* chore: upgrade maintenance scripts to robust PyYAML parsing - Replaces fragile regex frontmatter parsing with PyYAML/yaml library - Ensures multi-line descriptions and complex characters are handled safely - Normalizes quoting and field ordering across all maintenance scripts - Updates validator to strictly enforce description quality * fix: restore and refine truncated skill descriptions - Recovered 223+ truncated descriptions from git history (6.5.0 regression) - Refined long descriptions into concise, complete sentences (<200 chars) - Added missing descriptions for brainstorming and orchestration skills - Manually fixed imagen skill description - Resolved dangling links in competitor-alternatives skill * chore: sync generated registry files and document fixes - Regenerated skills index with normalized forward-slash paths - Updated README and CATALOG to reflect restored descriptions - Documented restoration and script improvements in CHANGELOG.md * fix: restore missing skill and align metadata for full 955 count - Renamed SKILL.MD to SKILL.md in andruia-skill-smith to ensure indexing - Fixed risk level and missing section in andruia-skill-smith - Synchronized all registry files for final 955 skill count * chore(scripts): add cross-platform runners and hermetic test orchestration * fix(scripts): harden utf-8 output and clone target writeability * fix(skills): add missing date metadata for strict validation * chore(index): sync generated metadata dates * fix(catalog): normalize skill paths to prevent CI drift * chore: sync generated registry files * fix: enforce LF line endings for generated registry files
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name, description, risk, source, date_added
| name | description | risk | source | date_added |
|---|---|---|---|---|
| azure-storage-queue-py | Azure Queue Storage SDK for Python. Use for reliable message queuing, task distribution, and asynchronous processing. | unknown | community | 2026-02-27 |
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
- 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)
When to Use
This skill is applicable to execute the workflow or actions described in the overview.