refactor: flatten Microsoft skills from nested to flat directory structure
Rewrote sync_microsoft_skills.py (v4) to use each SKILL.md's frontmatter 'name' field as the flat directory name under skills/, replacing the nested skills/official/microsoft/<lang>/<category>/<service>/ hierarchy. This fixes CI failures caused by the indexing, validation, and catalog scripts expecting skills/<id>/SKILL.md (depth 1). Changes: - Rewrite scripts/sync_microsoft_skills.py for flat output with collision detection - Update scripts/tests/inspect_microsoft_repo.py for flat name mapping - Update scripts/tests/test_comprehensive_coverage.py for name uniqueness checks - Delete skills/official/ nested directory - Add 129 Microsoft skills as flat directories (e.g. skills/azure-mgmt-botservice-dotnet/) - Move attribution files to docs/ (LICENSE-MICROSOFT, microsoft-skills-attribution.json) - Rebuild skills_index.json, CATALOG.md, README.md (845 total skills)
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skills/azure-monitor-opentelemetry-py/SKILL.md
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skills/azure-monitor-opentelemetry-py/SKILL.md
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---
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name: azure-monitor-opentelemetry-py
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description: |
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Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation.
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Triggers: "azure-monitor-opentelemetry", "configure_azure_monitor", "Application Insights", "OpenTelemetry distro", "auto-instrumentation".
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package: azure-monitor-opentelemetry
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---
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# Azure Monitor OpenTelemetry Distro for Python
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One-line setup for Application Insights with OpenTelemetry auto-instrumentation.
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## Installation
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```bash
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pip install azure-monitor-opentelemetry
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```
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## Environment Variables
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```bash
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APPLICATIONINSIGHTS_CONNECTION_STRING=InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/
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```
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## Quick Start
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```python
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from azure.monitor.opentelemetry import configure_azure_monitor
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# One-line setup - reads connection string from environment
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configure_azure_monitor()
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# Your application code...
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```
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## Explicit Configuration
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```python
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from azure.monitor.opentelemetry import configure_azure_monitor
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configure_azure_monitor(
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connection_string="InstrumentationKey=xxx;IngestionEndpoint=https://xxx.in.applicationinsights.azure.com/"
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)
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```
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## With Flask
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```python
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from flask import Flask
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from azure.monitor.opentelemetry import configure_azure_monitor
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configure_azure_monitor()
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app = Flask(__name__)
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@app.route("/")
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def hello():
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return "Hello, World!"
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if __name__ == "__main__":
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app.run()
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```
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## With Django
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```python
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# settings.py
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from azure.monitor.opentelemetry import configure_azure_monitor
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configure_azure_monitor()
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# Django settings...
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```
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## With FastAPI
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```python
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from fastapi import FastAPI
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from azure.monitor.opentelemetry import configure_azure_monitor
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configure_azure_monitor()
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app = FastAPI()
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@app.get("/")
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async def root():
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return {"message": "Hello World"}
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```
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## Custom Traces
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```python
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from opentelemetry import trace
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from azure.monitor.opentelemetry import configure_azure_monitor
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configure_azure_monitor()
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tracer = trace.get_tracer(__name__)
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with tracer.start_as_current_span("my-operation") as span:
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span.set_attribute("custom.attribute", "value")
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# Do work...
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```
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## Custom Metrics
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```python
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from opentelemetry import metrics
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from azure.monitor.opentelemetry import configure_azure_monitor
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configure_azure_monitor()
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meter = metrics.get_meter(__name__)
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counter = meter.create_counter("my_counter")
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counter.add(1, {"dimension": "value"})
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```
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## Custom Logs
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```python
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import logging
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from azure.monitor.opentelemetry import configure_azure_monitor
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configure_azure_monitor()
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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logger.info("This will appear in Application Insights")
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logger.error("Errors are captured too", exc_info=True)
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```
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## Sampling
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```python
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from azure.monitor.opentelemetry import configure_azure_monitor
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# Sample 10% of requests
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configure_azure_monitor(
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sampling_ratio=0.1
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)
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```
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## Cloud Role Name
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Set cloud role name for Application Map:
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```python
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from azure.monitor.opentelemetry import configure_azure_monitor
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from opentelemetry.sdk.resources import Resource, SERVICE_NAME
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configure_azure_monitor(
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resource=Resource.create({SERVICE_NAME: "my-service-name"})
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)
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```
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## Disable Specific Instrumentations
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```python
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from azure.monitor.opentelemetry import configure_azure_monitor
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configure_azure_monitor(
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instrumentations=["flask", "requests"] # Only enable these
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)
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```
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## Enable Live Metrics
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```python
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from azure.monitor.opentelemetry import configure_azure_monitor
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configure_azure_monitor(
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enable_live_metrics=True
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)
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```
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## Azure AD Authentication
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```python
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from azure.monitor.opentelemetry import configure_azure_monitor
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from azure.identity import DefaultAzureCredential
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configure_azure_monitor(
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credential=DefaultAzureCredential()
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)
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```
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## Auto-Instrumentations Included
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| Library | Telemetry Type |
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|---------|---------------|
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| Flask | Traces |
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| Django | Traces |
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| FastAPI | Traces |
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| Requests | Traces |
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| urllib3 | Traces |
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| httpx | Traces |
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| aiohttp | Traces |
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| psycopg2 | Traces |
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| pymysql | Traces |
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| pymongo | Traces |
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| redis | Traces |
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## Configuration Options
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| Parameter | Description | Default |
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|-----------|-------------|---------|
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| `connection_string` | Application Insights connection string | From env var |
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| `credential` | Azure credential for AAD auth | None |
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| `sampling_ratio` | Sampling rate (0.0 to 1.0) | 1.0 |
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| `resource` | OpenTelemetry Resource | Auto-detected |
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| `instrumentations` | List of instrumentations to enable | All |
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| `enable_live_metrics` | Enable Live Metrics stream | False |
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## Best Practices
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1. **Call configure_azure_monitor() early** — Before importing instrumented libraries
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2. **Use environment variables** for connection string in production
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3. **Set cloud role name** for multi-service applications
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4. **Enable sampling** in high-traffic applications
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5. **Use structured logging** for better log analytics queries
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6. **Add custom attributes** to spans for better debugging
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7. **Use AAD authentication** for production workloads
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