Agent skill · DevOps & Cloud

azure-monitor-ingestion-py

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

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill azure-monitor-ingestion-py --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 5 KB
Bundled scripts: none
Path: skills/api/azure-monitor-ingestion-py/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Azure Monitor Ingestion SDK for Python Send custom logs to Azure Monitor Log Analytics workspace using the Logs Ingestion API. ## Installation ```bash pip install azure-monitor-ingestion pip install azure-identity ``` ## Environment Variables ```bash # 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 ``` ## 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) ## Authentication ```python from azure.monitor.ingestion import LogsIngestionClient from azure.identity import DefaultAzureCredential import os client = LogsIngestionClient( endpoint=os.environ["AZURE_DCE_ENDPOINT"], credential=DefaultAzureCredential() ) ``` ## Upload Custom Logs ```python from azure.monitor.ingestion import LogsIngestionClient from azure.identity import Defau

What's inside
Steps it walks through
  1. Installation
  2. Environment Variables
  3. Prerequisites
  4. Authentication
  5. Upload Custom Logs
  6. Upload from JSON File
  7. Custom Error Handling
  8. Ignore Errors
  9. Async Client
  10. Sovereign Clouds
  11. Batching Behavior
  12. Client Types
  13. Key Concepts
  14. DCR Stream Name Format
Ships with 1 file
  • metadata.json
Commands it runs
pip install azure-monitor-ingestion
pip install azure-identity
Data Collection Endpoint (DCE)
Data Collection Rule (DCR) immutable ID
Stream name from DCR
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About this skill
What does the azure-monitor-ingestion-py skill do?

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

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill azure-monitor-ingestion-py --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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