Agent skill
observability-setup
Implements comprehensive observability with OpenTelemetry tracing, Prometheus metrics, and structured logging. Includes instrumentation plans, sample dashboards, and alert candidates. Use for "observability", "monitoring", "tracing", or "metrics".
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/observability-setup-monkey1sai-openai-cli
SKILL.md
Observability Setup
Implement the three pillars: Traces, Metrics, and Logs.
OpenTelemetry Tracing
// tracing.ts
import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node";
import { Resource } from "@opentelemetry/resources";
import { SemanticResourceAttributes } from "@opentelemetry/semantic-conventions";
import { registerInstrumentations } from "@opentelemetry/instrumentation";
import { HttpInstrumentation } from "@opentelemetry/instrumentation-http";
import { ExpressInstrumentation } from "@opentelemetry/instrumentation-express";
import { PrismaInstrumentation } from "@prisma/instrumentation";
const provider = new NodeTracerProvider({
resource: new Resource({
[SemanticResourceAttributes.SERVICE_NAME]: "my-api",
[SemanticResourceAttributes.SERVICE_VERSION]: "1.0.0",
}),
});
registerInstrumentations({
instrumentations: [
new HttpInstrumentation(),
new ExpressInstrumentation(),
new PrismaInstrumentation(),
],
});
provider.register();
// Custom spans
import { trace } from "@opentelemetry/api";
const tracer = trace.getTracer("my-app");
async function processOrder(orderId: string) {
const span = tracer.startSpan("processOrder");
span.setAttribute("order.id", orderId);
try {
await validateOrder(orderId);
await chargePayment(orderId);
await fulfillOrder(orderId);
span.setStatus({ code: 0 }); // OK
} catch (error) {
span.setStatus({ code: 2, message: error.message }); // ERROR
throw error;
} finally {
span.end();
}
}
Prometheus Metrics
// metrics.ts
import { Registry, Counter, Histogram, Gauge } from "prom-client";
const register = new Registry();
// HTTP request counter
export const httpRequestCounter = new Counter({
name: "http_requests_total",
help: "Total HTTP requests",
labelNames: ["method", "route", "status_code"],
registers: [register],
});
// HTTP request duration
export const httpRequestDuration = new Histogram({
name: "http_request_duration_seconds",
help: "HTTP request duration in seconds",
labelNames: ["method", "route", "status_code"],
buckets: [0.1, 0.5, 1, 2, 5, 10],
registers: [register],
});
// Active connections
export const activeConnections = new Gauge({
name: "active_connections",
help: "Number of active connections",
registers: [register],
});
// Business metrics
export const ordersProcessed = new Counter({
name: "orders_processed_total",
help: "Total orders processed",
labelNames: ["status"],
registers: [register],
});
// Middleware
app.use((req, res, next) => {
const start = Date.now();
res.on("finish", () => {
const duration = (Date.now() - start) / 1000;
const route = req.route?.path || "unknown";
httpRequestCounter.inc({
method: req.method,
route,
status_code: res.statusCode,
});
httpRequestDuration.observe(
{ method: req.method, route, status_code: res.statusCode },
duration
);
});
next();
});
// Metrics endpoint
app.get("/metrics", async (req, res) => {
res.set("Content-Type", register.contentType);
res.end(await register.metrics());
});
Structured Logging
// logger.ts
import pino from "pino";
export const logger = pino({
level: process.env.LOG_LEVEL || "info",
formatters: {
level: (label) => ({ level: label }),
},
base: {
service: "my-api",
environment: process.env.NODE_ENV,
},
});
// Usage
logger.info({ userId: "123", action: "login" }, "User logged in");
logger.error({ err: error, orderId: "456" }, "Order processing failed");
Sample Dashboard (Grafana)
{
"dashboard": {
"title": "API Overview",
"panels": [
{
"title": "Request Rate",
"targets": [{
"expr": "rate(http_requests_total[5m])"
}]
},
{
"title": "Error Rate",
"targets": [{
"expr": "rate(http_requests_total{status_code=~"5.."}[5m])"
}]
},
{
"title": "p95 Latency",
"targets": [{
"expr": "histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))"
}]
},
{
"title": "Active Connections",
"targets": [{
"expr": "active_connections"
}]
}
]
}
}
Alert Candidates
# alerts.yml
groups:
- name: api_alerts
interval: 30s
rules:
- alert: HighErrorRate
expr: rate(http_requests_total{status_code=~"5.."}[5m]) > 0.05
for: 5m
labels:
severity: critical
annotations:
summary: "High error rate detected"
- alert: HighLatency
expr: histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m])) > 2
for: 10m
labels:
severity: warning
annotations:
summary: "p95 latency above 2s"
- alert: LowAvailability
expr: rate(http_requests_total{status_code="200"}[5m]) / rate(http_requests_total[5m]) < 0.95
for: 5m
labels:
severity: critical
annotations:
summary: "Availability below 95%"
Output Checklist
- OpenTelemetry tracing configured
- Prometheus metrics instrumented
- Structured logging implemented
- Sample dashboards created
- Alert rules defined
- Metrics endpoint exposed
- Instrumentation tested ENDFILE
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