feat: Add Official Microsoft & Gemini Skills (845+ Total)
🚀 Impact Significantly expands the capabilities of **Antigravity Awesome Skills** by integrating official skill collections from **Microsoft** and **Google Gemini**. This update increases the total skill count to **845+**, making the library even more comprehensive for AI coding assistants. ✨ Key Changes 1. New Official Skills - **Microsoft Skills**: Added a massive collection of official skills from [microsoft/skills](https://github.com/microsoft/skills). - Includes Azure, .NET, Python, TypeScript, and Semantic Kernel skills. - Preserves the original directory structure under `skills/official/microsoft/`. - Includes plugin skills from the `.github/plugins` directory. - **Gemini Skills**: Added official Gemini API development skills under `skills/gemini-api-dev/`. 2. New Scripts & Tooling - **`scripts/sync_microsoft_skills.py`**: A robust synchronization script that: - Clones the official Microsoft repository. - Preserves the original directory heirarchy. - Handles symlinks and plugin locations. - Generates attribution metadata. - **`scripts/tests/inspect_microsoft_repo.py`**: Debug tool to inspect the remote repository structure. - **`scripts/tests/test_comprehensive_coverage.py`**: Verification script to ensure 100% of skills are captured during sync. 3. Core Improvements - **`scripts/generate_index.py`**: Enhanced frontmatter parsing to safely handle unquoted values containing `@` symbols and commas (fixing issues with some Microsoft skill descriptions). - **`package.json`**: Added `sync:microsoft` and `sync:all-official` scripts for easy maintenance. 4. Documentation - Updated `README.md` to reflect the new skill counts (845+) and added Microsoft/Gemini to the provider list. - Updated `CATALOG.md` and `skills_index.json` with the new skills. 🧪 Verification - Ran `scripts/tests/test_comprehensive_coverage.py` to verify all Microsoft skills are detected. - Validated `generate_index.py` fixes by successfully indexing the new skills.
This commit is contained in:
Ahmed Rehan
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skills/official/microsoft/python/monitoring/query/SKILL.md
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skills/official/microsoft/python/monitoring/query/SKILL.md
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---
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name: azure-monitor-query-py
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description: |
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Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics.
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Triggers: "azure-monitor-query", "LogsQueryClient", "MetricsQueryClient", "Log Analytics", "Kusto queries", "Azure metrics".
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package: azure-monitor-query
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---
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# Azure Monitor Query SDK for Python
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Query logs and metrics from Azure Monitor and Log Analytics workspaces.
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## Installation
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```bash
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pip install azure-monitor-query
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```
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## Environment Variables
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```bash
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# Log Analytics
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AZURE_LOG_ANALYTICS_WORKSPACE_ID=<workspace-id>
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# Metrics
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AZURE_METRICS_RESOURCE_URI=/subscriptions/<sub>/resourceGroups/<rg>/providers/<provider>/<type>/<name>
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```
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## Authentication
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```python
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from azure.identity import DefaultAzureCredential
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credential = DefaultAzureCredential()
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```
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## Logs Query Client
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### Basic Query
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```python
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from azure.monitor.query import LogsQueryClient
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from datetime import timedelta
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client = LogsQueryClient(credential)
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query = """
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AppRequests
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| where TimeGenerated > ago(1h)
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| summarize count() by bin(TimeGenerated, 5m), ResultCode
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| order by TimeGenerated desc
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"""
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response = client.query_workspace(
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workspace_id=os.environ["AZURE_LOG_ANALYTICS_WORKSPACE_ID"],
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query=query,
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timespan=timedelta(hours=1)
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)
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for table in response.tables:
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for row in table.rows:
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print(row)
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```
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### Query with Time Range
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```python
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from datetime import datetime, timezone
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response = client.query_workspace(
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workspace_id=workspace_id,
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query="AppRequests | take 10",
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timespan=(
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datetime(2024, 1, 1, tzinfo=timezone.utc),
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datetime(2024, 1, 2, tzinfo=timezone.utc)
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)
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)
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```
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### Convert to DataFrame
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```python
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import pandas as pd
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response = client.query_workspace(workspace_id, query, timespan=timedelta(hours=1))
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if response.tables:
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table = response.tables[0]
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df = pd.DataFrame(data=table.rows, columns=[col.name for col in table.columns])
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print(df.head())
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```
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### Batch Query
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```python
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from azure.monitor.query import LogsBatchQuery
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queries = [
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LogsBatchQuery(workspace_id=workspace_id, query="AppRequests | take 5", timespan=timedelta(hours=1)),
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LogsBatchQuery(workspace_id=workspace_id, query="AppExceptions | take 5", timespan=timedelta(hours=1))
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]
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responses = client.query_batch(queries)
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for response in responses:
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if response.tables:
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print(f"Rows: {len(response.tables[0].rows)}")
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```
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### Handle Partial Results
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```python
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from azure.monitor.query import LogsQueryStatus
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response = client.query_workspace(workspace_id, query, timespan=timedelta(hours=24))
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if response.status == LogsQueryStatus.PARTIAL:
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print(f"Partial results: {response.partial_error}")
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elif response.status == LogsQueryStatus.FAILURE:
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print(f"Query failed: {response.partial_error}")
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```
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## Metrics Query Client
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### Query Resource Metrics
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```python
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from azure.monitor.query import MetricsQueryClient
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from datetime import timedelta
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metrics_client = MetricsQueryClient(credential)
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response = metrics_client.query_resource(
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resource_uri=os.environ["AZURE_METRICS_RESOURCE_URI"],
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metric_names=["Percentage CPU", "Network In Total"],
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timespan=timedelta(hours=1),
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granularity=timedelta(minutes=5)
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)
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for metric in response.metrics:
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print(f"{metric.name}:")
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for time_series in metric.timeseries:
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for data in time_series.data:
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print(f" {data.timestamp}: {data.average}")
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```
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### Aggregations
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```python
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from azure.monitor.query import MetricAggregationType
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response = metrics_client.query_resource(
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resource_uri=resource_uri,
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metric_names=["Requests"],
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timespan=timedelta(hours=1),
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aggregations=[
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MetricAggregationType.AVERAGE,
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MetricAggregationType.MAXIMUM,
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MetricAggregationType.MINIMUM,
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MetricAggregationType.COUNT
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]
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)
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```
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### Filter by Dimension
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```python
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response = metrics_client.query_resource(
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resource_uri=resource_uri,
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metric_names=["Requests"],
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timespan=timedelta(hours=1),
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filter="ApiName eq 'GetBlob'"
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)
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```
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### List Metric Definitions
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```python
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definitions = metrics_client.list_metric_definitions(resource_uri)
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for definition in definitions:
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print(f"{definition.name}: {definition.unit}")
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```
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### List Metric Namespaces
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```python
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namespaces = metrics_client.list_metric_namespaces(resource_uri)
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for ns in namespaces:
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print(ns.fully_qualified_namespace)
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```
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## Async Clients
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```python
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from azure.monitor.query.aio import LogsQueryClient, MetricsQueryClient
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from azure.identity.aio import DefaultAzureCredential
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async def query_logs():
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credential = DefaultAzureCredential()
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client = LogsQueryClient(credential)
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response = await client.query_workspace(
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workspace_id=workspace_id,
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query="AppRequests | take 10",
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timespan=timedelta(hours=1)
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)
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await client.close()
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await credential.close()
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return response
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```
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## Common Kusto Queries
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```kusto
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// Requests by status code
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AppRequests
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| summarize count() by ResultCode
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| order by count_ desc
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// Exceptions over time
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AppExceptions
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| summarize count() by bin(TimeGenerated, 1h)
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// Slow requests
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AppRequests
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| where DurationMs > 1000
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| project TimeGenerated, Name, DurationMs
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| order by DurationMs desc
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// Top errors
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AppExceptions
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| summarize count() by ExceptionType
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| top 10 by count_
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```
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## Client Types
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| Client | Purpose |
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|--------|---------|
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| `LogsQueryClient` | Query Log Analytics workspaces |
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| `MetricsQueryClient` | Query Azure Monitor metrics |
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## Best Practices
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1. **Use timedelta** for relative time ranges
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2. **Handle partial results** for large queries
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3. **Use batch queries** when running multiple queries
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4. **Set appropriate granularity** for metrics to reduce data points
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5. **Convert to DataFrame** for easier data analysis
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6. **Use aggregations** to summarize metric data
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7. **Filter by dimensions** to narrow metric results
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