agent Users are specialized identities that allow AI agents to function as digital workers, giving them user-level capabilities (e.g., accessing Exchange mailboxes or Teams) while maintaining robust security boundaries. It covers using the Microsoft Graph API and PowerShell to provision and configure these agents.

Checks AI agent implementations against the OWASP Agentic Security Initiative Top 10 risks, producing findings on controls, vulnerabilities, and recommendations for prompt injection, tool governance, escalation, logging, identity, policy, supply chain, and behavioral monitoring before production or audit.

This guide details securing the CAST AI integration across multiple layers. It covers best practices for API key management (including rotation and secrets storage), implementing least-privilege RBAC using 'kubectl', deploying the Kvisor security agent for runtime scanning, and setting up Kubernetes Network Policies to restrict egress traffic. Use this when hardening production clusters or performing security audits.

A comprehensive guide to connecting any Kubernetes cluster (EKS, GKE, AKS) to CAST AI for cost optimization, autoscaling, and security monitoring. This skill covers the entire setup process, including generating API keys, installing the CAST AI agent via Helm charts, and integrating the provider using Terraform.

This tool provides comprehensive AI security scanning using the Tencent AI-Infra-Guard framework. It systematically audits AI infrastructure, scans tools and skills, checks agent security, and performs advanced LLM jailbreak evaluations to identify vulnerabilities and ensure the robustness and reliability of AI systems.

A comprehensive tool designed to scan any agent skill for potential security risks and vulnerabilities before implementation or use. Powered by local static analysis (A.I.G.), it ensures that no file contents, credentials, or personal data ever leave your device, providing maximum privacy and safety for integrating new AI capabilities across various agent platforms.

This framework implements the OWASP Top 10 standards, specifically tailored for Agentic Applications. It provides a standardized methodology to classify and map diverse security findings—such as prompt injection, tool misuse, and privilege abuse—to ten core risk categories (ASI01-ASI10). It is essential for security teams to systematically assess, prioritize, and remediate vulnerabilities inherent in complex AI agent systems.

This skill provides a comprehensive framework for security auditing LLM agents that possess external tools (file system access, command execution, network calls). It employs advanced dialogue probing techniques—such as command injection, path traversal, and SSRF attempts—to identify vulnerabilities where the agent might be induced to execute unexpected or malicious code, or access sensitive resources. Essential for red teaming and securing agent functionalities.

This framework enforces high output quality, intellectual honesty, and continuous improvement across all AppSec agents. It mandates structured processes, including self-critique loops, anti-rationalization guards, and self-reflection quality gates (≥8 scoring). Applicable during code review, SAST, SCA, and threat modeling, it ensures rigorous and verifiable security analysis.