Agent skill
azure-monitor-opentelemetry-py
Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/azure-monitor-opentelemetry-py-zidong-ia-biblioteca
SKILL.md
Azure Monitor OpenTelemetry Distro for Python
One-line setup for Application Insights with OpenTelemetry auto-instrumentation.
Installation
pip install azure-monitor-opentelemetry
Environment Variables
APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/
Quick Start
from azure.monitor.opentelemetry import configure_azure_monitor
# One-line setup - reads connection string from environment
configure_azure_monitor()
# Your application code...
Explicit Configuration
from azure.monitor.opentelemetry import configure_azure_monitor
configure_azure_monitor(
connection_string="InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/"
)
With Flask
from flask import Flask
from azure.monitor.opentelemetry import configure_azure_monitor
configure_azure_monitor()
app = Flask(__name__)
@app.route("/")
def hello():
return "Hello, World!"
if __name__ == "__main__":
app.run()
With Django
# settings.py
from azure.monitor.opentelemetry import configure_azure_monitor
configure_azure_monitor()
# Django settings...
With FastAPI
from fastapi import FastAPI
from azure.monitor.opentelemetry import configure_azure_monitor
configure_azure_monitor()
app = FastAPI()
@app.get("/")
async def root():
return {"message": "Hello World"}
Custom Traces
from opentelemetry import trace
from azure.monitor.opentelemetry import configure_azure_monitor
configure_azure_monitor()
tracer = trace.get_tracer(__name__)
with tracer.start_as_current_span("my-operation") as span:
span.set_attribute("custom.attribute", "value")
# Do work...
Custom Metrics
from opentelemetry import metrics
from azure.monitor.opentelemetry import configure_azure_monitor
configure_azure_monitor()
meter = metrics.get_meter(__name__)
counter = meter.create_counter("my_counter")
counter.add(1, {"dimension": "value"})
Custom Logs
import logging
from azure.monitor.opentelemetry import configure_azure_monitor
configure_azure_monitor()
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
logger.info("This will appear in Application Insights")
logger.error("Errors are captured too", exc_info=True)
Sampling
from azure.monitor.opentelemetry import configure_azure_monitor
# Sample 10% of requests
configure_azure_monitor(
sampling_ratio=0.1
)
Cloud Role Name
Set cloud role name for Application Map:
from azure.monitor.opentelemetry import configure_azure_monitor
from opentelemetry.sdk.resources import Resource, SERVICE_NAME
configure_azure_monitor(
resource=Resource.create({SERVICE_NAME: "my-service-name"})
)
Disable Specific Instrumentations
from azure.monitor.opentelemetry import configure_azure_monitor
configure_azure_monitor(
instrumentations=["flask", "requests"] # Only enable these
)
Enable Live Metrics
from azure.monitor.opentelemetry import configure_azure_monitor
configure_azure_monitor(
enable_live_metrics=True
)
Azure AD Authentication
from azure.monitor.opentelemetry import configure_azure_monitor
from azure.identity import DefaultAzureCredential
configure_azure_monitor(
credential=DefaultAzureCredential()
)
Auto-Instrumentations Included
| Library | Telemetry Type |
|---|---|
| Flask | Traces |
| Django | Traces |
| FastAPI | Traces |
| Requests | Traces |
| urllib3 | Traces |
| httpx | Traces |
| aiohttp | Traces |
| psycopg2 | Traces |
| pymysql | Traces |
| pymongo | Traces |
| redis | Traces |
Configuration Options
| Parameter | Description | Default |
|---|---|---|
connection_string |
Application Insights connection string | From env var |
credential |
Azure credential for AAD auth | None |
sampling_ratio |
Sampling rate (0.0 to 1.0) | 1.0 |
resource |
OpenTelemetry Resource | Auto-detected |
instrumentations |
List of instrumentations to enable | All |
enable_live_metrics |
Enable Live Metrics stream | False |
Best Practices
- Call configure_azure_monitor() early — Before importing instrumented libraries
- Use environment variables for connection string in production
- Set cloud role name for multi-service applications
- Enable sampling in high-traffic applications
- Use structured logging for better log analytics queries
- Add custom attributes to spans for better debugging
- Use AAD authentication for production workloads
When to Use
This skill is applicable to execute the workflow or actions described in the overview.
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