Skill UI

This skill guides users on performing deep runtime threat detection and forensics in cloud-native environments using Falco. It leverages Falco YAML rules to monitor syscalls, detecting critical security events such as shell spawning, file tampering (e.g., /etc/shadow), network anomalies, and privilege escalation within containers and Kubernetes clusters. Ideal for incident response, security auditing, and validating container runtime security controls.

Perform interactive malware analysis in the ANY.RUN cloud sandbox to trigger real-time behavior, inspect process trees, monitor network traffic, and capture system changes without local sandbox setup.

This sophisticated tool automates the security review of serverless functions across major cloud providers, including AWS Lambda, Azure Functions, and GCP Cloud Functions. It systematically identifies critical misconfigurations such as overly permissive IAM execution roles (wildcard actions), hardcoded secrets in environment variables, and potential lack of runtime protections. Ideal for pre-production auditing and compliance checks.

This skill provides a comprehensive guide to hardening managed Kubernetes clusters on major cloud platforms (EKS, AKS, GKE). It details implementing critical security layers, including Pod Security Standards, least-privilege network policies, workload identity (eliminating static cloud credentials), and runtime security monitoring. Essential for securing production environments and meeting compliance requirements.

Hono is a minimal and ultrafast web framework built entirely on Web Standards (Request/Response/fetch). It allows developers to build high-performance APIs and full-stack applications that run seamlessly across multiple environments, including Cloudflare Workers, Deno, Bun, and Node.js. It is ideal for edge deployment, Backend for Frontend (BFF) layers, and type-safe backend services.

This skill automates the process of renting, managing, and terminating cloud GPU instances on vast.ai. It analyzes your AI/ML task—determining VRAM, required GPU count, and estimated runtime—and then searches for the most cost-effective and reliable GPU offers, presenting optimized options for your training workflow.

Utilize Modal, a serverless GPU cloud, for running complex machine learning workloads, including training, fine-tuning, and inference. This tool provides a zero-config environment, eliminating the need for SSH setup or Docker management. It features automatic scale-to-zero billing and local-first execution, making high-powered GPU compute accessible for debugging and small-scale experiments.

This skill provides advanced debugging capabilities for failing Power Automate cloud flows using the FlowStudio MCP server. Unlike the limited status codes from the standard Graph API, this tool gives agents deep, action-level inputs and outputs, allowing users to find the true root cause of failures. It is essential for diagnosing complex issues like expression failures, connector authentication errors, or internal run errors.

Process videos, audio, images, and documents on Transloadit’s cloud using 86+ robots to encode formats, generate thumbnails, resize/watermark, run OCR, speech-to-text, moderation, and chain multi-step pipelines for scalable media automation.

This skill guides the management of CI/CD pipelines within KubeSphere DevOps. It utilizes Kubernetes Custom Resources (CRs) to define, trigger, and monitor complex build workflows, seamlessly integrating with Jenkins concepts in a cloud-native manner. Use it for automating build, test, and deployment processes for applications running on Kubernetes.

Modal is a serverless cloud platform designed for executing Python-based AI/ML workloads. It provides on-demand access to powerful GPUs (e.g., H100, A100), scalable serverless functions, and custom containerization. Use cases include deploying ML models, running large-scale training/inference jobs, creating web APIs, and scheduling data pipelines, all defined entirely in code.

This runbook provides systematic procedures for diagnosing and resolving common automation failures specific to Apple Notes on macOS. It covers critical local issues like app crashes, iCloud sync failures, TCC permission revocations, and database corruption, following a comprehensive detect-diagnose-fix-verify cycle. Essential for maintaining reliable Notes automation workflows.