Skill UI

Helps analyze current CPU, memory, storage, and network utilization to forecast future infrastructure needs, identify bottlenecks, and recommend scaling tactics with cost context for proactive capacity management.

Helps analyze current CPU, memory, storage, and network utilization to forecast future infrastructure needs, identify bottlenecks, and recommend scaling tactics with cost context for proactive capacity management.

This skill provides comprehensive observability for the Databricks platform. It enables users to monitor job run histories, track cluster utilization and associated costs (DBU), detect slow SQL queries, and audit access logs. Use it for setting up robust monitoring dashboards, ensuring pipeline health, and optimizing resource consumption across the entire data stack.

A comprehensive toolkit for monitoring the health and performance of Fireflies.ai integration. It tracks crucial metrics such as API connectivity, webhook delivery status, transcript processing latency, and seat utilization. Built using Prometheus metrics and suitable for Grafana dashboards, ensuring reliable data pipeline monitoring for engineering teams.

This workflow guides users through configuring CAST AI's autoscaler policies to achieve optimal cost management and resource utilization within a Kubernetes cluster. It covers enabling spot instances, setting node downscaler/evictor rules, defining cluster limits, and creating specialized node templates (e.g., for GPU or spot workers) using Terraform. Use this when implementing Phase 2 automation or optimizing cloud spend.

This skill provides comprehensive observability for CoreWeave's GPU-intensive workloads running on Kubernetes. It monitors critical operational metrics, including GPU utilization, memory usage, inference latency (p99), and job completion rates. Use it for setting up DCGM metrics dashboards, configuring proactive alerts for underutilization or Out-Of-Memory (OOM) conditions, and ensuring high reliability for AI/ML deployments.

This guide provides expert strategies for optimizing GPU inference performance on CoreWeave infrastructure. It covers GPU selection based on workload (LLM, image generation, training), advanced techniques like continuous batching (using vLLM), autoscaling setup (HPA), and benchmarking data. Use this to maximize GPU utilization, minimize latency, and optimize large-scale AI model deployment.

Programmatically query Oracle Cloud Infrastructure (OCI) metrics using the Monitoring Query Language (MQL) and create monitoring alarms via the Python SDK. This skill bypasses the limitations of the OCI console query builder, allowing reliable retrieval of key infrastructure metrics such as CPU utilization, memory usage, network traffic, and disk I/O. Ideal for building robust dashboards, automating resource monitoring, and setting up complex alerting rules.

This guide provides best practices for scaling multi-tenant deployments in Qdrant. It advises against the inefficient 'one collection per tenant' approach. Solutions include using shared collections with payload filtering, implementing custom sharding for large-scale tenants (100k+), and employing tiered multitenancy for unevenly sized workloads, ensuring optimal resource utilization and performance.