Skill UI

A comprehensive guide providing step-by-step instructions for upgrading Langfuse SDK versions (v3 to v5) and migrating between major API changes. It covers best practices for both TypeScript/JavaScript and Python implementations, focusing on adopting OpenTelemetry standards to ensure seamless LLM observability integration.

Documents setting up OpenTelemetry tracing/metrics, Prometheus/Grafana monitoring, structured logging, and alerting rules so teams can instrument services, track performance, and respond to incidents.

Expert skill in designing and implementing comprehensive observability stacks for enterprise microservices. Covers advanced monitoring (Prometheus, Grafana), distributed tracing (Jaeger, OpenTelemetry), centralized logging (ELK, Splunk), and defining SLIs/SLOs. Ideal for ensuring high availability, mastering incident response, and performing reliability engineering on large-scale, distributed applications.

This skill simulates an expert performance engineer capable of diagnosing complex bottlenecks across the entire technology stack. It covers modern observability techniques (OpenTelemetry, APM), advanced profiling (CPU, Memory, I/O), and sophisticated load testing strategies (k6, Chaos Engineering). Use this when optimizing scalability, reducing latency, improving throughput, or architecting highly reliable, performant distributed systems in cloud environments.

This guide provides a comprehensive setup for Vercel observability. It covers enabling built-in analytics, collecting detailed runtime logs, implementing structured logging, configuring external log drains (e.g., Datadog, Sentry), and integrating OpenTelemetry tracing. Use this when monitoring production deployments and setting up robust error tracking.

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.

This skill provides a comprehensive framework for building automated pipelines that ingest, normalize, and operationalize threat intelligence (TI). It connects diverse sources—including STIX/TAXII feeds, open-source feeds (e.g., Abuse.ch), and commercial TI platforms—into SIEM and security tools. Use this when SOC teams need to automate the entire lifecycle of IOC ingestion, scoring, and real-time matching against network telemetry.

This skill provides comprehensive observability tooling for Go services, covering the five essential signals: structured logging (slog), Prometheus metrics, OpenTelemetry tracing, continuous profiling, and RUM. Use it for proactive production monitoring, correlating logs with traces, setting up SLOs, and ensuring all new features are fully instrumented for robust diagnosis.

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.

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.