Official agent skill

Azure Data Tables Py

by microsoft in microsoft/skills

Azure Tables SDK for Python (Storage and Cosmos DB). An agent skill from microsoft/skills.

OfficialMITAuto-check passedDatabases

Install Azure Data Tables Py

skills CLI
$ npx skills add microsoft/skills --skill azure-data-tables-py -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install microsoft/skills azure-data-tables-py --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/microsoft/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/plugins/azure-sdk-python/skills/azure-data-tables-py .claude/skills/azure-data-tables-py && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
azure-data-tables-py
GitHub stars
3.1k
Used in
1 other repo
Token cost
~2.1k tokens
SKILL.md length
377 words
Files
3 (incl. references)
Skills in repo
150
Repo updated
First seen
Licence
MIT

At a glance

Azure Tables SDK for Python (Storage and Cosmos DB). An agent skill from microsoft/skills.

  • Works in 10 steps: Pick sync OR async and stay consistent.… → Always use context managers for clients… → Use DefaultAzureCredential for portable… → …
  • NoSQL key-value storage
  • SKILL.md covers Installation, Environment Variables, Authentication & Lifecycle and Client Types, plus 8 more sections
  • Calls pip; reaches learn.microsoft.com; needs AZURE_TOKEN_CREDENTIALS

What it does

Azure Data Tables Py is an agent skill from microsoft/skills, published by the product's own GitHub organization. Azure Tables SDK for Python (Storage and Cosmos DB). Use for NoSQL key-value storage, entity CRUD, and batch operations. Triggers: "table storage", "TableServiceClient", "TableClient", "entities", "PartitionKey", "RowKey".

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/capabilities.md` and `references/non-hero-scenarios.md`).

It sits in Databases, covering NoSQL databases. It works with Microsoft Azure, Python, Azure Cosmos DB and Visual Studio Code. The repository describes itself as: Skills, MCP servers, Custom Agents, Agents.md for SDKs to ground Coding Agents. The licence is MIT.

When your agent uses it

  • NoSQL key-value storage
  • Batch operations

Example prompts

  • “table storage”
  • “TableServiceClient”
  • “TableClient”
  • “/azure-data-tables-py”

Requirements

  • Python 3

Workflow steps

10 steps, taken from the first numbered list in SKILL.md.

  1. Pick sync OR async and stay consistent. Do not mix azure.data.tables sync clients with azure.data.tables.aio async clients in the same…
  2. Always use context managers for clients and async credentials. Wrap every client in with TableClient(...) as client: (sync) or async with…
  3. Use DefaultAzureCredential for portable auth across local dev and Azure (avoid connection strings / API keys when possible).
  4. Design partition keys for query patterns and even distribution
  5. Query within partitions whenever possible (cross-partition is expensive)
  6. Use batch operations for multiple entities in same partition
  7. Use upsert_entity for idempotent writes
  8. Use parameterized queries to prevent injection
  9. Keep entities small — max 1MB per entity
  10. Use async client for high-throughput scenarios

What it can do on your machine

Read from SKILL.md and the folder at commit 354361d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • learn.microsoft.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • AZURE_TOKEN_CREDENTIALS

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Azure Data Tables Py loads about 2.1k tokens when it runs, and up to ~2.9k if it reads all its reference files. Until then it costs about 61 tokens; SKILL.md has 377 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~61
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from microsoft/skills at commit 354361d, republished under its MIT licence (© microsoft). 377 words, ~2,103 tokens.

Download SKILL.mdSave it as .claude/skills/azure-data-tables-py/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
azure-data-tables-py
description
Azure Tables SDK for Python (Storage and Cosmos DB). Use for NoSQL key-value storage, entity CRUD, and batch operations. Triggers: "table storage", "TableServiceClient", "TableClient", "entities", "PartitionKey", "RowKey".
license
MIT
metadata.author
Microsoft
metadata.version
1.0.0
metadata.package
azure-data-tables

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://<account>.table.cosmos.azure.com  # Required for Cosmos DB Table API
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production

Authentication & Lifecycle

🔑 Two rules apply to every code sample below:

  1. Prefer DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
    • Local dev: DefaultAzureCredential works as-is.
    • Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.
  2. Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically:
    • Sync: with <Client>(...) as client:
    • Async: async with <Client>(...) as client: and async with DefaultAzureCredential() as credential: (from azure.identity.aio)

Snippets may abbreviate this setup, but production code should always follow both rules.

python
import os
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.data.tables import TableServiceClient, TableClient

# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()

endpoint = "https://<account>.table.core.windows.net"

# Service client (manage tables)
with TableServiceClient(endpoint=endpoint, credential=credential) as service_client:
    # Use service_client here (see following sections for operations)
    ...

# Table client (work with entities)
with TableClient(endpoint=endpoint, table_name="mytable", credential=credential) as table_client:
    # Use table_client here (see following sections for operations)
    ...

Client Types

ClientPurpose
TableServiceClientCreate/delete tables, list tables
TableClientEntity CRUD, queries

Table Operations

python
# Create table
service_client.create_table("mytable")

# Create if not exists
service_client.create_table_if_not_exists("mytable")

# Delete table
service_client.delete_table("mytable")

# List tables
for table in service_client.list_tables():
    print(table.name)

# Get table client
table_client = service_client.get_table_client("mytable")

Entity Operations

Important: Every entity requires PartitionKey and RowKey (together form unique ID).

Create Entity
python
entity = {
    "PartitionKey": "sales",
    "RowKey": "order-001",
    "product": "Widget",
    "quantity": 5,
    "price": 9.99,
    "shipped": False
}

# Create (fails if exists)
table_client.create_entity(entity=entity)

# Upsert (create or replace)
table_client.upsert_entity(entity=entity)
Get Entity
python
# Get by key (fastest)
entity = table_client.get_entity(
    partition_key="sales",
    row_key="order-001"
)
print(f"Product: {entity['product']}")
Update Entity
python
# Replace entire entity
entity["quantity"] = 10
table_client.update_entity(entity=entity, mode="replace")

# Merge (update specific fields only)
update = {
    "PartitionKey": "sales",
    "RowKey": "order-001",
    "shipped": True
}
table_client.update_entity(entity=update, mode="merge")
Delete Entity
python
table_client.delete_entity(
    partition_key="sales",
    row_key="order-001"
)

Query Entities

Query Within Partition
python
# Query by partition (efficient)
entities = table_client.query_entities(
    query_filter="PartitionKey eq 'sales'"
)
for entity in entities:
    print(entity)
Query with Filters
python
# Filter by properties
entities = table_client.query_entities(
    query_filter="PartitionKey eq 'sales' and quantity gt 3"
)

# With parameters (safer)
entities = table_client.query_entities(
    query_filter="PartitionKey eq @pk and price lt @max_price",
    parameters={"pk": "sales", "max_price": 50.0}
)
Select Specific Properties
python
entities = table_client.query_entities(
    query_filter="PartitionKey eq 'sales'",
    select=["RowKey", "product", "price"]
)
List All Entities
python
# List all (cross-partition - use sparingly)
for entity in table_client.list_entities():
    print(entity)

Batch Operations

python
from azure.data.tables import TableTransactionError

# Batch operations (same partition only!)
operations = [
    ("create", {"PartitionKey": "batch", "RowKey": "1", "data": "first"}),
    ("create", {"PartitionKey": "batch", "RowKey": "2", "data": "second"}),
    ("upsert", {"PartitionKey": "batch", "RowKey": "3", "data": "third"}),
]

try:
    table_client.submit_transaction(operations)
except TableTransactionError as e:
    print(f"Transaction failed: {e}")

Async Client

python
from azure.data.tables.aio import TableServiceClient, TableClient
from azure.identity.aio import DefaultAzureCredential

async def table_operations():
    async with DefaultAzureCredential() as credential:
        async with TableClient(
            endpoint="https://<account>.table.core.windows.net",
            table_name="mytable",
            credential=credential
        ) as client:
            # Create
            await client.create_entity(entity={
                "PartitionKey": "async",
                "RowKey": "1",
                "data": "test"
            })
            
            # Query
            async for entity in client.query_entities("PartitionKey eq 'async'"):
                print(entity)

import asyncio
asyncio.run(table_operations())

Data Types

Python TypeTable Storage Type
strString
intInt64
floatDouble
boolBoolean
datetimeDateTime
bytesBinary
UUIDGuid
Show full SKILL.md (168 more words)Show less

Best Practices

  1. Pick sync OR async and stay consistent. Do not mix azure.data.tables sync clients with azure.data.tables.aio async clients in the same call path. Choose one mode per module.
  2. Always use context managers for clients and async credentials. Wrap every client in with TableClient(...) as client: (sync) or async with TableClient(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.
  3. Use DefaultAzureCredential for portable auth across local dev and Azure (avoid connection strings / API keys when possible).
  4. Design partition keys for query patterns and even distribution
  5. Query within partitions whenever possible (cross-partition is expensive)
  6. Use batch operations for multiple entities in same partition
  7. Use upsert_entity for idempotent writes
  8. Use parameterized queries to prevent injection
  9. Keep entities small — max 1MB per entity
  10. Use async client for high-throughput scenarios

Reference Files

FileContents
references/capabilities.mdAdditional non-hero capabilities, operation-group coverage, and production checklists.
references/non-hero-scenarios.mdDedicated non-hero examples for secondary/advanced scenarios.

© microsoft, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in .github/plugins/azure-sdk-python/skills/azure-data-tables-py of microsoft/skills.

  • SKILL.md
  • references/capabilities.md
  • references/non-hero-scenarios.md

Open the folder on GitHubat commit 354361d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in microsoft/skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Azure Data Tables Py next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Azure Data Tables Py compared with similar skills
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Questions about Azure Data Tables Py

What does Azure Data Tables Py do?

Azure Tables SDK for Python (Storage and Cosmos DB). An agent skill from microsoft/skills. Azure Data Tables Py is an agent skill from microsoft/skills, published by the product's own GitHub organization. Azure Tables SDK for Python (Storage and Cosmos DB).

When should I use Azure Data Tables Py?

Azure Data Tables Py fits situations like: noSQL key-value storage; batch operations.

How do I install Azure Data Tables Py in Claude Code?

Run `npx skills add microsoft/skills --skill azure-data-tables-py -a claude-code`. Or copy the skill folder (.github/plugins/azure-sdk-python/skills/azure-data-tables-py in microsoft/skills) into .claude/skills/azure-data-tables-py in your project. Claude Code loads it when a task matches its description.

How do I install Azure Data Tables Py in Codex?

Run `npx skills add microsoft/skills --skill azure-data-tables-py -a codex`. Or copy the skill folder (.github/plugins/azure-sdk-python/skills/azure-data-tables-py in microsoft/skills) into .agents/skills/azure-data-tables-py in your project. Codex loads it when a task matches its description.

Can I use Azure Data Tables Py in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add microsoft/skills --skill azure-data-tables-py -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/azure-data-tables-py, .gemini/skills/azure-data-tables-py, .github/skills/azure-data-tables-py and .opencode/skills/azure-data-tables-py in your project.

What does Azure Data Tables Py need to run?

Going by SKILL.md and its folder, Azure Data Tables Py needs the command-line tools its instructions call (pip) and credentials named AZURE_TOKEN_CREDENTIALS. Our summary lists: Python 3.

Does Azure Data Tables Py access the network?

SKILL.md names 1 domain. In commands or code: learn.microsoft.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Azure Data Tables Py safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Azure Data Tables Py use?

Azure Data Tables Py is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Azure Data Tables Py use?

About 2.1k tokens (SKILL.md is roughly 8.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 786 tokens, read only when the agent opens those files.

What are the alternatives to Azure Data Tables Py?

Skills that share tags, products or a category with Azure Data Tables Py: Cosmosdb Best Practices (microsoft/vscode-cosmosdb, 200 stars), Azure Data Tables Py (aiskillstore/marketplace, 430 stars), Azure Storage (microsoft/GitHub-Copilot-for-Azure, 255 stars) and Azure Cosmosdb (alinaqi/maggy, 707 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Azure Data Tables Py?

microsoft (a GitHub organization, an official publisher) maintains it in microsoft/skills, which has 3,091 GitHub stars. The repository holds 150 skills in this directory. The repository was last updated on October 6, 2026.

Source: microsoft/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.