azure-storage-queue-py
Azure Queue Storage SDK for Python. Use for reliable message queuing, task distribution, and asynchronous processing. Triggers: "queue storage", "QueueServiceClient", "QueueClient", "message queue", "dequeue".
Install / Use
npx skills add microsoft/skills --skill azure-storage-queue-pyInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Development & EngineeringSupported Platforms
Our assessment of azure-storage-queue-py
azure-storage-queue-py scores 93/100 on our quality scale, 566th of 3,481 Development & Engineering skills we index (top 17%).
Its SKILL.md is 7.6 KB long, well organised into 37 sections with 13 code examples: a thorough specification that gives an agent plenty to work with.
With 3,051 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 5 days ago, so azure-storage-queue-py is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.
Automated pattern scan on 2026-09-29. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
azure-storage-queue-py compared with similar skills
All 4 of these similar skills score higher than azure-storage-queue-py; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| azure-storage-queue-py (this skill)by microsoft | 93 | 3.1k | 5d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 86.1k | 13d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 74.1k | today | CLAUDE.md |
| ai-job-searchby MadsLorentzen | 100 | 44.4k | today | CLAUDE.md |
| claude-howtoby luongnv89 | 100 | 41.7k | 2d ago | CLAUDE.md |
Frequently asked questions
- How do I install azure-storage-queue-py?
- Run
npx skills add microsoft/skills --skill azure-storage-queue-py. The install tabs above show the steps for each supported agent. - Which AI agents does azure-storage-queue-py work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is azure-storage-queue-py safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is azure-storage-queue-py still maintained?
- The repository was last updated 5 days ago, so azure-storage-queue-py is actively maintained.
Skill content
View source on GitHubname: 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 version: "1.0.0" package: azure-storage-queue
Azure Queue Storage SDK for Python
Simple, cost-effective message queuing for asynchronous communication.
Installation
pip install azure-storage-queue azure-identity
Environment Variables
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:
- 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:
DefaultAzureCredentialworks as-is.- Production: set
AZURE_TOKEN_CREDENTIALS=prod(orAZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.- 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:andasync with DefaultAzureCredential() as credential:(fromazure.identity.aio)Snippets may abbreviate this setup, but production code should always follow both rules.
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
# 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
# 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
# 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
# Peek without hiding (doesn't affect visibility)
messages = queue_client.peek_messages(max_messages=5)
for message in messages:
print(message.content)
Update Message
# 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
# 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
# Delete all messages
queue_client.clear_messages()
Queue Properties
# 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
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
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
- Pick sync OR async and stay consistent. Do not mix
azure.xxxsync clients withazure.xxx.aioasync clients in the same call path. Choose one mode per module. - Always use context managers for clients and async credentials. Wrap every client in
with Client(...) as client:(sync) orasync with Client(...) as client:(async). For asyncDefaultAzureCredentialfromazure.identity.aio, also useasync with credential:so tokens and transports are cleaned up. - Use
DefaultAzureCredentialfor portable auth across local dev and Azure (avoid connection strings / API keys when possible). - Delete messages after processing to prevent reprocessing
- Set appropriate visibility timeout based on processing time
- Handle
dequeue_countfor poison message detection - Use async client for high-throughput scenarios
- Use
peek_messagesfor monitoring without affecting queue - Set
time_to_liveto prevent stale messages - Consider Service Bus for advanced features (sessions, topics)
Reference Files
| File | Contents | |------|----------| | references/capabilities.md | Additional non-hero capabilities, operation-group coverage, and production checklists. | | references/non-hero-scenarios.md | Dedicated non-hero examples for secondary/advanced scenarios. |
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
