Official agent skill

Azure Storage Queue Py

by microsoft in microsoft/skills

Azure Queue Storage SDK for Python. An agent skill from microsoft/skills.

OfficialMITAuto-check passedBackend & APIs

Install Azure Storage Queue Py

skills CLI
$ npx skills add microsoft/skills --skill azure-storage-queue-py -a claude-code

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

GitHub CLI
$ gh skill install microsoft/skills azure-storage-queue-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-storage-queue-py .claude/skills/azure-storage-queue-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-storage-queue-py
GitHub stars
3.1k
Used in
6 other repos
Token cost
~1.9k tokens
SKILL.md length
314 words
Files
3 (incl. references)
Skills in repo
150
Repo updated
First seen
Licence
MIT

At a glance

Azure Queue Storage SDK for Python. 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… → …
  • Reliable message queuing
  • SKILL.md covers Installation, Environment Variables, Authentication & Lifecycle and Queue Operations, plus 11 more sections
  • Calls pip; reaches learn.microsoft.com; needs AZURE_TOKEN_CREDENTIALS

What it does

Azure Storage Queue Py is an agent skill from microsoft/skills, published by the product's own GitHub organization. Azure Queue Storage SDK for Python. Use for reliable message queuing, task distribution, and asynchronous processing. Triggers: "queue storage", "QueueServiceClient", "QueueClient", "message queue", "dequeue".

Its SKILL.md is about 1.9k 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 Backend & APIs, covering Async programming and Event-driven systems. It works with Microsoft Azure, Python 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

  • Reliable message queuing
  • Task distribution
  • Asynchronous processing

Example prompts

  • “queue storage”
  • “QueueServiceClient”
  • “QueueClient”
  • “/azure-storage-queue-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.xxx sync clients with azure.xxx.aio async clients in the same call path. Choose…
  2. Always use context managers for clients and async credentials. Wrap every client in with Client(...) 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. Delete messages after processing to prevent reprocessing
  5. Set appropriate visibility timeout based on processing time
  6. Handle dequeue_count for poison message detection
  7. Use async client for high-throughput scenarios
  8. Use peek_messages for monitoring without affecting queue
  9. Set time_to_live to prevent stale messages
  10. Consider Service Bus for advanced features (sessions, topics)

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 Storage Queue Py loads about 1.9k tokens when it runs, and up to ~3.1k if it reads all its reference files. Until then it costs about 58 tokens; SKILL.md has 314 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~58
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.1k

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). 314 words, ~1,950 tokens.

Download SKILL.mdSave it as .claude/skills/azure-storage-queue-py/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
azure-storage-queue-py
description
Azure Queue Storage SDK for Python. Use for reliable message queuing, task distribution, and asynchronous processing. Triggers: "queue storage", "QueueServiceClient", "QueueClient", "message queue", "dequeue".
license
MIT
metadata.author
Microsoft
metadata.version
1.0.0
metadata.package
azure-storage-queue

Azure Queue Storage SDK for Python

Simple, cost-effective message queuing for asynchronous communication.

Installation

bash
pip install azure-storage-queue azure-identity

Environment Variables

bash
AZURE_STORAGE_ACCOUNT_URL=https://<account>.queue.core.windows.net  # Required for all auth methods
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
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.storage.queue import QueueServiceClient, QueueClient

# 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()
account_url = "https://<account>.queue.core.windows.net"

# Service client
with QueueServiceClient(account_url=account_url, credential=credential) as service_client:
    # Use service_client here (see following sections for operations)
    ...

# Queue client
with QueueClient(account_url=account_url, queue_name="myqueue", credential=credential) as queue_client:
    # Use queue_client here (see following sections for operations)
    ...

Queue Operations

python
# Create queue
service_client.create_queue("myqueue")

# Get queue client
queue_client = service_client.get_queue_client("myqueue")

# Delete queue
service_client.delete_queue("myqueue")

# List queues
for queue in service_client.list_queues():
    print(queue.name)

Send Messages

python
# Send message (string)
queue_client.send_message("Hello, Queue!")

# Send with options
queue_client.send_message(
    content="Delayed message",
    visibility_timeout=60,  # Hidden for 60 seconds
    time_to_live=3600       # Expires in 1 hour
)

# Send JSON
import json
data = {"task": "process", "id": 123}
queue_client.send_message(json.dumps(data))

Receive Messages

python
# Receive messages (makes them invisible temporarily)
messages = queue_client.receive_messages(
    messages_per_page=10,
    visibility_timeout=30  # 30 seconds to process
)

for message in messages:
    print(f"ID: {message.id}")
    print(f"Content: {message.content}")
    print(f"Dequeue count: {message.dequeue_count}")
    
    # Process message...
    
    # Delete after processing
    queue_client.delete_message(message)

Peek Messages

python
# Peek without hiding (doesn't affect visibility)
messages = queue_client.peek_messages(max_messages=5)

for message in messages:
    print(message.content)

Update Message

python
# Extend visibility or update content
messages = queue_client.receive_messages()
for message in messages:
    # Extend timeout (need more time)
    queue_client.update_message(
        message,
        visibility_timeout=60
    )
    
    # Update content and timeout
    queue_client.update_message(
        message,
        content="Updated content",
        visibility_timeout=60
    )

Delete Message

python
# Delete after successful processing
messages = queue_client.receive_messages()
for message in messages:
    try:
        # Process...
        queue_client.delete_message(message)
    except Exception:
        # Message becomes visible again after timeout
        pass

Clear Queue

python
# Delete all messages
queue_client.clear_messages()

Queue Properties

python
# Get queue properties
properties = queue_client.get_queue_properties()
print(f"Approximate message count: {properties.approximate_message_count}")

# Set/get metadata
queue_client.set_queue_metadata(metadata={"environment": "production"})
properties = queue_client.get_queue_properties()
print(properties.metadata)

Async Client

python
from azure.storage.queue.aio import QueueServiceClient, QueueClient
from azure.identity.aio import DefaultAzureCredential

async def queue_operations():
    credential = DefaultAzureCredential()
    
    async with QueueClient(
        account_url="https://<account>.queue.core.windows.net",
        queue_name="myqueue",
        credential=credential
    ) as client:
        # Send
        await client.send_message("Async message")
        
        # Receive
        async for message in client.receive_messages():
            print(message.content)
            await client.delete_message(message)

import asyncio
asyncio.run(queue_operations())

Base64 Encoding

python
from azure.storage.queue import QueueClient, BinaryBase64EncodePolicy, BinaryBase64DecodePolicy

# For binary data
with QueueClient(
    account_url=account_url,
    queue_name="myqueue",
    credential=credential,
    message_encode_policy=BinaryBase64EncodePolicy(),
    message_decode_policy=BinaryBase64DecodePolicy()
) as queue_client:
    # Send bytes
    queue_client.send_message(b"Binary content")

Best Practices

  1. Pick sync OR async and stay consistent. Do not mix azure.xxx sync clients with azure.xxx.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 Client(...) as client: (sync) or async with Client(...) 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. Delete messages after processing to prevent reprocessing
  5. Set appropriate visibility timeout based on processing time
  6. Handle dequeue_count for poison message detection
  7. Use async client for high-throughput scenarios
  8. Use peek_messages for monitoring without affecting queue
  9. Set time_to_live to prevent stale messages
  10. Consider Service Bus for advanced features (sessions, topics)

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-storage-queue-py of microsoft/skills.

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

Open the folder on GitHubat commit 354361d

Used in 6 other repositories

We found 16 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 6 other GitHub owners. This page covers the copy in microsoft/skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Azure Storage Queue 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 Storage Queue Py compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Azure Storage Queue Py this skillmicrosoft/skills3.1k6 repos~1.9kAutomated safety check: PassMIT
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Python Appservice Deploymicrosoft/GitHub-Copilot-for-Azure2551 repos~688Automated safety check: PassMIT
Azure Identity Pyaiskillstore/marketplace4305 repos~1.4kAutomated safety check: PassNone
Ak Cloud Deployyaalalabs/agent-kernel191—~14kAutomated safety check: PassApache-2.0
Azure Carbon OptimizationMicrosoftDocs/Agent-Skills775—~837Automated safety check: PassCC-BY-4.0

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Questions about Azure Storage Queue Py

What does Azure Storage Queue Py do?

Azure Queue Storage SDK for Python. An agent skill from microsoft/skills. Azure Storage Queue Py is an agent skill from microsoft/skills, published by the product's own GitHub organization. Azure Queue Storage SDK for Python.

When should I use Azure Storage Queue Py?

Azure Storage Queue Py fits situations like: reliable message queuing; task distribution; asynchronous processing.

How do I install Azure Storage Queue Py in Claude Code?

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

How do I install Azure Storage Queue Py in Codex?

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

Can I use Azure Storage Queue 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-storage-queue-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-storage-queue-py, .gemini/skills/azure-storage-queue-py, .github/skills/azure-storage-queue-py and .opencode/skills/azure-storage-queue-py in your project.

What does Azure Storage Queue Py need to run?

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

Does Azure Storage Queue 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 Storage Queue 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 Storage Queue Py use?

Azure Storage Queue 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 Storage Queue Py use?

About 1.9k tokens (SKILL.md is roughly 7.8k 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 1.1k tokens, read only when the agent opens those files.

What are the alternatives to Azure Storage Queue Py?

Skills that share tags, products or a category with Azure Storage Queue Py: Azure Prepare (microsoft/GitHub-Copilot-for-Azure, 255 stars), Python Appservice Deploy (microsoft/GitHub-Copilot-for-Azure, 255 stars), Azure Identity Py (aiskillstore/marketplace, 430 stars) and Ak Cloud Deploy (yaalalabs/agent-kernel, 191 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Azure Storage Queue Py?

microsoft (a GitHub organization, an official publisher) maintains it in microsoft/skills, which has 3,086 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.