Skill UI

This utility detects and classifies various anomalies (such as spikes, flatlines, drift, or pattern breaks) within telemetry data streamed from IoT devices. It utilizes Z-score anomaly detection to provide actionable insights. This is crucial for investigating unusual device metrics, ensuring system health before deploying major firmware updates (canary advancement), or triaging fleet-wide operational alerts.

Enable, disable, or check the status of mobile-app usage telemetry. Use when the user wants to opt in/out of usage data collection or check telemetry status.

Instrument AI agents with OpenTelemetry tracing, Prometheus metrics, token and cost tracking, latency monitoring, and structured logs for reliability and debugging.

This skill covers using the Azure Event Hubs SDK for TypeScript to build high-throughput event streaming applications. It includes real-time data ingestion, sending events to specific partitions, receiving events with checkpointing for production reliability, and handling batch processing options. Ideal for IoT telemetry and real-time data pipelines.

Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights. This skill provides Java code examples for setting up the exporter, creating spans, adding attributes, custom processors, nested spans, exception recording, and metrics. Note: package deprecated, use autoconfigure instead.

Python low-level exporter for sending OpenTelemetry traces, metrics, and logs to Azure Application Insights. Supports custom pipelines, sampling, Azure AD auth, and offline storage.

One-line setup of Azure Monitor Application Insights for Python apps using OpenTelemetry auto-instrumentation. Covers Flask, Django, FastAPI, custom traces, metrics, logs, sampling, and AAD auth.

Auto-instrument Node.js and TypeScript apps with Azure Monitor OpenTelemetry for distributed tracing, custom metrics, and log ingestion. Covers auto-instrumentation, ESM loader, sampling, exporters, and manual provider setup.

Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices. Configure OpenTelemetry SDK and exporters to diagnose latency and errors. Ensure secure context propagation without logging sensitive data. Verify trace spans and sampling strategies for production observability.

A comprehensive guide for designing and implementing production-grade microservices using gRPC in Go. Covers best practices for API contract standardization with Protobuf and Buf, securing service-to-service communication via mTLS, and integrating deep observability using OpenTelemetry interceptors. Ideal for building high-performance, reliable internal APIs.

Install and configure the Manifest observability plugin, setting up telemetry, configuring API keys, and troubleshooting integration.

This skill enables building production-ready monitoring, logging, and tracing systems for enterprise applications. It covers implementing observability strategies, managing SLIs and SLOs, and handling incident response workflows. Expertise includes distributed tracing, log management, alerting, and reliability engineering practices using tools like Prometheus, Grafana, and OpenTelemetry to ensure system stability and performance.