Azure Monitor OpenTelemetry Exporter for Java

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/azure-monitor-opentelemetry-exporter-java

https://github.com/sickn33/antigravity-awesome-skills

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

metadata.json

ORIGIN.md

Azure Monitor OpenTelemetry Exporter for Java > ⚠️ DEPRECATION NOTICE: This package is deprecated. Migrate to azure-monitor-opentelemetry-autoconfigure. > > See Migration Guide for detailed instructions. Export OpenTelemetry telemetry data to Azure Monitor / Application Insights.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Recommended: Use Autoconfigure Instead, Environment Variables, Creating Spans, Adding Span Attributes, Nested Spans, Recording Exceptions.

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: Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights.
• 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

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

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. xml <dependency> <groupId>com.azure</groupId> <artifactId>azure-monitor-opentelemetry-exporter</artifactId> <version>1.0.0-beta.x</version> </dependency> ### Using Environment Variable java import io.opentelemetry.sdk.autoconfigure.AutoConfiguredOpenTelemetrySdk; import io.opentelemetry.sdk.autoconfigure.AutoConfiguredOpenTelemetrySdkBuilder; import io.opentelemetry.api.OpenTelemetry; import com.azure.monitor.opentelemetry.exporter.AzureMonitorExporter; // Connection string from APPLICATIONINSIGHTSCONNECTIONSTRING env var AutoConfiguredOpenTelemetrySdkBuilder sdkBuilder = AutoConfiguredOpenTelemetrySdk.builder(); AzureMonitorExporter.customize(sdkBuilder); OpenTelemetry openTelemetry = sdkBuilder.build().getOpenTelemetrySdk(); ### With Explicit Connection String java AutoConfiguredOpenTelemetrySdkBuilder sdkBuilder = AutoConfiguredOpenTelemetrySdk.builder(); AzureMonitorExporter.customize(sdkBuilder, "{connection-string}"); OpenTelemetry openTelemetry = sdkBuilder.build().getOpenTelemetrySdk(); java import io.opentelemetry.sdk.trace.SpanProcessor; import io.opentelemetry.sdk.trace.ReadWriteSpan; import io.opentelemetry.sdk.trace.ReadableSpan; import io.opentelemetry.context.Context; private static final AttributeKey<String> CUSTOMATTR = AttributeKey.stringKey("custom.attribute"); SpanProcessor customProcessor = new SpanProcessor() { @Override public void onStart(Context context, ReadWriteSpan span) { // Add custom attribute to every span span.setAttribute(CUSTOMATTR, "customValue"); } @Override public boolean isStartRequired() { return true; } @Override public void onEnd(ReadableSpan span) { // Post-processing if needed } @Override public boolean isEndRequired() { return false; } }; // Register processor AutoConfiguredOpenTelemetrySdkBuilder sdkBuilder = AutoConfiguredOpenTelemetrySdk.builder(); AzureMonitorExporter.customize(sdkBuilder); sdkBuilder.addTracerProviderCustomizer( (sdkTracerProviderBuilder, configProperties) -> sdkTracerProviderBuilder.addSpanProcessor(customProcessor) ); OpenTelemetry openTelemetry = sdkBuilder.build().getOpenTelemetrySdk();
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 (Deprecated)

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-monitor-opentelemetry-exporter</artifactId>
    <version>1.0.0-beta.x</version>
</dependency>

Imported: Basic Setup with Autoconfigure (Recommended)

Using Environment Variable

import io.opentelemetry.sdk.autoconfigure.AutoConfiguredOpenTelemetrySdk;
import io.opentelemetry.sdk.autoconfigure.AutoConfiguredOpenTelemetrySdkBuilder;
import io.opentelemetry.api.OpenTelemetry;
import com.azure.monitor.opentelemetry.exporter.AzureMonitorExporter;

// Connection string from APPLICATIONINSIGHTS_CONNECTION_STRING env var
AutoConfiguredOpenTelemetrySdkBuilder sdkBuilder = AutoConfiguredOpenTelemetrySdk.builder();
AzureMonitorExporter.customize(sdkBuilder);
OpenTelemetry openTelemetry = sdkBuilder.build().getOpenTelemetrySdk();

With Explicit Connection String

AutoConfiguredOpenTelemetrySdkBuilder sdkBuilder = AutoConfiguredOpenTelemetrySdk.builder();
AzureMonitorExporter.customize(sdkBuilder, "{connection-string}");
OpenTelemetry openTelemetry = sdkBuilder.build().getOpenTelemetrySdk();

Imported: Custom Span Processor

import io.opentelemetry.sdk.trace.SpanProcessor;
import io.opentelemetry.sdk.trace.ReadWriteSpan;
import io.opentelemetry.sdk.trace.ReadableSpan;
import io.opentelemetry.context.Context;

private static final AttributeKey<String> CUSTOM_ATTR = AttributeKey.stringKey("custom.attribute");

SpanProcessor customProcessor = new SpanProcessor() {
    @Override
    public void onStart(Context context, ReadWriteSpan span) {
        // Add custom attribute to every span
        span.setAttribute(CUSTOM_ATTR, "customValue");
    }

    @Override
    public boolean isStartRequired() {
        return true;
    }

    @Override
    public void onEnd(ReadableSpan span) {
        // Post-processing if needed
    }

    @Override
    public boolean isEndRequired() {
        return false;
    }
};

// Register processor
AutoConfiguredOpenTelemetrySdkBuilder sdkBuilder = AutoConfiguredOpenTelemetrySdk.builder();
AzureMonitorExporter.customize(sdkBuilder);

sdkBuilder.addTracerProviderCustomizer(
    (sdkTracerProviderBuilder, configProperties) -> 
        sdkTracerProviderBuilder.addSpanProcessor(customProcessor)
);

OpenTelemetry openTelemetry = sdkBuilder.build().getOpenTelemetrySdk();

Imported: Recommended: Use Autoconfigure Instead

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-monitor-opentelemetry-autoconfigure</artifactId>
    <version>LATEST</version>
</dependency>

Examples

Example 1: Ask for the upstream workflow directly

Use @azure-monitor-opentelemetry-exporter-java 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-exporter-java 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-exporter-java 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-exporter-java 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.

• Use autoconfigure — Migrate to azure-monitor-opentelemetry-autoconfigure
• Set meaningful span names — Use descriptive operation names
• Add relevant attributes — Include contextual data for debugging
• Handle exceptions — Always record exceptions on spans
• Use semantic conventions — Follow OpenTelemetry semantic conventions
• End spans in finally — Ensure spans are always ended
• Use try-with-resources — Scope management with try-with-resources pattern

Imported Operating Notes

Imported: Best Practices

1. Use autoconfigure — Migrate to

   azure-monitor-opentelemetry-autoconfigure

2. Set meaningful span names — Use descriptive operation names
3. Add relevant attributes — Include contextual data for debugging
4. Handle exceptions — Always record exceptions on spans
5. Use semantic conventions — Follow OpenTelemetry semantic conventions
6. End spans in finally — Ensure spans are always ended
7. Use try-with-resources — Scope management with try-with-resources pattern

Troubleshooting

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

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/azure-monitor-opentelemetry-exporter-java

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

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

• @azure-mgmt-apicenter-py

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @azure-mgmt-apimanagement-dotnet

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @azure-mgmt-apimanagement-py

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @azure-mgmt-applicationinsights-dotnet

  - 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.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Reference Links

Imported: Environment Variables

APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/

Imported: Creating Spans

import io.opentelemetry.api.trace.Tracer;
import io.opentelemetry.api.trace.Span;
import io.opentelemetry.context.Scope;

// Get tracer
Tracer tracer = openTelemetry.getTracer("com.example.myapp");

// Create span
Span span = tracer.spanBuilder("myOperation").startSpan();

try (Scope scope = span.makeCurrent()) {
    // Your application logic
    doWork();
} catch (Throwable t) {
    span.recordException(t);
    throw t;
} finally {
    span.end();
}

Imported: Adding Span Attributes

import io.opentelemetry.api.common.AttributeKey;
import io.opentelemetry.api.common.Attributes;

Span span = tracer.spanBuilder("processOrder")
    .setAttribute("order.id", "12345")
    .setAttribute("customer.tier", "premium")
    .startSpan();

try (Scope scope = span.makeCurrent()) {
    // Add attributes during execution
    span.setAttribute("items.count", 3);
    span.setAttribute("total.amount", 99.99);
    
    processOrder();
} finally {
    span.end();
}

Imported: Nested Spans

public void parentOperation() {
    Span parentSpan = tracer.spanBuilder("parentOperation").startSpan();
    try (Scope scope = parentSpan.makeCurrent()) {
        childOperation();
    } finally {
        parentSpan.end();
    }
}

public void childOperation() {
    // Automatically links to parent via Context
    Span childSpan = tracer.spanBuilder("childOperation").startSpan();
    try (Scope scope = childSpan.makeCurrent()) {
        // Child work
    } finally {
        childSpan.end();
    }
}

Imported: Recording Exceptions

Span span = tracer.spanBuilder("riskyOperation").startSpan();
try (Scope scope = span.makeCurrent()) {
    performRiskyWork();
} catch (Exception e) {
    span.recordException(e);
    span.setStatus(StatusCode.ERROR, e.getMessage());
    throw e;
} finally {
    span.end();
}

Imported: Metrics (via OpenTelemetry)

import io.opentelemetry.api.metrics.Meter;
import io.opentelemetry.api.metrics.LongCounter;
import io.opentelemetry.api.metrics.LongHistogram;

Meter meter = openTelemetry.getMeter("com.example.myapp");

// Counter
LongCounter requestCounter = meter.counterBuilder("http.requests")
    .setDescription("Total HTTP requests")
    .setUnit("requests")
    .build();

requestCounter.add(1, Attributes.of(
    AttributeKey.stringKey("http.method"), "GET",
    AttributeKey.longKey("http.status_code"), 200L
));

// Histogram
LongHistogram latencyHistogram = meter.histogramBuilder("http.latency")
    .setDescription("Request latency")
    .setUnit("ms")
    .ofLongs()
    .build();

latencyHistogram.record(150, Attributes.of(
    AttributeKey.stringKey("http.route"), "/api/users"
));

Imported: Key Concepts

Imported: Migration to Autoconfigure

The

azure-monitor-opentelemetry-autoconfigure

• Automatic instrumentation of common libraries
• Simplified configuration
• Better integration with OpenTelemetry SDK

Migration Steps

1. Replace dependency:

   <!-- Remove -->
   <dependency>
       <groupId>com.azure</groupId>
       <artifactId>azure-monitor-opentelemetry-exporter</artifactId>
   </dependency>
   
   <!-- Add -->
   <dependency>
       <groupId>com.azure</groupId>
       <artifactId>azure-monitor-opentelemetry-autoconfigure</artifactId>
   </dependency>
   

2. Update initialization code per Migration Guide

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.