MR
Mayur Rathi
@mayurrathi
⭐ 40.7k GitHub stars

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.

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Last verified on: 2026-07-08

Quick Facts

Category code
Works With Claude
Source sickn33/antigravity-awesome-skills
Stars ⭐ 40.7k
Last Verified 2026-07-08
Risk Level Low
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


bash
pip install azure-storage-queue azure-identity

Environment Variables


bash
AZURE_STORAGE_ACCOUNT_URL=https://<account>.queue.core.windows.net

Authentication


python
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


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 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. 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. 2

    Load into Your AI Assistant

    Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 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. 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.

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