observability▌

Structured logging, metrics, and distributed tracing patterns for Python production systems. \n \n Covers four core observability areas: structured JSON logging with structlog, Prometheus metrics for the four golden signals (latency, traffic, errors, saturation), correlation ID propagation across service boundaries, and OpenTelemetry distributed tracing \n Includes semantic log level guidance, bounded cardinality rules for metrics to prevent storage explosion, and context manager patterns for co

Use this skill when the user needs trace-based observability, not just testing: extract Session Tracing Data Model (STDM) records, work with Parquet datasets, reconstruct session timelines, analyze topic/action latency, or debug agent behavior from Data 360 telemetry.

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.

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.