Azure Monitor Query Py
Azure Monitor Query 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-query-py && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/azure-monitor-query-py/SKILL.md -o ./skills/azure-monitor-query-py/SKILL.mdRun in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Azure Monitor Query SDK for Python
Query logs and metrics from Azure Monitor and Log Analytics workspaces.
Installation
pip install azure-monitor-queryEnvironment Variables
# Log Analytics
AZURE_LOG_ANALYTICS_WORKSPACE_ID=<workspace-id>
# Metrics
AZURE_METRICS_RESOURCE_URI=/subscriptions/<sub>/resourceGroups/<rg>/providers/<provider>/<type>/<name>Authentication
from azure.identity import DefaultAzureCredential
credential = DefaultAzureCredential()Logs Query Client
Basic Query
from azure.monitor.query import LogsQueryClient
from datetime import timedelta
client = LogsQueryClient(credential)
query = """
AppRequests
| where TimeGenerated > ago(1h)
| summarize count() by bin(TimeGenerated, 5m), ResultCode
| order by TimeGenerated desc
"""
response = client.query_workspace(
workspace_id=os.environ["AZURE_LOG_ANALYTICS_WORKSPACE_ID"],
query=query,
timespan=timedelta(hours=1)
)
for table in response.tables:
for row in table.rows:
print(row)Query with Time Range
from datetime import datetime, timezone
response = client.query_workspace(
workspace_id=workspace_id,
query="AppRequests | take 10",
timespan=(
datetime(2024, 1, 1, tzinfo=timezone.utc),
datetime(2024, 1, 2, tzinfo=timezone.utc)
)
)Convert to DataFrame
import pandas as pd
response = client.query_workspace(workspace_id, query, timespan=timedelta(hours=1))
if response.tables:
table = response.tables[0]
df = pd.DataFrame(data=table.rows, columns=[col.name for col in table.columns])
print(df.head())Batch Query
from azure.monitor.query import LogsBatchQuery
queries = [
LogsBatchQuery(workspace_id=workspace_id, query="AppRequests | take 5", timespan=timedelta(hours=1)),
LogsBatchQuery(workspace_id=workspace_id, query="AppExceptions | take 5", timespan=timedelta(hours=1))
]
responses = client.query_batch(queries)
for response in responses:
if response.tables:
print(f"Rows: {len(response.tables[0].rows)}")Handle Partial Results
from azure.monitor.query import LogsQueryStatus
response = client.query_workspace(workspace_id, query, timespan=timedelta(hours=24))
if response.status == LogsQueryStatus.PARTIAL:
print(f"Partial results: {response.partial_error}")
elif response.status == LogsQueryStatus.FAILURE:
print(f"Query failed: {response.partial_error}")Metrics Query Client
Query Resource Metrics
from azure.monitor.query import MetricsQueryClient
from datetime import timedelta
metrics_client = MetricsQueryClient(credential)
response = metrics_client.query_resource(
resource_uri=os.environ["AZURE_METRICS_RESOURCE_URI"],
metric_names=["Percentage CPU", "Network In Total"],
timespan=timedelta(hours=1),
granularity=timedelta(minutes=5)
)
for metric in response.metrics:
print(f"{metric.name}:")
for time_series in metric.timeseries:
for data in time_series.data:
print(f" {data.timestamp}: {data.average}")Aggregations
from azure.monitor.query import MetricAggregationType
response = metrics_client.query_resource(
resource_uri=resource_uri,
metric_names=["Requests"],
timespan=timedelta(hours=1),
aggregations=[
MetricAggregationType.AVERAGE,
MetricAggregationType.MAXIMUM,
MetricAggregationType.MINIMUM,
MetricAggregationType.COUNT
]
)Filter by Dimension
response = metrics_client.query_resource(
resource_uri=resource_uri,
metric_names=["Requests"],
timespan=timedelta(hours=1),
filter="ApiName eq 'GetBlob'"
)List Metric Definitions
definitions = metrics_client.list_metric_definitions(resource_uri)
for definition in definitions:
print(f"{definition.name}: {definition.unit}")List Metric Namespaces
namespaces 🎯 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 Query 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 Query 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 Query 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 Query Py?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/azure-monitor-query-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.