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Found 103 Skills
Observability guidelines for distributed systems using OpenTelemetry, tracing, metrics, and structured logging
Guide for implementing Grafana Loki - a horizontally scalable, highly available log aggregation system. Use when configuring Loki deployments, setting up storage backends (S3, Azure Blob, GCS), writing LogQL queries, configuring retention and compaction, deploying via Helm, integrating with OpenTelemetry, or troubleshooting Loki issues on Kubernetes.
Expert guidance for emitting high-quality, cost-efficient OpenTelemetry telemetry. Use when instrumenting applications with traces, metrics, or logs. Triggers on requests for observability, telemetry, tracing, metrics collection, logging integration, or OTel setup.
Expert guidance for configuring and deploying the OpenTelemetry Collector. Use when setting up a Collector pipeline, configuring receivers, exporters, or processors, deploying a Collector to Kubernetes or Docker, or forwarding telemetry to Dash0. Triggers on requests involving collector, pipeline, OTLP receiver, exporter, or Dash0 collector setup.
Instrument a Python application with the Elastic Distribution of OpenTelemetry (EDOT) Python agent for automatic tracing, metrics, and logs. Use when adding observability to a Python service that has no existing APM agent.
Implement OpenTelemetry logs/metrics/traces, SLI/SLO gates, burn-rate alerts, and APM integrations. Use when adding or validating observability.
Integrates Flowlines observability SDK into Python LLM applications. Use when adding Flowlines telemetry, instrumenting LLM providers, or setting up OpenTelemetry-based LLM monitoring.
Instrument a .NET application with the Elastic Distribution of OpenTelemetry (EDOT) .NET SDK for automatic tracing, metrics, and logs. Use when adding observability to a .NET service that has no existing APM agent.
Configure OpenTelemetry distributed tracing, metrics, and logging in ASP.NET Core using the .NET OpenTelemetry SDK. Use when adding observability, setting up OTLP exporters, creating custom metrics/spans, or troubleshooting distributed trace correlation.
Set up orq.ai observability for LLM applications. Use when setting up tracing, adding the AI Router proxy, integrating OpenTelemetry, auditing existing instrumentation, or enriching traces with metadata.
Monitoring and observability strategy, implementation, and troubleshooting. Use for designing metrics/logs/traces systems, setting up Prometheus/Grafana/Loki, creating alerts and dashboards, calculating SLOs and error budgets, analyzing performance issues, and comparing monitoring tools (Datadog, ELK, CloudWatch). Covers the Four Golden Signals, RED/USE methods, OpenTelemetry instrumentation, log aggregation patterns, and distributed tracing.
This skill should be used when adding error tracking and performance monitoring with Sentry and OpenTelemetry tracing to Next.js applications. Apply when setting up error monitoring, configuring tracing for Server Actions and routes, implementing logging wrappers, adding performance instrumentation, or establishing observability for debugging production issues.