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Found 38 Skills
Python observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.
Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.
Use when setting up monitoring systems, logging, metrics, tracing, or alerting. Invoke for dashboards, Prometheus/Grafana, load testing, profiling, capacity planning.
Build microservices - Spring Cloud, service mesh, event-driven, resilience patterns
Observability patterns for metrics, logging, distributed tracing, and error tracking. Trigger: When setting up monitoring, when implementing logging, when adding error tracking, when configuring distributed tracing, when building health checks, when creating dashboards.
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.
Grafana Tempo distributed tracing backend. Covers TraceQL query language (span selectors, attribute scopes, pipeline operators, structural operators, metrics functions), trace ingestion via OTLP/Jaeger/Zipkin, Tempo architecture (distributor/ingester/compactor/querier/metrics-generator), full configuration reference with YAML, metrics-from-traces (span metrics, service graphs, TraceQL metrics), deployment modes (monolithic/microservices/Helm/Kubernetes), multi-tenancy, performance tuning, caching, and HTTP API. Use when working with distributed traces, writing TraceQL queries, deploying Tempo, configuring trace pipelines, or setting up Grafana-Tempo integrations (traces-to-logs, traces-to-metrics, traces-to-profiles).
Structured logging with proper levels, context, PII handling, centralized aggregation. Use for application logging, log management integration, distributed tracing, or encountering log bloat, PII exposure, missing context errors.
Comprehensive logging and observability patterns for production systems including structured logging, distributed tracing, metrics collection, log aggregation, and alerting. Triggers for this skill - log, logging, logs, trace, tracing, traces, metrics, observability, OpenTelemetry, OTEL, Jaeger, Zipkin, structured logging, log level, debug, info, warn, error, fatal, correlation ID, span, spans, ELK, Elasticsearch, Loki, Datadog, Prometheus, Grafana, distributed tracing, log aggregation, alerting, monitoring, JSON logs, telemetry.
Setup Sentry Tracing (Performance Monitoring) in any project. Use this when asked to add performance monitoring, enable tracing, track transactions/spans, or instrument application performance. Supports JavaScript, TypeScript, Python, Ruby, React, Next.js, and Node.js.
You are a monitoring and observability expert specializing in implementing comprehensive monitoring solutions. Set up metrics collection, distributed tracing, log aggregation, and create insightful da
Instruments code so production behavior is visible and diagnosable. Use when adding logging, metrics, tracing, or alerting. Use when shipping any feature that runs in production and you need evidence it works. Use when production issues are reported but you can't tell what happened from the available data.