“observability”

Observability — an observability concept covering observability, trace, telemetry, span, audit.

Guide for instrumenting and operating observable software systems.

Add agent-first observability to code — structured logs, health endpoints, failure-state persistence, and explicit failure modes — so the next agent hitting a…

Choose observability tier by hardware: Grafana Cloud Free + Alloy (light), self-hosted Grafana+Prometheus+Loki single, or full LGTM (Mimir/Loki/Tempo RF=1+).

Observability as an engineering discipline — wide events / canonical log lines, OpenTelemetry instrumentation (traces, metrics, context propagation, sampling…

Practical observability guide for this workspace.

Expert at making systems observable and debuggable.

Observe systems: logging, metrics, tracing, health checks, graceful shutdown.

Instrument code so production behavior is visible and diagnosable — structured logs with correlation ids, metrics, traces, symptom-based alerts and minimal…

Use for correlated API failures, cascading errors, and distributed-system incident tracing.

Instrument, monitor, and debug production systems.

Distributed tracing with Jaeger, OpenTelemetry, and observability platforms for microservices insights

Instruments a feature so production behavior is diagnosable before the first incident: structured logs, RED/USE metrics, traces, symptom alerts.

Log and metrics analyst — reads .cladding/audit.log.jsonl, perf/baseline.json, and drift reports; surfaces patterns the human can act on.

Add or change structured logging, log queries, and infra for observability (e.g.

Design and review logging, metrics, alerts and SLOs for services — mainly Spring Boot on OpenShift with logs in Elasticsearch/Kibana and metrics through…

Add or review logging, metrics, tracing, and alerts for a backend service so production failures are diagnosable: structured log discipline, RED/USE metrics…

Use when adding, reviewing, or debugging logs, metrics, traces, audit events, correlation IDs, lifecycle status, timestamps, retries, or error metadata in…

Use when instrumenting a service (structured logs, metrics, traces), defining SLOs or alerts, building a dashboard, or triaging a live production incident /…

Change trajectory logging, report generation, or runtime error reporting while preserving useful diagnostics and safe HTML output.

Structured logging, request correlation, Prometheus metrics, health probes, and sensitive data redaction.

Add or improve structured logging, metrics, tracing, health checks, error reporting, and operational diagnostics without leaking sensitive data.

Instrument AI workflows with tracing, logging, and monitoring to enable debugging, auditing, and performance analysis.

Use when Pi needs to troubleshoot logs/metrics/traces or verify LGTM (Grafana/Loki/Mimir/Tempo) access in local k3s, cloud Kubernetes, or any generic K8s setup.