sickn33/agentic-awesome-skills

azure-storage-queue-py

Azure Queue Storage SDK for Python. Use for reliable message queuing, task distribution, and asynchronous processing.

First seen Feb 25, 2026

Installation

$ npx skills add sickn33/agentic-awesome-skills --skill azure-storage-queue-py

Similar popular skills

Related neighbors and high-traction skills in the same topics — useful to compare before installing.

Also in this package

Other skills from sickn33/agentic-awesome-skills · top by installs.

npx skills add sickn33/agentic-awesome-skills

Browse all from sickn33/agentic-awesome-skills

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 46.2K
License LICENSE
Default branch main
Open issues 0
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 5,471 B
  • docs SUMMARY.md 147 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 72 installs

SKILL.md

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

Authentication

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
        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
queue_client = QueueClient(
    account_url=account_url,
    queue_name="myqueue",
    credential=credential,
    message_encode_policy=BinaryBase64EncodePolicy(),
    message_decode_policy=BinaryBase64DecodePolicy()
)

# Send bytes
queue_client.send_message(b"Binary content")

Best Practices

  1. Delete messages after processing to prevent reprocessing
  2. Set appropriate visibility timeout based on processing time
  3. Handle dequeue_count for poison message detection
  4. Use async client for high-throughput scenarios
  5. Use peek_messages for monitoring without affecting queue
  6. Set timetolive to prevent stale messages
  7. Consider Service Bus for advanced features (sessions, topics)

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.