Individual skill

observability-setup

Instruments applications with structured logging, metrics, and distributed traces, then derives SLIs, SLOs, error budgets, and alerts that page only on user-facing pain. Use this skill when the user asks to "add observability", "instrument my app", "set up OpenTelemetry/OTel", "add structured logging", "expose Prometheus metrics", "add tracing", "define SLOs/SLIs", "set up alerting", "reduce alert noise", "build a Grafana dashboard", or wants the three pillars (logs/metrics/traces) wired into a service.

observability-setup/SKILL.md

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Repository stars (not skill ratings): 12
Repository last updated: Oct 14, 2026
License: MIT

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SKILL.mdcore · 8,106 bytes
SKILL.mdcore · 8,106 bytes
examples/instrument-flask-service.mddoc · 5,025 bytes
references/instrumentation-patterns.mddoc · 5,880 bytes
references/slo-cookbook.mddoc · 4,378 bytes
scripts/check_slo_burn.pyruns · 3,705 bytes
scripts/gen_slo_alerts.pyruns · 5,435 bytes
templates/slo-spec.mddoc · 2,386 bytes

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# Observability Setup

## Overview

Observability is the ability to ask arbitrary questions about a system's behavior from the outside, using its telemetry. This skill instruments a service across the **three pillars** — structured logs, metrics, and distributed traces — then turns raw signals into **SLIs**, **SLOs**, **error budgets**, and **actionable alerts**. The goal is not "more dashboards" but the ability to answer *"is the user in pain, and where?"* in under five minutes.

**Keywords:** observability, OpenTelemetry, OTel, structured logging, JSON logs, Prometheus, metrics, RED method, USE method, distributed tracing, spans, trace context, correlation ID, SLI, SLO, error budget, burn rate, alerting, Alertmanager, Grafana, on-call, golden signals, cardinality, exemplars.

Standardize on **OpenTelemetry (OTel)** for instrumentation — it is vendor-neutral and emits to Prometheus, Tempo, Loki, Datadog, Honeycomb, etc. Keep instrumentation code free of vendor SDKs; push vendor choice to the OTel Collector.

## Workflow

1. **Inventory the service.** Identify language/runtime, request entry points (HTTP/gRPC/queue), critical user journeys, downstream dependencies, and the existing telemetry backend (or pick one). Read `references/instrumentation-patterns.md` for per-pillar guidance.
2. **Add structured logging first.** Emit one JSON event per request with a stable schema (timestamp, level, message, `trace_id`, `span_id`, service, plus event-specific fields). Never log secrets/PII. Logs become useful only once they are queryable and correlated to traces.