Agent skill · DevOps & Cloud

azure-data-tables-py

Azure Tables SDK for Python (Storage and Cosmos DB). Use for NoSQL key-value storage, entity CRUD, and batch operations.

Microsoft293,217★ · +1,988/wk · 14 repos on radarProfile →
copilotMIT
Install
npx skills add microsoft/skills --skill azure-data-tables-py --agent copilot

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

Facts
Files in the skill folder: 3
SKILL.md size: 8 KB
Bundled scripts: none
Version: 1.0.0
Declared author: Microsoft
Path: .github/plugins/azure-sdk-python/skills/azure-data-tables-py/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,860
Language: TypeScript
Read our review of the source →

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

From the SKILL.md

# Azure Tables SDK for Python NoSQL key-value store for structured data (Azure Storage Tables or Cosmos DB Table API). ## Installation ```bash pip install azure-data-tables azure-identity ``` ## Environment Variables ```bash # Azure Storage Tables AZURE_STORAGE_ACCOUNT_URL=https://<account>.table.core.windows.net # Required for Azure Storage Tables # Cosmos DB Table API COSMOS_TABLE_ENDPOINT=https

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About this skill
What does the azure-data-tables-py skill do?

Azure Tables SDK for Python (Storage and Cosmos DB). Use for NoSQL key-value storage, entity CRUD, and batch operations.

How do I install it?

Run `npx skills add microsoft/skills --skill azure-data-tables-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 microsoft/skills, a repository with 2,860 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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