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 simplifies the process of creating production-grade Model Context Protocol (MCP) servers. Instead of manually writing tool wrappers, it consumes standard OpenAPI specifications to generate complete server scaffolds in Python or TypeScript. It enforces schema validation, manages versioning, and ensures safe, maintainable integration points for LLM agents and AI tools.

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 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.

Ginkgo Cloud Lab provides remote access to an advanced, autonomous biological research platform. Users can submit and manage complex protocols, including cell-free protein expression (validation and optimization) and fluorescent bio-art generation. The platform utilizes Reconfigurable Automation Carts (RACs) and features an integrated AI agent, EstiMate, to assess feasibility and price for custom workflows. Ideal for researchers needing high-throughput, scalable lab services.

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.