Azure Storage Queue Py
Azure Storage Queue Py is an code AI skill with a core value of |. It
helps developers solve real-world problems in the code domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
|
Quick Facts
mkdir -p ./skills/azure-storage-queue-py && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/azure-storage-queue-py/SKILL.md -o ./skills/azure-storage-queue-py/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Azure Queue Storage SDK for Python
Simple, cost-effective message queuing for asynchronous communication.
Installation
pip install azure-storage-queue azure-identityEnvironment Variables
AZURE_STORAGE_ACCOUNT_URL=https://<account>.queue.core.windows.netAuthentication
from azure.identity import DefaultAzureCredential
from azure.storage.queue import QueueServiceClient, QueueClient
credential = DefaultAzureCredential()
account_url = "https://<account>.queue.core.windows.net"
# Service client
service_client = QueueServiceClient(account_url=account_url, credential=credential)
# Queue client
queue_client = QueueClient(account_url=account_url, queue_name="myqueue", credential=credential)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
passClear 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 i🎯 Best For
- Claude users
- Software engineers
- Development teams
- Tech leads
💡 Use Cases
- Code quality improvement
- Best practice enforcement
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Azure Storage Queue Py to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Is Azure Storage Queue Py compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for Azure Storage Queue Py?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install Azure Storage Queue Py?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/azure-storage-queue-py/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.