Skill UI

This skill guides the integration of Arize AX tracing into existing applications, especially complex LLM applications. It follows a structured, two-phase, agent-assisted flow: first analyzing the codebase (read-only) to understand dependencies, and then implementing instrumentation. It supports multi-language environments (Python, TypeScript, Java, Go) and integrates deeply with OpenTelemetry and various LLM providers, ensuring comprehensive observability without altering core business logic.

OpenSearch is a distributed search and analytics engine integrated into the KubeSphere WizTelemetry Observability Platform. It is crucial for storing, searching, and analyzing various types of observability data, including application logs, system events, audit records, and notification history. This guide covers installation, configuration, and operational best practices for managing the OpenSearch cluster within KubeSphere.

This service provides a centralized, common API server backend for the entire KubeSphere WizTelemetry Observability Platform. It unifies backend functionalities for core observability components including Logging, Auditing, Events, Tracing, and Notifications, enabling global monitoring and streamlined platform management within Kubernetes environments. Use it when installing or configuring the core telemetry platform.

This guide provides comprehensive instructions for setting up monitoring and observability for Qdrant deployments. It helps users track critical performance metrics, check system health, and diagnose complex production issues such as memory growth or slow query requests. Use cases include setting up Prometheus, Grafana, and implementing robust health checks.

A comprehensive guide to implementing observability for Klaviyo API integrations. This solution uses Prometheus metrics (counters, histograms, gauges) to track API volume, latency, and errors, combined with OpenTelemetry for distributed tracing and structured logging. Ideal for setting up robust monitoring dashboards and critical alerting systems for marketing data pipelines.

This guide details how to implement comprehensive observability for various Adobe API integrations (e.g., Firefly, PDF Services). It covers the four pillars of observability: structured metrics using Prometheus, distributed tracing via OpenTelemetry, structured JSON logging, and defining critical alert rules. This ensures deep visibility into API performance, latency, and error rates for robust production monitoring.

This skill provides a comprehensive guide to instrumenting Algolia search services for enterprise-grade observability. It integrates core monitoring patterns, including Prometheus metrics (latency, error rates, request counts), OpenTelemetry distributed tracing, and structured logging. Use this to achieve deep insights into search performance, user experience, and system health in production environments.

This guide provides a comprehensive observability stack for Apple Notes on macOS. Since Notes lacks a built-in metrics API, this solution demonstrates how to monitor key health indicators—such as note/folder counts, system latency, and iCloud sync daemon status—by polling via JXA and Bash scripts. It includes scripts for scheduled health checks, trend visualization, and robust error handling for production monitoring environments.

Implement comprehensive observability for Canva Connect API integrations using industry best practices. This guide covers gathering critical metrics (latency, error rates, rate limits) with Prometheus, implementing distributed tracing with OpenTelemetry, and setting up robust alert rules. Essential for monitoring API health, optimizing performance, and ensuring system reliability in SaaS environments.

This skill provides comprehensive observability for ClickUp API integrations. It instruments API calls to capture key metrics like request rate, latency percentiles, error counts, and rate limit usage. It integrates with Prometheus for metrics, OpenTelemetry for distributed tracing, and structured logging for detailed event tracking. Use it to build robust dashboards and set up critical alerts for API performance issues.

A comprehensive solution for setting up production-grade observability for Cohere API v2. It integrates Prometheus metrics, OpenTelemetry tracing, and robust alerting mechanisms. This skill tracks critical operational data including per-endpoint latency, token consumption (input/output), error rates, and usage costs, ensuring reliable monitoring for AI-powered applications.

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.