Azure Monitor Query SDK for Python workflow skill. Use this skill when the user needs Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Azure Monitor Query SDK for Python workflow skill. Use this skill when the user needs Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills/skills/azure-monitor-query-py from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
Azure Monitor Query SDK for Python Query logs and metrics from Azure Monitor and Log Analytics workspaces.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Environment Variables, Authentication, Logs Query Client, Metrics Query Client, Async Clients, Common Kusto Queries.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
This skill is applicable to execute the workflow or actions described in the overview.
Use when the request clearly matches the imported source intent: Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics.
Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
Use when provenance needs to stay visible in the answer, PR, or review packet.
Use when copied upstream references, examples, or scripts materially improve the answer.
Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
bash pip install azure-monitor-query
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
Use @azure-monitor-query-py-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @azure-monitor-query-py-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @azure-monitor-query-py-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @azure-monitor-query-py-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Use timedelta for relative time ranges
Handle partial results for large queries
Use batch queries when running multiple queries
Set appropriate granularity for metrics to reduce data points
Convert to DataFrame for easier data analysis
Use aggregations to summarize metric data
Filter by dimensions to narrow metric results
Imported Operating Notes
Imported: Best Practices
Use timedelta for relative time ranges
Handle partial results for large queries
Use batch queries when running multiple queries
Set appropriate granularity for metrics to reduce data points
Convert to DataFrame for easier data analysis
Use aggregations to summarize metric data
Filter by dimensions to narrow metric results
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/azure-monitor-query-py, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
assets/n/a
Imported Reference Notes
Imported: Authentication
from azure.identity import DefaultAzureCredential
credential = DefaultAzureCredential()
Imported: 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)
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)}")
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}")
// Requests by status code
AppRequests
| summarize count() by ResultCode
| order by count_ desc
// Exceptions over time
AppExceptions
| summarize count() by bin(TimeGenerated, 1h)
// Slow requests
AppRequests
| where DurationMs > 1000
| project TimeGenerated, Name, DurationMs
| order by DurationMs desc
// Top errors
AppExceptions
| summarize count() by ExceptionType
| top 10 by count_
Imported: Client Types
Client
Purpose
LogsQueryClient
Query Log Analytics workspaces
MetricsQueryClient
Query Azure Monitor metrics
Imported: Limitations
Use this skill only when the task clearly matches the scope described above.
Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
