Loading...
Loading...
Found 12 Skills
Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.
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.
Use this skill when implementing logging, metrics, distributed tracing, alerting, or defining SLOs. Triggers on structured logging, Prometheus, Grafana, OpenTelemetry, Datadog, distributed tracing, error tracking, dashboards, alert fatigue, SLIs, SLOs, error budgets, and any task requiring system observability or monitoring setup.
OpenTelemetry, structured logging, distributed tracing, alerting, and dashboards
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.
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.
Instrument a Java application with the Elastic Distribution of OpenTelemetry (EDOT) Java agent for automatic tracing, metrics, and logs. Use when adding observability to a Java service that has no existing APM agent.
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.
Instrument applications with OpenTelemetry SDK and validate telemetry using Kopai. Use when setting up observability, adding tracing/logging/metrics, testing instrumentation, or debugging missing telemetry data.
Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.
Integrates Flowlines observability SDK into Python LLM applications. Use when adding Flowlines telemetry, instrumenting LLM providers, or setting up OpenTelemetry-based LLM monitoring.
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.