observability-setup

Getting Started

1. Install the skill using the command above
2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
3. Reference the skill in your prompt
4. The AI will use the skill's capabilities automatically

Example Prompts

• "Deploy the latest build to the staging environment and run smoke tests"
• "Check the CI pipeline status and summarize any recent failures"

Overview

This skill helps AI agents implement the three pillars of observability — traces, metrics, and structured logs — across microservice architectures. It uses OpenTelemetry as the instrumentation standard and Grafana's LGTM stack (Loki, Grafana, Tempo, Mimir) as the backend, though patterns apply to any OTel-compatible backend.

Instructions

OpenTelemetry Instrumentation (Node.js)

1. Install packages:

   bash

   npm install @opentelemetry/sdk-node @opentelemetry/auto-instrumentations-node \
     @opentelemetry/exporter-trace-otlp-http @opentelemetry/exporter-metrics-otlp-http
   

2. Create src/instrumentation.ts — this file must be loaded BEFORE any other imports:

   ts

   import { NodeSDK } from '@opentelemetry/sdk-node';
   import { getNodeAutoInstrumentations } from '@opentelemetry/auto-instrumentations-node';
   import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http';
   import { OTLPMetricExporter } from '@opentelemetry/exporter-metrics-otlp-http';
   import { PeriodicExportingMetricReader } from '@opentelemetry/sdk-metrics';
   
   const sdk = new NodeSDK({
     serviceName: process.env.OTEL_SERVICE_NAME || 'my-service',
     traceExporter: new OTLPTraceExporter({ url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT + '/v1/traces' }),
     metricReader: new PeriodicExportingMetricReader({
       exporter: new OTLPMetricExporter({ url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT + '/v1/metrics' }),
       exportIntervalMillis: 15000,
     }),
     instrumentations: [getNodeAutoInstrumentations()],
   });
   sdk.start();
   

3. Add to start script: node --require ./src/instrumentation.ts src/index.ts

4. Add custom spans for business-critical operations:

   ts

   import { trace } from '@opentelemetry/api';
   const tracer = trace.getTracer('payment-service');
   
   async function processPayment(order) {
     return tracer.startActiveSpan('process-payment', async (span) => {
       span.setAttribute('order.id', order.id);
       span.setAttribute('payment.amount_cents', order.totalCents);
       try {
         const result = await paymentGateway.charge(order);
         span.setAttribute('payment.status', result.status);
         return result;
       } catch (error) {
         span.recordException(error);
         span.setStatus({ code: SpanStatusCode.ERROR, message: error.message });
         throw error;
       } finally {
         span.end();
       }
     });
   }
   

OpenTelemetry Instrumentation (Python)

1. Install: pip install opentelemetry-distro opentelemetry-exporter-otlp
2. Run: opentelemetry-instrument --service_name notification-service python app.py
3. For structured logs with trace context:
   python

   import structlog
   from opentelemetry import trace
   
   def add_trace_context(logger, method_name, event_dict):
       span = trace.get_current_span()
       ctx = span.get_span_context()
       if ctx.is_valid:
           event_dict['trace_id'] = format(ctx.trace_id, '032x')
           event_dict['span_id'] = format(ctx.span_id, '016x')
       return event_dict
   
   structlog.configure(processors=[add_trace_context, structlog.dev.ConsoleRenderer()])
   

Structured Logging with Trace Correlation

Every log line must include trace_id and span_id. Use JSON format:

{"level":"error","msg":"payment failed","trace_id":"abc123...","span_id":"def456...","service":"payment-service","error":"timeout","timestamp":"2025-01-15T10:30:00Z"}

For Node.js, use pino with a custom mixin:

const pino = require('pino');
const { trace } = require('@opentelemetry/api');

const logger = pino({
  mixin() {
    const span = trace.getActiveSpan();
    if (span) {
      const ctx = span.spanContext();
      return { trace_id: ctx.traceId, span_id: ctx.spanId };
    }
    return {};
  }
});

OTel Collector Configuration

receivers:
  otlp:
    protocols:
      grpc: { endpoint: "0.0.0.0:4317" }
      http: { endpoint: "0.0.0.0:4318" }

processors:
  batch:
    timeout: 5s
    send_batch_size: 1000
  memory_limiter:
    limit_mib: 512

exporters:
  otlphttp/tempo:
    endpoint: http://tempo:4318
  prometheusremotewrite:
    endpoint: http://mimir:9009/api/v1/push
  loki:
    endpoint: http://loki:3100/loki/api/v1/push

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [memory_limiter, batch]
      exporters: [otlphttp/tempo]
    metrics:
      receivers: [otlp]
      processors: [memory_limiter, batch]
      exporters: [prometheusremotewrite]
    logs:
      receivers: [otlp]
      processors: [memory_limiter, batch]
      exporters: [loki]

Grafana Dashboard Provisioning

Create dashboards as JSON files in grafana/dashboards/. Key panels:

Service Overview Dashboard:

• Request rate: sum(rate(http_server_request_duration_seconds_count[5m])) by (service_name)
• P95 latency: histogram_quantile(0.95, sum(rate(http_server_request_duration_seconds_bucket[5m])) by (le, service_name))
• Error rate: sum(rate(http_server_request_duration_seconds_count{http_status_code=~"5.."}[5m])) / sum(rate(http_server_request_duration_seconds_count[5m]))

Alert Rules:

• Error rate > 5 % for 5 min → P2
• P99 latency > 2s for 5 min → P3
• Service health check failing for 1 min → P1

Signal Correlation in Grafana

1. Loki data source: add derived field with regex trace_id=(\w+) linking to Tempo.
2. Tempo data source: enable "Trace to logs" linking to Loki with filter {service_name="$service"} | trace_id="$traceId".
3. Metrics → Logs: use exemplars in Mimir to link metric data points to trace IDs.

Examples

Example 1 — Quick OTel setup for Express app

Input: "Add observability to my Express API."

Output: Create src/instrumentation.ts with the Node SDK config above, add the --require flag to the start script, set OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318 and OTEL_SERVICE_NAME=api-gateway in .env.

Example 2 — Docker Compose for LGTM stack

Input: "Give me a docker-compose for the observability backend."

Output:

services:
  otel-collector:
    image: otel/opentelemetry-collector-contrib:0.96.0
    volumes: ["./otel-collector-config.yaml:/etc/otelcol/config.yaml"]
    ports: ["4317:4317", "4318:4318"]
  tempo:
    image: grafana/tempo:2.4.0
    command: ["-config.file=/etc/tempo.yaml"]
    volumes: ["./tempo.yaml:/etc/tempo.yaml"]
  loki:
    image: grafana/loki:3.0.0
    ports: ["3100:3100"]
  mimir:
    image: grafana/mimir:2.11.0
    command: ["-config.file=/etc/mimir.yaml"]
    volumes: ["./mimir.yaml:/etc/mimir.yaml"]
  grafana:
    image: grafana/grafana:10.4.0
    ports: ["3000:3000"]
    volumes: ["./grafana/provisioning:/etc/grafana/provisioning", "./grafana/dashboards:/var/lib/grafana/dashboards"]
    environment:
      GF_AUTH_ANONYMOUS_ENABLED: "true"
      GF_AUTH_ANONYMOUS_ORG_ROLE: Admin

Guidelines

• Instrument before you optimize. You cannot improve what you cannot see.
• Auto-instrumentation first, custom spans second. Auto-instrumentation covers 80 % of what you need. Add custom spans only for business-critical paths.
• Always correlate signals. A trace without logs is incomplete. A metric spike without a trace is unactionable.
• Set resource limits on the collector. Without memory_limiter, a traffic spike can OOM the collector and create a cascading failure.
• Use service.name consistently. It is the primary grouping key across all three signal types. Mismatched names break correlation.