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signoz

Expert guidance for SigNoz, the open-source observability platform that provides traces, metrics, and logs in a single UI. Built natively on OpenTelemetry, SigNoz is a self-hosted alternative to Datadog and New Relic. Helps developers set up distributed tracing, application performance monitoring, log management, and custom dashboards.

#observability#apm#traces#metrics#logs
terminal-skillsv1.0.0
Works with:claude-codeopenai-codexgemini-clicursor
Source

Usage

$
✓ Installed signoz v1.0.0

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"

Documentation

Overview

SigNoz, the open-source observability platform that provides traces, metrics, and logs in a single UI. Built natively on OpenTelemetry, SigNoz is a self-hosted alternative to Datadog and New Relic. Helps developers set up distributed tracing, application performance monitoring, log management, and custom dashboards.

Instructions

Deployment

bash
# Docker Compose (quickstart)
git clone -b main https://github.com/SigNoz/signoz.git
cd signoz/deploy
docker compose -f docker/clickhouse-setup/docker-compose.yaml up -d

# SigNoz UI at http://localhost:3301
# OTel Collector at localhost:4317 (gRPC) / localhost:4318 (HTTP)

Instrument a Node.js Application

typescript
// tracing.ts — OpenTelemetry auto-instrumentation for SigNoz
// Import this file BEFORE any other imports in your app entry point.

import { NodeSDK } from "@opentelemetry/sdk-node";
import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-http";
import { OTLPMetricExporter } from "@opentelemetry/exporter-metrics-otlp-http";
import { getNodeAutoInstrumentations } from "@opentelemetry/auto-instrumentations-node";
import { PeriodicExportingMetricReader } from "@opentelemetry/sdk-metrics";
import { Resource } from "@opentelemetry/resources";
import { ATTR_SERVICE_NAME, ATTR_SERVICE_VERSION } from "@opentelemetry/semantic-conventions";

const sdk = new NodeSDK({
  resource: new Resource({
    [ATTR_SERVICE_NAME]: "api-gateway",
    [ATTR_SERVICE_VERSION]: "1.4.2",
    "deployment.environment": process.env.NODE_ENV ?? "development",
  }),
  traceExporter: new OTLPTraceExporter({
    url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT ?? "http://localhost:4318/v1/traces",
  }),
  metricReader: new PeriodicExportingMetricReader({
    exporter: new OTLPMetricExporter({
      url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT ?? "http://localhost:4318/v1/metrics",
    }),
    exportIntervalMillis: 30000,       // Export metrics every 30s
  }),
  instrumentations: [
    getNodeAutoInstrumentations({
      // Auto-instruments: HTTP, Express, pg, mysql, redis, MongoDB, gRPC
      "@opentelemetry/instrumentation-fs": { enabled: false },  // Too noisy
    }),
  ],
});

sdk.start();

// Graceful shutdown
process.on("SIGTERM", () => sdk.shutdown());

Custom Spans and Attributes

typescript
// src/services/order-service.ts — Add business context to traces
import { trace, SpanStatusCode, context } from "@opentelemetry/api";

const tracer = trace.getTracer("order-service");

async function processOrder(orderId: string, userId: string) {
  // Create a span for the entire order processing
  return tracer.startActiveSpan("process-order", async (span) => {
    // Add business attributes — visible in SigNoz trace details
    span.setAttribute("order.id", orderId);
    span.setAttribute("user.id", userId);

    try {
      // Child span for payment
      const paymentResult = await tracer.startActiveSpan("charge-payment", async (paymentSpan) => {
        paymentSpan.setAttribute("payment.method", "stripe");
        const result = await stripe.charges.create({ amount: order.total, currency: "usd" });
        paymentSpan.setAttribute("payment.charge_id", result.id);
        paymentSpan.end();
        return result;
      });

      // Child span for inventory
      await tracer.startActiveSpan("update-inventory", async (inventorySpan) => {
        inventorySpan.setAttribute("items.count", order.items.length);
        await inventoryService.reserve(order.items);
        inventorySpan.end();
      });

      // Child span for notification
      await tracer.startActiveSpan("send-confirmation", async (notifSpan) => {
        await emailService.sendOrderConfirmation(userId, orderId);
        notifSpan.end();
      });

      span.setAttribute("order.status", "completed");
      span.setStatus({ code: SpanStatusCode.OK });
    } catch (error) {
      span.setStatus({ code: SpanStatusCode.ERROR, message: error.message });
      span.recordException(error);
      throw error;
    } finally {
      span.end();
    }
  });
}

Custom Metrics

typescript
// src/metrics/business-metrics.ts — Track business KPIs in SigNoz
import { metrics } from "@opentelemetry/api";

const meter = metrics.getMeter("business-metrics");

// Counter — total orders processed
const ordersProcessed = meter.createCounter("orders.processed", {
  description: "Total number of orders processed",
  unit: "orders",
});

// Histogram — order value distribution
const orderValue = meter.createHistogram("orders.value", {
  description: "Order value in cents",
  unit: "cents",
});

// Up/down counter — active users
const activeUsers = meter.createUpDownCounter("users.active", {
  description: "Currently active users",
});

// Usage
function onOrderCompleted(order: Order) {
  ordersProcessed.add(1, {
    "order.plan": order.plan,
    "order.region": order.region,
  });
  orderValue.record(order.totalCents, {
    "order.plan": order.plan,
  });
}

Structured Logging

typescript
// src/lib/logger.ts — Logs that correlate with traces in SigNoz
import pino from "pino";
import { context, trace } from "@opentelemetry/api";

const logger = pino({
  mixin() {
    // Inject trace context into every log line
    // SigNoz correlates logs with traces using these fields
    const span = trace.getSpan(context.active());
    if (span) {
      const spanContext = span.spanContext();
      return {
        trace_id: spanContext.traceId,
        span_id: spanContext.spanId,
        trace_flags: `0${spanContext.traceFlags.toString(16)}`,
      };
    }
    return {};
  },
  transport: {
    target: "pino-opentelemetry-transport",
    options: {
      resourceAttributes: { "service.name": "api-gateway" },
      logRecordProcessorOptions: [{
        exporterOptions: {
          protocol: "http",
          httpExporterPath: "/v1/logs",
          hostname: "localhost",
          port: 4318,
        },
      }],
    },
  },
});

export default logger;

Alerts

yaml
# SigNoz supports alerting on any metric or trace-based condition.
# Configure via the SigNoz UI under Settings → Alerts

# Example alert rules:
# 1. P99 latency > 2s on /api/checkout endpoint
# 2. Error rate > 5% on any service in the last 5 minutes
# 3. Orders processed = 0 for 10 minutes (business metric)
# 4. CPU usage > 80% for 5 minutes

# Notification channels: Slack, PagerDuty, webhook, email, MS Teams, Opsgenie

Installation

bash
# Self-hosted (Docker Compose)
git clone https://github.com/SigNoz/signoz.git
cd signoz/deploy && docker compose up -d

# Helm (Kubernetes)
helm repo add signoz https://charts.signoz.io
helm install signoz signoz/signoz -n observability --create-namespace

# SigNoz Cloud (managed)
# https://signoz.io/teams/

# Client instrumentation
npm install @opentelemetry/sdk-node @opentelemetry/auto-instrumentations-node
npm install @opentelemetry/exporter-trace-otlp-http @opentelemetry/exporter-metrics-otlp-http

Examples

Example 1: Setting up Signoz for a microservices project

User request:

I have a Node.js API and a React frontend running in Docker. Set up Signoz for monitoring/deployment.

The agent creates the necessary configuration files based on patterns like # Docker Compose (quickstart), sets up the integration with the existing Docker setup, configures appropriate defaults for a Node.js + React stack, and provides verification commands to confirm everything is working.

Example 2: Troubleshooting instrument a node.js application issues

User request:

Signoz is showing errors in our instrument a node.js application. Here are the logs: [error output]

The agent analyzes the error output, identifies the root cause by cross-referencing with common Signoz issues, applies the fix (updating configuration, adjusting resource limits, or correcting syntax), and verifies the resolution with appropriate health checks.

Guidelines

  1. OpenTelemetry native — SigNoz uses OTel as the standard; instrument with OTel SDKs and switch between SigNoz/Datadog/Jaeger without code changes
  2. Auto-instrumentation first — Start with auto-instrumentation packages; add custom spans only for business-critical paths
  3. Correlate logs, traces, metrics — Inject trace_id into logs; SigNoz links them together in the UI for root cause analysis
  4. Business metrics — Track revenue, orders, signups as OTel metrics; monitor them alongside infrastructure metrics
  5. Tail-based sampling — For high-traffic services, configure tail-based sampling in the OTel Collector to keep errors and slow traces
  6. ClickHouse storage — SigNoz uses ClickHouse for storage; tune retention policies based on your data volume
  7. Dashboard per service — Create a SigNoz dashboard for each service with RED metrics (Rate, Errors, Duration)
  8. Self-host for cost — SigNoz on your infrastructure costs 5-10x less than Datadog/New Relic for the same data volume

Information

Version
1.0.0
Author
terminal-skills
Category
DevOps
License
Apache-2.0