Azure Monitor Opentelemetry Exporter Py V2

Azure Monitor OpenTelemetry Exporter for Python

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/azure-monitor-opentelemetry-exporter-py 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 Exporter for Python Low-level exporter for sending OpenTelemetry traces, metrics, and logs to 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: Environment Variables, Trace Exporter, Metric Exporter, Log Exporter, From Environment Variable, Azure AD Authentication.

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.

• Scenario - Use
• Quick setup, auto-instrumentation - azure-monitor-opentelemetry (distro)
• Custom OpenTelemetry pipeline - azure-monitor-opentelemetry-exporter (this)
• Fine-grained control over telemetry - azure-monitor-opentelemetry-exporter (this)
• Use when the request clearly matches the imported source intent: Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.

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 pip install azure-monitor-opentelemetry-exporter
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

pip install azure-monitor-opentelemetry-exporter

Imported: Environment Variables

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

Examples

Example 1: Ask for the upstream workflow directly

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

• Use BatchSpanProcessor for production (not SimpleSpanProcessor)
• Use ApplicationInsightsSampler for consistent sampling across services
• Enable offline storage for reliability in production
• Use AAD authentication instead of instrumentation keys
• Set export intervals appropriate for your workload
• Use the distro (azure-monitor-opentelemetry) unless you need custom pipelines
• 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. Use BatchSpanProcessor for production (not SimpleSpanProcessor)
2. Use ApplicationInsightsSampler for consistent sampling across services
3. Enable offline storage for reliability in production
4. Use AAD authentication instead of instrumentation keys
5. Set export intervals appropriate for your workload
6. Use the distro (azure-monitor-opentelemetry) unless you need custom pipelines

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-exporter-py, 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: Trace Exporter

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Create exporter
exporter = AzureMonitorTraceExporter(
    connection_string="InstrumentationKey=xxx;..."
)

# Configure tracer provider
trace.set_tracer_provider(TracerProvider())
trace.get_tracer_provider().add_span_processor(
    BatchSpanProcessor(exporter)
)

# Use tracer
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("my-span"):
    print("Hello, World!")

Imported: Metric Exporter

from opentelemetry import metrics
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import PeriodicExportingMetricReader
from azure.monitor.opentelemetry.exporter import AzureMonitorMetricExporter

# Create exporter
exporter = AzureMonitorMetricExporter(
    connection_string="InstrumentationKey=xxx;..."
)

# Configure meter provider
reader = PeriodicExportingMetricReader(exporter, export_interval_millis=60000)
metrics.set_meter_provider(MeterProvider(metric_readers=[reader]))

# Use meter
meter = metrics.get_meter(__name__)
counter = meter.create_counter("requests_total")
counter.add(1, {"route": "/api/users"})

Imported: Log Exporter

import logging
from opentelemetry._logs import set_logger_provider
from opentelemetry.sdk._logs import LoggerProvider, LoggingHandler
from opentelemetry.sdk._logs.export import BatchLogRecordProcessor
from azure.monitor.opentelemetry.exporter import AzureMonitorLogExporter

# Create exporter
exporter = AzureMonitorLogExporter(
    connection_string="InstrumentationKey=xxx;..."
)

# Configure logger provider
logger_provider = LoggerProvider()
logger_provider.add_log_record_processor(BatchLogRecordProcessor(exporter))
set_logger_provider(logger_provider)

# Add handler to Python logging
handler = LoggingHandler(level=logging.INFO, logger_provider=logger_provider)
logging.getLogger().addHandler(handler)

# Use logging
logger = logging.getLogger(__name__)
logger.info("This will be sent to Application Insights")

Imported: From Environment Variable

Exporters read APPLICATIONINSIGHTS_CONNECTION_STRING automatically:

from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Connection string from environment
exporter = AzureMonitorTraceExporter()

Imported: Azure AD Authentication

from azure.identity import DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

exporter = AzureMonitorTraceExporter(
    credential=DefaultAzureCredential()
)

Imported: Sampling

Use ApplicationInsightsSampler for consistent sampling:

from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.sampling import ParentBasedTraceIdRatio
from azure.monitor.opentelemetry.exporter import ApplicationInsightsSampler

# Sample 10% of traces
sampler = ApplicationInsightsSampler(sampling_ratio=0.1)

trace.set_tracer_provider(TracerProvider(sampler=sampler))

Imported: Offline Storage

Configure offline storage for retry:

from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

exporter = AzureMonitorTraceExporter(
    connection_string="...",
    storage_directory="/path/to/storage",  # Custom storage path
    disable_offline_storage=False  # Enable retry (default)
)

Imported: Disable Offline Storage

exporter = AzureMonitorTraceExporter(
    connection_string="...",
    disable_offline_storage=True  # No retry on failure
)

Imported: Sovereign Clouds

from azure.identity import AzureAuthorityHosts, DefaultAzureCredential
from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter

# Azure Government
credential = DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_GOVERNMENT)
exporter = AzureMonitorTraceExporter(
    connection_string="InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.us/",
    credential=credential
)

Imported: Exporter Types

Imported: Configuration Options

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.