Skill UI

Provision, configure, and monitor NVIDIA GPU servers for AI inference and training workloads, including driver/CUDA installation, Docker GPU support, MIG partitioning, and health monitoring.

Create and manage production-ready Grafana dashboards for comprehensive system observability. Covers dashboard design principles, panel types, variables, alerts, and best practices for monitoring applications, infrastructure, and business metrics.

Analyze health data trends and patterns over time, including weight, symptoms, medications, and lab results. Identify correlations and significant changes to provide data-driven insights. Supports multi-dimensional analysis and generates interactive HTML visualization reports using ECharts. Ideal for tracking health improvements and monitoring risks.

This agent automates iOS app debugging on a booted simulator using XcodeBuildMCP. It handles project building, running, UI interaction, screenshot capture, and log monitoring. Ideal for testing and inspecting app behavior during development cycles without manual intervention.

This skill provides production-ready shell script templates for common Linux system administration tasks such as backups, monitoring, user management, and log analysis. It serves as a foundation for security operations and penetration testing environments, requiring basic bash knowledge and appropriate system permissions.

This skill enables horizontal scaling of Large Language Model inference clusters on Kubernetes. It utilizes KEDA for event-driven autoscaling based on custom GPU metrics like request queues and cache usage. The guide covers deploying vLLM on GPU nodes, configuring Prometheus for monitoring, implementing spot instance strategies for cost efficiency, and setting up queue-based scaling for batch jobs. It ensures optimal resource utilization and cost management for high-traffic AI workloads.

This skill covers configuring Grafana Loki for log aggregation and analysis within DevOps environments. It includes setting up Promtail for log shipping, deploying via Docker or Kubernetes, and writing LogQL queries for filtering. Ideal for replacing ELK stacks with a lighter-weight solution for monitoring application and system logs.

A powerful tool utilizing Rube MCP to automate interactions with Make (formerly Integromat) scenarios. It allows users to retrieve detailed operation logs, monitor usage data, and validate critical configuration parameters such as supported languages and timezones. Ideal for building comprehensive workflow monitoring dashboards and ensuring the integrity of complex integration pipelines.

This skill enables building production-ready machine learning systems using modern frameworks like PyTorch and TensorFlow. It covers model serving, feature engineering, A/B testing, monitoring, and MLOps practices. Focuses on scalable, reliable, and efficient ML infrastructure for delivering business value in production environments.

End-to-end MLOps pipeline orchestration guidance covering data preparation, model training, validation, deployment, monitoring, and workflow automation best practices.

This skill evaluates the health of data assets using Monte Carlo's observability platform. It generates structured reports on freshness, alerts, monitoring coverage, importance, and upstream dependencies. Use it when users inquire about asset status, reliability, or quality.

Analyzes data coverage gaps and creates monitors for warehouse tables and AI agents. Covers use-case analysis, data monitor creation, monitors-as-code YAML, and agent observability.