Adoption

Agent Skills are supported by leading AI development tools.

VS Code Gemini CLI GitHub Goose Amp Cursor Claude Code Letta OpenCode Claude OpenAI Codex Factory VS Code Gemini CLI GitHub Goose Amp Cursor Claude Code Letta OpenCode Claude OpenAI Codex Factory

microsoft/azure-monitor-opentelemetry-ts

Name: azure-monitor-opentelemetry-ts
Author: microsoft

.github/plugins/azure-sdk-typescript/skills/azure-monitor-opentelemetry-ts/SKILL.md

npx skillsauth add microsoft/skills azure-monitor-opentelemetry-ts

Clean

TrivyContainer and dependency vulnerability scanner

Clean

SemgrepStatic code analysis for vulnerabilities

Clean

mcp-scan (Snyk)Model Context Protocol security validation

Skipped

Snyk (dep)Open source security scanning

Skipped

Socket.devSupply chain security analysis

Skipped

VirusTotalMulti-engine malware detection

Skipped

CrowdStrikeAdvanced threat intelligence

Skipped

OSV-ScannerOpen Source Vulnerability database check

Skipped

OWASP Dep-Check

Azure Monitor OpenTelemetry SDK for TypeScript

Auto-instrument Node.js applications with distributed tracing, metrics, and logs.

Installation

# Distro (recommended - auto-instrumentation)
npm install @azure/monitor-opentelemetry

# Low-level exporters (custom OpenTelemetry setup)
npm install @azure/monitor-opentelemetry-exporter

# Custom logs ingestion
npm install @azure/monitor-ingestion

Environment Variables

APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=...;IngestionEndpoint=...

Quick Start (Auto-Instrumentation)

IMPORTANT: Call useAzureMonitor() BEFORE importing other modules.

import { useAzureMonitor } from "@azure/monitor-opentelemetry";

useAzureMonitor({
  azureMonitorExporterOptions: {
    connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
  }
});

// Now import your application
import express from "express";
const app = express();

ESM Support (Node.js 18.19+)

node --import @azure/monitor-opentelemetry/loader ./dist/index.js

package.json:

{
  "scripts": {
    "start": "node --import @azure/monitor-opentelemetry/loader ./dist/index.js"
  }
}

Full Configuration

import { useAzureMonitor, AzureMonitorOpenTelemetryOptions } from "@azure/monitor-opentelemetry";
import { resourceFromAttributes } from "@opentelemetry/resources";

const options: AzureMonitorOpenTelemetryOptions = {
  azureMonitorExporterOptions: {
    connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING,
    storageDirectory: "/path/to/offline/storage",
    disableOfflineStorage: false
  },
  
  // Sampling
  samplingRatio: 1.0,  // 0-1, percentage of traces
  
  // Features
  enableLiveMetrics: true,
  enableStandardMetrics: true,
  enablePerformanceCounters: true,
  
  // Instrumentation libraries
  instrumentationOptions: {
    azureSdk: { enabled: true },
    http: { enabled: true },
    mongoDb: { enabled: true },
    mySql: { enabled: true },
    postgreSql: { enabled: true },
    redis: { enabled: true },
    bunyan: { enabled: false },
    winston: { enabled: false }
  },
  
  // Custom resource
  resource: resourceFromAttributes({ "service.name": "my-service" })
};

useAzureMonitor(options);

Custom Traces

import { trace } from "@opentelemetry/api";

const tracer = trace.getTracer("my-tracer");

const span = tracer.startSpan("doWork");
try {
  span.setAttribute("component", "worker");
  span.setAttribute("operation.id", "42");
  span.addEvent("processing started");
  
  // Your work here
  
} catch (error) {
  span.recordException(error as Error);
  span.setStatus({ code: 2, message: (error as Error).message });
} finally {
  span.end();
}

Custom Metrics

import { metrics } from "@opentelemetry/api";

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

// Counter
const counter = meter.createCounter("requests_total");
counter.add(1, { route: "/api/users", method: "GET" });

// Histogram
const histogram = meter.createHistogram("request_duration_ms");
histogram.record(150, { route: "/api/users" });

// Observable Gauge
const gauge = meter.createObservableGauge("active_connections");
gauge.addCallback((result) => {
  result.observe(getActiveConnections(), { pool: "main" });
});

Manual Exporter Setup

Trace Exporter

import { AzureMonitorTraceExporter } from "@azure/monitor-opentelemetry-exporter";
import { NodeTracerProvider, BatchSpanProcessor } from "@opentelemetry/sdk-trace-node";

const exporter = new AzureMonitorTraceExporter({
  connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});

const provider = new NodeTracerProvider({
  spanProcessors: [new BatchSpanProcessor(exporter)]
});

provider.register();

Metric Exporter

import { AzureMonitorMetricExporter } from "@azure/monitor-opentelemetry-exporter";
import { PeriodicExportingMetricReader, MeterProvider } from "@opentelemetry/sdk-metrics";
import { metrics } from "@opentelemetry/api";

const exporter = new AzureMonitorMetricExporter({
  connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});

const meterProvider = new MeterProvider({
  readers: [new PeriodicExportingMetricReader({ exporter })]
});

metrics.setGlobalMeterProvider(meterProvider);

Log Exporter

import { AzureMonitorLogExporter } from "@azure/monitor-opentelemetry-exporter";
import { BatchLogRecordProcessor, LoggerProvider } from "@opentelemetry/sdk-logs";
import { logs } from "@opentelemetry/api-logs";

const exporter = new AzureMonitorLogExporter({
  connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});

const loggerProvider = new LoggerProvider();
loggerProvider.addLogRecordProcessor(new BatchLogRecordProcessor(exporter));

logs.setGlobalLoggerProvider(loggerProvider);

Custom Logs Ingestion

import { DefaultAzureCredential } from "@azure/identity";
import { LogsIngestionClient, isAggregateLogsUploadError } from "@azure/monitor-ingestion";

const endpoint = "https://<dce>.ingest.monitor.azure.com";
const ruleId = "<data-collection-rule-id>";
const streamName = "Custom-MyTable_CL";

const client = new LogsIngestionClient(endpoint, new DefaultAzureCredential());

const logs = [
  {
    Time: new Date().toISOString(),
    Computer: "Server1",
    Message: "Application started",
    Level: "Information"
  }
];

try {
  await client.upload(ruleId, streamName, logs);
} catch (error) {
  if (isAggregateLogsUploadError(error)) {
    for (const uploadError of error.errors) {
      console.error("Failed logs:", uploadError.failedLogs);
    }
  }
}

Custom Span Processor

import { SpanProcessor, ReadableSpan } from "@opentelemetry/sdk-trace-base";
import { Span, Context, SpanKind, TraceFlags } from "@opentelemetry/api";
import { useAzureMonitor } from "@azure/monitor-opentelemetry";

class FilteringSpanProcessor implements SpanProcessor {
  forceFlush(): Promise<void> { return Promise.resolve(); }
  shutdown(): Promise<void> { return Promise.resolve(); }
  onStart(span: Span, context: Context): void {}
  
  onEnd(span: ReadableSpan): void {
    // Add custom attributes
    span.attributes["CustomDimension"] = "value";
    
    // Filter out internal spans
    if (span.kind === SpanKind.INTERNAL) {
      span.spanContext().traceFlags = TraceFlags.NONE;
    }
  }
}

useAzureMonitor({
  spanProcessors: [new FilteringSpanProcessor()]
});

Sampling

import { ApplicationInsightsSampler } from "@azure/monitor-opentelemetry-exporter";
import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node";

// Sample 75% of traces
const sampler = new ApplicationInsightsSampler(0.75);

const provider = new NodeTracerProvider({ sampler });

Shutdown

import { useAzureMonitor, shutdownAzureMonitor } from "@azure/monitor-opentelemetry";

useAzureMonitor();

// On application shutdown
process.on("SIGTERM", async () => {
  await shutdownAzureMonitor();
  process.exit(0);
});

Key Types

import {
  useAzureMonitor,
  shutdownAzureMonitor,
  AzureMonitorOpenTelemetryOptions,
  InstrumentationOptions
} from "@azure/monitor-opentelemetry";

import {
  AzureMonitorTraceExporter,
  AzureMonitorMetricExporter,
  AzureMonitorLogExporter,
  ApplicationInsightsSampler,
  AzureMonitorExporterOptions
} from "@azure/monitor-opentelemetry-exporter";

import {
  LogsIngestionClient,
  isAggregateLogsUploadError
} from "@azure/monitor-ingestion";

Best Practices

Call useAzureMonitor() first - Before importing other modules
Use ESM loader for ESM projects - --import @azure/monitor-opentelemetry/loader
Enable offline storage - For reliable telemetry in disconnected scenarios
Set sampling ratio - For high-traffic applications
Add custom dimensions - Use span processors for enrichment
Graceful shutdown - Call shutdownAzureMonitor() to flush telemetry

microsoft/azure-monitor-opentelemetry-ts

.github/plugins/azure-sdk-typescript/skills/azure-monitor-opentelemetry-ts/SKILL.md

Instrument applications with Azure Monitor and OpenTelemetry for JavaScript (@azure/monitor-opentelemetry). Use when adding distributed tracing, metrics, and logs to Node.js applications with Application Insights.

development

Updated Apr 9, 2026

$ install --global

skillsauth

npx skillsauth add microsoft/skills azure-monitor-opentelemetry-ts

Install this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.

Security Scan Results

3 of 9 scanners reported clean

Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.

Scanners Passed

Scanners in report

Clean

TrivyContainer and dependency vulnerability scanner

95%

Clean

SemgrepStatic code analysis for vulnerabilities

95%

Clean

mcp-scan (Snyk)Model Context Protocol security validation

95%

Skipped

Snyk (dep)Open source security scanning

50%

Skipped

Socket.devSupply chain security analysis

50%

Skipped

VirusTotalMulti-engine malware detection

50%

Skipped

CrowdStrikeAdvanced threat intelligence

50%

Skipped

OSV-ScannerOpen Source Vulnerability database check

50%

Skipped

OWASP Dep-Check

50%

Last scanned: Apr 9, 2026, 3:08 AM5.2s2 files scanned

SKILL.md

name:: azure-monitor-opentelemetry-ts
description:: Instrument applications with Azure Monitor and OpenTelemetry for JavaScript (@azure/monitor-opentelemetry). Use when adding distributed tracing, metrics, and logs to Node.js applications with Application Insights.
license:: MIT
author:: Microsoft
version:: 1.0.0
package:: @azure/monitor-opentelemetry

Azure Monitor OpenTelemetry SDK for TypeScript

Auto-instrument Node.js applications with distributed tracing, metrics, and logs.

Installation

# Distro (recommended - auto-instrumentation)
npm install @azure/monitor-opentelemetry

# Low-level exporters (custom OpenTelemetry setup)
npm install @azure/monitor-opentelemetry-exporter

# Custom logs ingestion
npm install @azure/monitor-ingestion

Environment Variables

APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=...;IngestionEndpoint=...

Quick Start (Auto-Instrumentation)

IMPORTANT: Call useAzureMonitor() BEFORE importing other modules.

import { useAzureMonitor } from "@azure/monitor-opentelemetry";

useAzureMonitor({
  azureMonitorExporterOptions: {
    connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
  }
});

// Now import your application
import express from "express";
const app = express();

ESM Support (Node.js 18.19+)

node --import @azure/monitor-opentelemetry/loader ./dist/index.js

package.json:

{
  "scripts": {
    "start": "node --import @azure/monitor-opentelemetry/loader ./dist/index.js"
  }
}

Full Configuration

import { useAzureMonitor, AzureMonitorOpenTelemetryOptions } from "@azure/monitor-opentelemetry";
import { resourceFromAttributes } from "@opentelemetry/resources";

const options: AzureMonitorOpenTelemetryOptions = {
  azureMonitorExporterOptions: {
    connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING,
    storageDirectory: "/path/to/offline/storage",
    disableOfflineStorage: false
  },
  
  // Sampling
  samplingRatio: 1.0,  // 0-1, percentage of traces
  
  // Features
  enableLiveMetrics: true,
  enableStandardMetrics: true,
  enablePerformanceCounters: true,
  
  // Instrumentation libraries
  instrumentationOptions: {
    azureSdk: { enabled: true },
    http: { enabled: true },
    mongoDb: { enabled: true },
    mySql: { enabled: true },
    postgreSql: { enabled: true },
    redis: { enabled: true },
    bunyan: { enabled: false },
    winston: { enabled: false }
  },
  
  // Custom resource
  resource: resourceFromAttributes({ "service.name": "my-service" })
};

useAzureMonitor(options);

Custom Traces

import { trace } from "@opentelemetry/api";

const tracer = trace.getTracer("my-tracer");

const span = tracer.startSpan("doWork");
try {
  span.setAttribute("component", "worker");
  span.setAttribute("operation.id", "42");
  span.addEvent("processing started");
  
  // Your work here
  
} catch (error) {
  span.recordException(error as Error);
  span.setStatus({ code: 2, message: (error as Error).message });
} finally {
  span.end();
}

Custom Metrics

import { metrics } from "@opentelemetry/api";

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

// Counter
const counter = meter.createCounter("requests_total");
counter.add(1, { route: "/api/users", method: "GET" });

// Histogram
const histogram = meter.createHistogram("request_duration_ms");
histogram.record(150, { route: "/api/users" });

// Observable Gauge
const gauge = meter.createObservableGauge("active_connections");
gauge.addCallback((result) => {
  result.observe(getActiveConnections(), { pool: "main" });
});

Manual Exporter Setup

Trace Exporter

import { AzureMonitorTraceExporter } from "@azure/monitor-opentelemetry-exporter";
import { NodeTracerProvider, BatchSpanProcessor } from "@opentelemetry/sdk-trace-node";

const exporter = new AzureMonitorTraceExporter({
  connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});

const provider = new NodeTracerProvider({
  spanProcessors: [new BatchSpanProcessor(exporter)]
});

provider.register();

Metric Exporter

import { AzureMonitorMetricExporter } from "@azure/monitor-opentelemetry-exporter";
import { PeriodicExportingMetricReader, MeterProvider } from "@opentelemetry/sdk-metrics";
import { metrics } from "@opentelemetry/api";

const exporter = new AzureMonitorMetricExporter({
  connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});

const meterProvider = new MeterProvider({
  readers: [new PeriodicExportingMetricReader({ exporter })]
});

metrics.setGlobalMeterProvider(meterProvider);

Log Exporter

import { AzureMonitorLogExporter } from "@azure/monitor-opentelemetry-exporter";
import { BatchLogRecordProcessor, LoggerProvider } from "@opentelemetry/sdk-logs";
import { logs } from "@opentelemetry/api-logs";

const exporter = new AzureMonitorLogExporter({
  connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
});

const loggerProvider = new LoggerProvider();
loggerProvider.addLogRecordProcessor(new BatchLogRecordProcessor(exporter));

logs.setGlobalLoggerProvider(loggerProvider);

Custom Logs Ingestion

import { DefaultAzureCredential } from "@azure/identity";
import { LogsIngestionClient, isAggregateLogsUploadError } from "@azure/monitor-ingestion";

const endpoint = "https://<dce>.ingest.monitor.azure.com";
const ruleId = "<data-collection-rule-id>";
const streamName = "Custom-MyTable_CL";

const client = new LogsIngestionClient(endpoint, new DefaultAzureCredential());

const logs = [
  {
    Time: new Date().toISOString(),
    Computer: "Server1",
    Message: "Application started",
    Level: "Information"
  }
];

try {
  await client.upload(ruleId, streamName, logs);
} catch (error) {
  if (isAggregateLogsUploadError(error)) {
    for (const uploadError of error.errors) {
      console.error("Failed logs:", uploadError.failedLogs);
    }
  }
}

Custom Span Processor

import { SpanProcessor, ReadableSpan } from "@opentelemetry/sdk-trace-base";
import { Span, Context, SpanKind, TraceFlags } from "@opentelemetry/api";
import { useAzureMonitor } from "@azure/monitor-opentelemetry";

class FilteringSpanProcessor implements SpanProcessor {
  forceFlush(): Promise<void> { return Promise.resolve(); }
  shutdown(): Promise<void> { return Promise.resolve(); }
  onStart(span: Span, context: Context): void {}
  
  onEnd(span: ReadableSpan): void {
    // Add custom attributes
    span.attributes["CustomDimension"] = "value";
    
    // Filter out internal spans
    if (span.kind === SpanKind.INTERNAL) {
      span.spanContext().traceFlags = TraceFlags.NONE;
    }
  }
}

useAzureMonitor({
  spanProcessors: [new FilteringSpanProcessor()]
});

Sampling

import { ApplicationInsightsSampler } from "@azure/monitor-opentelemetry-exporter";
import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node";

// Sample 75% of traces
const sampler = new ApplicationInsightsSampler(0.75);

const provider = new NodeTracerProvider({ sampler });

Shutdown

import { useAzureMonitor, shutdownAzureMonitor } from "@azure/monitor-opentelemetry";

useAzureMonitor();

// On application shutdown
process.on("SIGTERM", async () => {
  await shutdownAzureMonitor();
  process.exit(0);
});

Key Types

import {
  useAzureMonitor,
  shutdownAzureMonitor,
  AzureMonitorOpenTelemetryOptions,
  InstrumentationOptions
} from "@azure/monitor-opentelemetry";

import {
  AzureMonitorTraceExporter,
  AzureMonitorMetricExporter,
  AzureMonitorLogExporter,
  ApplicationInsightsSampler,
  AzureMonitorExporterOptions
} from "@azure/monitor-opentelemetry-exporter";

import {
  LogsIngestionClient,
  isAggregateLogsUploadError
} from "@azure/monitor-ingestion";

Best Practices

Call useAzureMonitor() first - Before importing other modules
Use ESM loader for ESM projects - --import @azure/monitor-opentelemetry/loader
Enable offline storage - For reliable telemetry in disconnected scenarios
Set sampling ratio - For high-traffic applications
Add custom dimensions - Use span processors for enrichment
Graceful shutdown - Call shutdownAzureMonitor() to flush telemetry

Related Skills

microsoft/kql

tools

VerifiedTrustedOfficial

KQL language expertise for writing correct, efficient Kusto Query Language queries. Covers syntax gotchas, join patterns, dynamic types, datetime pitfalls, regex patterns, serialization, memory management, result-size discipline, and advanced functions (geo, vector, graph). USE THIS SKILL whenever writing, debugging, or reviewing KQL queries — even simple ones — because the gotchas section prevents the most common errors that waste tool calls and cause expensive retry cascades. Trigger on: KQL, Kusto, ADX, Azure Data Explorer, Fabric Real-Time Intelligence, EventHouse, Log Analytics, log analysis, data exploration, time series, anomaly detection, summarize, where clause, join, extend, project, let statement, parse operator, extract function, any mention of pipe-forward query syntax.

2,074SKILL.mdUpdated Apr 15, 2026

microsoft/microsoft-foundry

development

VerifiedTrustedOfficial

Deploy, evaluate, and manage Foundry agents end-to-end: Docker build, ACR push, hosted/prompt agent create, container start, batch eval, prompt optimization, prompt optimizer workflows, agent.yaml, dataset curation from traces. USE FOR: deploy agent to Foundry, hosted agent, create agent, invoke agent, evaluate agent, run batch eval, optimize prompt, improve prompt, prompt optimization, prompt optimizer, improve agent instructions, optimize agent instructions, optimize system prompt, deploy model, Foundry project, RBAC, role assignment, permissions, quota, capacity, region, troubleshoot agent, deployment failure, create dataset from traces, dataset versioning, eval trending, create AI Services, Cognitive Services, create Foundry resource, provision resource, knowledge index, agent monitoring, customize deployment, onboard, availability. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).

2,074SKILL.mdUpdated Mar 22, 2026

microsoft/microsoft-foundry

microsoft/azure-validate

testing

VerifiedTrustedOfficial

Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity permissions, and prerequisites before deploying. WHEN: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors, validate Azure Functions, validate function app, validate serverless deployment, verify RBAC roles, check role assignments, review managed identity permissions, what-if analysis, validate Container Apps deployment.

2,074SKILL.mdUpdated Mar 22, 2026

microsoft/azure-validate

microsoft/azure-quotas

testing

VerifiedTrustedOfficial

Check/manage Azure quotas and usage across providers. For deployment planning, capacity validation, region selection. WHEN: "check quotas", "service limits", "current usage", "request quota increase", "quota exceeded", "validate capacity", "regional availability", "provisioning limits", "vCPU limit", "how many vCPUs available in my subscription".

2,074SKILL.mdUpdated Mar 22, 2026

microsoft/azure-quotas

Download

For Claude Desktop. Download once, then upload the file in the app — no terminal needed.

Need help? View full Cowork setup guide →

Install manually

Choose your platform

# Clone the repo
git clone https://github.com/microsoft/skills.git

# Copy into Claude Code skills folder (global)
cp -r skills/.github/plugins/azure-sdk-typescript/skills/azure-monitor-opentelemetry-ts ~/.claude/skills/

Claude Code Skills — official skills path docs.

Repository

microsoft/skills

Compatible with

Claude Code

OpenAI Codex CLI

ChatGPT