Azure Monitor Ingestion Py V2

Azure Monitor Ingestion SDK for Python

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/azure-monitor-ingestion-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 Ingestion SDK for Python Send custom logs to Azure Monitor Log Analytics workspace using the Logs Ingestion API.

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, Prerequisites, Authentication, Upload Custom Logs, Upload from JSON File, Custom Error Handling.

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 Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API.
• 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

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.

1. bash pip install azure-monitor-ingestion pip install azure-identity
2. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
3. Read the overview and provenance files before loading any copied upstream support files.
4. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
5. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
6. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
7. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.

Imported Workflow Notes

Imported: Installation

pip install azure-monitor-ingestion
pip install azure-identity

Imported: Environment Variables

# Data Collection Endpoint (DCE)
AZURE_DCE_ENDPOINT=https://<dce-name>.<region>.ingest.monitor.azure.com

# Data Collection Rule (DCR) immutable ID
AZURE_DCR_RULE_ID=dcr-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

# Stream name from DCR
AZURE_DCR_STREAM_NAME=Custom-MyTable_CL

Examples

Example 1: Ask for the upstream workflow directly

Use @azure-monitor-ingestion-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-ingestion-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-ingestion-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-ingestion-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 DefaultAzureCredential for authentication
• Handle errors gracefully — use on_error callback for partial failures
• Include TimeGenerated — Required field for all logs
• Match DCR schema — Log fields must match DCR column definitions
• Use async client for high-throughput scenarios
• Batch uploads — SDK handles batching, but send reasonable chunks
• Monitor ingestion — Check Log Analytics for ingestion status

Imported Operating Notes

Imported: Best Practices

1. Use DefaultAzureCredential for authentication
2. Handle errors gracefully — use on_error callback for partial failures
3. Include TimeGenerated — Required field for all logs
4. Match DCR schema — Log fields must match DCR column definitions
5. Use async client for high-throughput scenarios
6. Batch uploads — SDK handles batching, but send reasonable chunks
7. Monitor ingestion — Check Log Analytics for ingestion status
8. Use context manager — Ensures proper client cleanup

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-ingestion-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.

Imported Reference Notes

Imported: Prerequisites

Before using this SDK, you need:

1. Log Analytics Workspace — Target for your logs
2. Data Collection Endpoint (DCE) — Ingestion endpoint
3. Data Collection Rule (DCR) — Defines schema and destination
4. Custom Table — In Log Analytics (created via DCR or manually)

Imported: Authentication

from azure.monitor.ingestion import LogsIngestionClient
from azure.identity import DefaultAzureCredential
import os

client = LogsIngestionClient(
    endpoint=os.environ["AZURE_DCE_ENDPOINT"],
    credential=DefaultAzureCredential()
)

Imported: Upload Custom Logs

from azure.monitor.ingestion import LogsIngestionClient
from azure.identity import DefaultAzureCredential
import os

client = LogsIngestionClient(
    endpoint=os.environ["AZURE_DCE_ENDPOINT"],
    credential=DefaultAzureCredential()
)

rule_id = os.environ["AZURE_DCR_RULE_ID"]
stream_name = os.environ["AZURE_DCR_STREAM_NAME"]

logs = [
    {"TimeGenerated": "2024-01-15T10:00:00Z", "Computer": "server1", "Message": "Application started"},
    {"TimeGenerated": "2024-01-15T10:01:00Z", "Computer": "server1", "Message": "Processing request"},
    {"TimeGenerated": "2024-01-15T10:02:00Z", "Computer": "server2", "Message": "Connection established"}
]

client.upload(rule_id=rule_id, stream_name=stream_name, logs=logs)

Imported: Upload from JSON File

import json

with open("logs.json", "r") as f:
    logs = json.load(f)

client.upload(rule_id=rule_id, stream_name=stream_name, logs=logs)

Imported: Custom Error Handling

Handle partial failures with a callback:

failed_logs = []

def on_error(error):
    print(f"Upload failed: {error.error}")
    failed_logs.extend(error.failed_logs)

client.upload(
    rule_id=rule_id,
    stream_name=stream_name,
    logs=logs,
   
)

# Retry failed logs
if failed_logs:
    print(f"Retrying {len(failed_logs)} failed logs...")
    client.upload(rule_id=rule_id, stream_name=stream_name, logs=failed_logs)

Imported: Ignore Errors

def ignore_errors(error):
    pass  # Silently ignore upload failures

client.upload(
    rule_id=rule_id,
    stream_name=stream_name,
    logs=logs,
   
)

Imported: Async Client

import asyncio
from azure.monitor.ingestion.aio import LogsIngestionClient
from azure.identity.aio import DefaultAzureCredential

async def upload_logs():
    async with LogsIngestionClient(
        endpoint=endpoint,
        credential=DefaultAzureCredential()
    ) as client:
        await client.upload(
            rule_id=rule_id,
            stream_name=stream_name,
            logs=logs
        )

asyncio.run(upload_logs())

Imported: Sovereign Clouds

from azure.identity import AzureAuthorityHosts, DefaultAzureCredential
from azure.monitor.ingestion import LogsIngestionClient

# Azure Government
credential = DefaultAzureCredential(authority=AzureAuthorityHosts.AZURE_GOVERNMENT)
client = LogsIngestionClient(
    endpoint="https://example.ingest.monitor.azure.us",
    credential=credential,
    credential_scopes=["https://monitor.azure.us/.default"]
)

Imported: Batching Behavior

The SDK automatically:

• Splits logs into chunks of 1MB or less
• Compresses each chunk with gzip
• Uploads chunks in parallel

No manual batching needed for large log sets.

Imported: Client Types

Imported: Key Concepts

Imported: DCR Stream Name Format

Stream names follow patterns:

• Custom-<TableName>_CL — For custom tables
• Microsoft-<TableName> — For built-in tables

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.