Skill UI

This skill provides comprehensive operational controls for long-lived, cloud-hosted agent workloads. It manages the entire lifecycle—from deployment and scaling to observability and security. Features include detailed monitoring (logs, metrics, traces), safety controls (kill switches, least privilege), and robust change management processes (rollout, rollback, auditing). Ideal for mission-critical microservices and background agents.

A comprehensive guide for deploying and configuring CrowdStrike Falcon EDR agents across diverse endpoints (Windows, macOS, Linux). This skill covers silent installation via SCCM, Intune, GPO, and command-line methods, alongside establishing robust prevention and response policies. Ideal for onboarding endpoints, enhancing threat detection capabilities, and integrating Falcon telemetry into SIEM platforms for advanced security operations.

This comprehensive skill guides the deployment and configuration of Wazuh SIEM/XDR for robust endpoint monitoring. Users will learn to manage agents via the API, create custom decoders and rules (XML), query security alerts, and test rule logic, enabling organizations to establish strong security controls and automate incident response.

This comprehensive guide details the deployment of HashiCorp Vault for robust, centralized secrets management in hybrid and multi-cloud environments. It covers configuring dynamic secret engines for databases (PostgreSQL) and cloud providers (AWS), integrating diverse authentication methods (OIDC, AppRole, Kubernetes), and securely delivering short-lived secrets to Kubernetes workloads via agents. The primary goal is to enforce zero-trust principles by eliminating hardcoded, long-lived credentials from application code and CI/CD pipelines.

This guide provides a detailed template and directive for generating machine-readable, deterministic implementation plans. It is designed for complex software development tasks, such as adding new features, refactoring existing code, or designing system architecture. The output ensures that all steps are atomic, measurable, and structured for autonomous execution by AI agents or human teams, minimizing ambiguity and dependency.