Azure Monitor Opentelemetry Py
Azure Monitor Opentelemetry Py is an code AI skill with a core value of |. It
helps developers solve real-world problems in the code domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
|
Quick Facts
mkdir -p ./skills/azure-monitor-opentelemetry-py && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/azure-monitor-opentelemetry-py/SKILL.md -o ./skills/azure-monitor-opentelemetry-py/SKILL.mdRun in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Azure Monitor OpenTelemetry Distro for Python
One-line setup for Application Insights with OpenTelemetry auto-instrumentation.
Installation
pip install azure-monitor-opentelemetryEnvironment 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
🎯 Best For
- Claude users
- Software engineers
- Development teams
- Tech leads
💡 Use Cases
- Code quality improvement
- Best practice enforcement
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Azure Monitor Opentelemetry Py to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Is Azure Monitor Opentelemetry Py compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for Azure Monitor Opentelemetry Py?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install Azure Monitor Opentelemetry Py?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/azure-monitor-opentelemetry-py/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.