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azure-storage-queue-pyAzure storage queue PY 搜索

Agent Skill

用于辅助云资源、部署、容器、基础设施和运维自动化任务。它适合让 Agent 检查配置、整理部署步骤、分析资源状态、生成排障思路或辅助云服务接入。使用时需要明确目标环境、账号权限、区域和资源组,区分本地测试与生产操作;涉及删除资源、重启服务、修改网络或权限配置时,应先确认影响范围。

总安装

1,211

周安装

52

GitHub Stars

35,717

下载量

424
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:azure-storage-queue-py(Azure storage queue PY 搜索)
来源仓库:https://github.com/sickn33/antigravity-awesome-skills
仓库路径:skills/azure-storage-queue-py
安装命令:
npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill azure-storage-queue-py
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill azure-storage-queue-py

简介

Python 实现的 Azure Queue Storage 消息队列 SDK。

  • 支持异步通信、任务分发与事件驱动架构。
  • 适用于解耦服务间调用与后台作业调度。
  • 需安装 azure-storage-queue 并设置账户 URL 与凭据。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

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 time_to_live 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.

适合场景

01

研究助手

02

事实核查

03

知识库问答

04

带来源的搜索总结

能力概览

能力 1

组合搜索和大模型调用

能力 2

支持多来源检索和总结

能力 3

强调引用来源和事实核查

能力 4

适合研究型 Agent 流程

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.16%
按下载量换算149

Claude

29.11%
按下载量换算123

Cursor

17.95%
按下载量换算76

Gemini CLI

10.03%
按下载量换算43

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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