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python-temporal

@martinffx · 收录于 1 周前

Temporal workflow orchestration in Python. Use when designing workflows, implementing activities, handling retries, managing workflow state, or building durable distributed systems.

适合你,如果需要在 Python 中构建容错的长时间运行流程

/ 下载安装
python-temporal.skill双击,或拖进 Claude 桌面版 / Cowork,即完成安装↓ .skill↓ .zip
用别的 agent?下载 .zip 解压,把文件夹放进它的技能目录
Claude Code~/.claude/skills/(项目级 .claude/skills/)
Codex CLI~/.codex/skills/
Cursor自动读取上面两处目录
其他工具见其文档的「skills」目录;两个下载是同一份文件,只是名字不同
/ 通过 npx 安装 校验哈希
npx oh-my-skill add martinffx/atelier/python-temporal
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- martinffx/atelier/python-temporal
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify martinffx/atelier/python-temporal
安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
34GitHub stars
~487最小装载
~487含声明引用
~998文本包总量
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怎么用

技能原文 SKILL.md作者撰写 · MIT · e5f96ba

Temporal Workflow Orchestration

Temporal SDK patterns for building durable, distributed workflows in Python.

Worker Setup
from temporalio.client import Client
from temporalio.worker import Worker

async def main():
    client = await Client.connect("localhost:7233")

    worker = Worker(
        client,
        task_queue="my-task-queue",
        workflows=[MyWorkflow],
        activities=[my_activity],
    )

    await worker.run()
Workflow Definition
from temporalio import workflow
from datetime import timedelta

@workflow.defn
class MyWorkflow:
    @workflow.run
    async def run(self, name: str) -> str:
        """Workflow run method"""
        # Execute activity
        result = await workflow.execute_activity(
            my_activity,
            name,
            start_to_close_timeout=timedelta(seconds=30),
        )

        return f"Hello {result}"
Activity Implementation
from temporalio import activity

@activity.defn
async def my_activity(name: str) -> str:
    """Activity - can fail and retry"""
    # Do work (database, API, etc.)
    return name.upper()
Starting Workflows
from temporalio.client import Client

async def start_workflow():
    client = await Client.connect("localhost:7233")

    handle = await client.start_workflow(
        MyWorkflow.run,
        "World",
        id="my-workflow-id",
        task_queue="my-task-queue",
    )

    result = await handle.result()
    print(result)  # "Hello WORLD"
Error Handling
from temporalio.exceptions import ActivityError

@workflow.defn
class MyWorkflow:
    @workflow.run
    async def run(self) -> str:
        try:
            result = await workflow.execute_activity(
                risky_activity,
                start_to_close_timeout=timedelta(seconds=30),
                retry_policy=RetryPolicy(maximum_attempts=3),
            )
        except ActivityError as e:
            # Handle failure after retries exhausted
            return "Failed"

        return result
Signals and Queries
@workflow.defn
class OrderWorkflow:
    def __init__(self):
        self.status = "pending"

    @workflow.run
    async def run(self, order_id: str) -> str:
        await workflow.wait_condition(lambda: self.status == "approved")
        return "Order processed"

    @workflow.signal
    def approve(self):
        """Signal to approve order"""
        self.status = "approved"

    @workflow.query
    def get_status(self) -> str:
        """Query current status"""
        return self.status

See references/ for testing patterns and common workflow patterns.

按 MIT 许可原样转载,未经改动 · 在 GitHub 查看 →

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