Azure Monitor Opentelemetry TS V2

Azure Monitor OpenTelemetry SDK for TypeScript

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/azure-monitor-opentelemetry-ts from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Azure Monitor OpenTelemetry SDK for TypeScript Auto-instrument Node.js applications with distributed tracing, metrics, and logs.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Environment Variables, Quick Start (Auto-Instrumentation), ESM Support (Node.js 18.19+), Full Configuration, Custom Traces, Custom Metrics.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• This skill is applicable to execute the workflow or actions described in the overview.
• Use when the request clearly matches the imported source intent: Auto-instrument Node.js applications with distributed tracing, metrics, and logs.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. bash # 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 ### Trace Exporter typescript import { AzureMonitorTraceExporter } from "@azure/monitor-opentelemetry-exporter"; import { NodeTracerProvider, BatchSpanProcessor } from "@opentelemetry/sdk-trace-node"; const exporter = new AzureMonitorTraceExporter({ connectionString: process.env.APPLICATIONINSIGHTSCONNECTIONSTRING }); const provider = new NodeTracerProvider({ spanProcessors: [new BatchSpanProcessor(exporter)] }); provider.register(); ### Metric Exporter typescript 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.APPLICATIONINSIGHTSCONNECTIONSTRING }); const meterProvider = new MeterProvider({ readers: [new PeriodicExportingMetricReader({ exporter })] }); metrics.setGlobalMeterProvider(meterProvider); ### Log Exporter typescript 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.APPLICATIONINSIGHTSCONNECTIONSTRING }); const loggerProvider = new LoggerProvider(); loggerProvider.addLogRecordProcessor(new BatchLogRecordProcessor(exporter)); logs.setGlobalLoggerProvider(loggerProvider); typescript 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 { return Promise.resolve(); } shutdown(): Promise { 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()] });
2. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
3. Read the overview and provenance files before loading any copied upstream support files.
4. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
5. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
6. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
7. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.

Imported Workflow Notes

Imported: 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

Imported: 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);

Imported: 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()]
});

Imported: Environment Variables

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

Examples

Example 1: Ask for the upstream workflow directly

Use @azure-monitor-opentelemetry-ts-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @azure-monitor-opentelemetry-ts-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @azure-monitor-opentelemetry-ts-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @azure-monitor-opentelemetry-ts-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• 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
• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.

Imported Operating Notes

Imported: Best Practices

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

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/azure-monitor-opentelemetry-ts, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: 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();

Imported: 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"
  }
}

Imported: 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);

Imported: 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();
}

Imported: 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" });
});

Imported: 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);
    }
  }
}

Imported: 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 });

Imported: Shutdown

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

useAzureMonitor();

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

Imported: 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";

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.