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Found 66 Skills
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.
Migrate a .NET application from the classic Elastic APM .NET agent to the EDOT .NET SDK. Use when switching from Elastic.Apm.* packages to Elastic.OpenTelemetry.
Migrate a Python application from the classic Elastic APM Python agent to the EDOT Python agent. Use when switching from elastic-apm to elastic-opentelemetry.
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.
Implement OpenTelemetry logs/metrics/traces, SLI/SLO gates, burn-rate alerts, and APM integrations. Use when adding or validating observability.
Use this skill when working with SigNoz - open-source observability platform for application monitoring, distributed tracing, log management, metrics, alerts, and dashboards. Triggers on SigNoz setup, OpenTelemetry instrumentation for SigNoz, sending traces/logs/metrics to SigNoz, creating SigNoz dashboards, configuring SigNoz alerts, exception monitoring, and migrating from Datadog/Grafana/New Relic to SigNoz.
Structured observability with Pydantic Logfire and OpenTelemetry. Use when: (1) Adding traces/logs to Python APIs, (2) Instrumenting FastAPI, HTTPX, SQLAlchemy, or LLMs, (3) Setting up service metadata, (4) Configuring sampling or scrubbing sensitive data, (5) Testing observability code.
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.
Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.
Implements comprehensive observability with OpenTelemetry tracing, Prometheus metrics, and structured logging. Includes instrumentation plans, sample dashboards, and alert candidates. Use for "observability", "monitoring", "tracing", or "metrics".
Monitoring and observability with OpenTelemetry, Prometheus, Grafana dashboards, and structured logging