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Found 103 Skills
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.
Use this skill first whenever the user asks about SigNoz instrumentation, OpenTelemetry setup, querying, dashboards, alerts, troubleshooting, self-hosted deployment, API endpoints, auth headers, or where to find anything in SigNoz docs.
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.
Implement OpenTelemetry logs/metrics/traces, SLI/SLO gates, burn-rate alerts, and APM integrations. Use when adding or validating observability.
OpenTelemetry with Grafana stack. Covers OTel SDK instrumentation for Go/Java/Python/Node.js/.NET, OTLP protocol and endpoint configuration, sending telemetry to Grafana Cloud via OTLP endpoint, Grafana Alloy as OTel collector, sampling strategies, Kubernetes OTel Operator, and migration from other observability tools. Use when instrumenting apps with OTel, configuring OTLP endpoints, setting up collectors, or migrating to OpenTelemetry.
Integrates Flowlines observability SDK into Python LLM applications. Use when adding Flowlines telemetry, instrumenting LLM providers, or setting up OpenTelemetry-based LLM monitoring.
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.
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.
See exactly what your AI did on a specific request. Use when you need to debug a wrong answer, trace a specific AI request, profile slow AI pipelines, find which step failed, inspect LM calls, view token usage per request, build audit trails, or understand why a customer got a bad response. Covers DSPy inspection, per-step tracing, OpenTelemetry instrumentation, and trace viewer setup.
Set up Kafka-based event-driven microservices with Platformatic Watt. Use when users ask about: - "kafka", "event-driven", "messaging" - "kafka hooks", "kafka webhooks" - "kafka producer", "kafka consumer" - "dead letter queue", "DLQ" - "request response pattern" with Kafka - "migrate from kafkajs", "kafkajs migration", "replace kafkajs" Covers @platformatic/kafka, @platformatic/kafka-hooks, consumer lag monitoring, and OpenTelemetry instrumentation.
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.