observability▌

Comprehensive observability for Istio and Linkerd service meshes with distributed tracing, metrics, and visualization. \n \n Covers three observability pillars: metrics (request rate, error rate, latency), traces (span context, dependencies, bottlenecks), and logs (access logs, error details) \n Includes ready-to-use templates for Prometheus, Grafana, Jaeger, Kiali, and OpenTelemetry integration with Istio and Linkerd \n Provides golden signals framework (latency, traffic, errors, saturation) wi

Category: test

Comprehensive patterns for infrastructure monitoring, LLM observability, and quality drift detection. Each category has individual rule files in rules/ loaded on-demand.

You are an SLO (Service Level Objective) expert specializing in implementing reliability standards and error budget-based engineering practices. Design comprehensive SLO frameworks, establish meaningful SLIs, and create monitoring systems that balance reliability with feature velocity.

Modern Go applications require comprehensive observability through the three pillars: traces, metrics, and logs. OpenTelemetry provides vendor-neutral instrumentation for distributed tracing, Prometheus offers powerful metrics collection, and Go's slog package (1.21+) delivers structured logging with minimal overhead.

Create and manage SLOs in Elastic Observability. SLOs track service performance against measurable targets using service-level indicators (SLIs) computed from Elasticsearch data.

Search and filter logs to support incident investigation. The workflow mirrors Kibana Discover: apply a time range and scope filter, then iteratively add exclusion filters (NOT) until a small, interesting subset of logs remains—either the root cause or the key document. Optionally view logs in context (preceding and following that document) or pivot to another entity and start a fresh search. Use ES|QL only (POST /_query); do not use Query DSL.

Assess APM service health using Observability APIs, ES|QL against APM indices, Elasticsearch APIs, and (for correlation and APM-specific logic) the Kibana repo. Use SLOs, firing alerts, ML anomalies, throughput, latency (avg/p95/p99), error rate, and dependency health.

Instrument LLM applications with Langfuse tracing, following best practices and tailored to your use case.

Persona: You are a Go observability engineer. You treat every unobserved production system as a liability — instrument proactively, correlate signals to diagnose, and never consider a feature done until it is observable.