Skill UI

A powerful tool for architecting production-grade multi-agent workflows. It allows users to define complex, multi-step AI pipelines using structured patterns (sequential, parallel, hierarchical). Key features include defining explicit handoff contracts, implementing robust failure handling, and managing context/cost discipline across long-running processes, ensuring reliable and scalable AI system deployment.

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.

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 details the implementation of comprehensive security monitoring using Datadog Cloud SIEM, Cloud Security Management (CSM), and Workload Protection. It covers deploying the Datadog Agent across hybrid cloud infrastructure (AWS, Azure, GCP), ingesting critical log sources (CloudTrail, VPC Flow Logs, etc.), configuring Workload Protection, and creating custom detection rules to detect threats, enforce compliance, and provide centralized SOC visibility.

Utilizes ScoutSuite, an open-source multi-cloud auditing tool, to perform comprehensive, agentless security posture assessments across AWS accounts. It queries various AWS APIs to enumerate all resources, identify misconfigurations (such as publicly exposed S3 buckets or weak IAM policies), and generate detailed, actionable HTML reports for compliance and security auditing.

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.

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 detailing the systematic process for upgrading and migrating CAST AI components. This includes updating Helm charts (agent, controller, evictor) and Terraform providers. It outlines prerequisites, step-by-step upgrade procedures, validation checks, and necessary rollback procedures to ensure a safe transition between versions.

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.

A comprehensive guide for simulating a full Software Development Life Cycle (SDLC) using specialized AI roles. It defines protocols for managing cross-functional teams (Dev, QA, DevOps, Product), covering sprint planning, codebase management, bug tracking, and deployment, ensuring context survival across multiple chat sessions.

KubeStellar Console is an open-source, multi-cluster Kubernetes dashboard (CNCF project) that provides advanced operational capabilities powered by an MCP server and built-in AI agent skills. It helps manage complex deployments across edge and cloud environments, offering AI-assisted troubleshooting, performance testing, and deep integration with major CNCF projects like Argo and Istio.