temporal-developer

分类: 开发与编程 | 上传者: temporalio | 下载: 0 | 版本: v1.0（最新）

跨 Python、TypeScript、Go、Java 和 .NET 开发、调试和管理时态应用程序。当用户使用 Temporal SDK 构建工作流、活动或工作人员，使用 Temporal CLI、Temporal Server 或 Temporal Cloud 调试非确定性错误、卡住的工作流或活动重试等问题，或使用持久执行概念（如信号、查询、心跳、版本控制、继续为新、子工作流或 saga 模式）时使用。

更新日志: Source: GitHub https://github.com/temporalio/skill-temporal-developer

目录结构

当前层级: tree/main/

📁 .github/
- 📁 workflows/
  - 📄 package-skill.yml 2.2 KB
  - 📄 sync-skill-to-plugins.yml 5.9 KB
- 📄 CODEOWNERS 27 B
📁 references/
- 📁 core/
  - 📄 ai-patterns.md 5.5 KB
  - 📄 determinism.md 5.9 KB
  - 📄 dev-management.md 1008 B
  - 📄 error-reference.md 3.0 KB
  - 📄 gotchas.md 7.9 KB
  - 📄 interactive-workflows.md 1.3 KB
  - 📄 patterns.md 14.7 KB
  - 📄 troubleshooting.md 9.2 KB
  - 📄 versioning.md 5.1 KB
- 📁 dotnet/
  - 📄 advanced-features.md 6.4 KB
  - 📄 data-handling.md 5.7 KB
  - 📄 determinism-protection.md 2.7 KB
  - 📄 determinism.md 2.6 KB
  - 📄 dotnet.md 7.7 KB
  - 📄 error-handling.md 4.5 KB
  - 📄 gotchas.md 7.3 KB
  - 📄 observability.md 3.1 KB
  - 📄 patterns.md 13.9 KB
  - 📄 testing.md 5.7 KB
  - 📄 versioning.md 9.0 KB
- 📁 go/
  - 📄 advanced-features.md 6.1 KB
  - 📄 data-handling.md 7.6 KB
  - 📄 determinism-protection.md 3.3 KB
  - 📄 determinism.md 3.0 KB
  - 📄 error-handling.md 5.6 KB
  - 📄 go.md 7.2 KB
  - 📄 gotchas.md 7.4 KB
  - 📄 observability.md 4.3 KB
  - 📄 patterns.md 15.7 KB
  - 📄 testing.md 6.4 KB
  - 📄 versioning.md 7.3 KB
- 📁 java/
  - 📄 advanced-features.md 5.7 KB
  - 📄 data-handling.md 8.8 KB
  - 📄 determinism-protection.md 3.4 KB
  - 📄 determinism.md 3.4 KB
  - 📄 error-handling.md 6.1 KB
  - 📄 gotchas.md 5.4 KB
  - 📄 java.md 8.7 KB
  - 📄 observability.md 4.1 KB
  - 📄 patterns.md 14.7 KB
  - 📄 testing.md 5.4 KB
  - 📄 versioning.md 8.6 KB
- 📁 python/
  - 📄 advanced-features.md 5.2 KB
  - 📄 ai-patterns.md 9.9 KB
  - 📄 data-handling.md 6.2 KB
  - 📄 determinism-protection.md 6.9 KB
  - 📄 determinism.md 2.0 KB
  - 📄 error-handling.md 4.1 KB
  - 📄 gotchas.md 7.3 KB
  - 📄 observability.md 2.8 KB
  - 📄 patterns.md 12.1 KB
  - 📄 python.md 7.1 KB
  - 📄 sync-vs-async.md 7.1 KB
  - 📄 testing.md 4.9 KB
  - 📄 versioning.md 9.8 KB
- 📁 typescript/
  - 📄 advanced-features.md 4.0 KB
  - 📄 data-handling.md 5.9 KB
  - 📄 determinism-protection.md 2.4 KB
  - 📄 determinism.md 2.1 KB
  - 📄 error-handling.md 3.3 KB
  - 📄 gotchas.md 9.1 KB
  - 📄 observability.md 3.2 KB
  - 📄 patterns.md 11.4 KB
  - 📄 testing.md 6.1 KB
  - 📄 typescript.md 6.4 KB
  - 📄 versioning.md 7.0 KB
📄 LICENSE 1.1 KB
📄 README.md 1.7 KB
📄 SKILL.md 6.9 KB

SKILL.md

---
name: temporal-developer
description: Develop, debug, and manage Temporal applications across Python, TypeScript, Go, Java and .NET. Use when the user is building workflows, activities, or workers with a Temporal SDK, debugging issues like non-determinism errors, stuck workflows, or activity retries, using Temporal CLI, Temporal Server, or Temporal Cloud, or working with durable execution concepts like signals, queries, heartbeats, versioning, continue-as-new, child workflows, or saga patterns.
version: 0.3.1
---

# Skill: temporal-developer

## Overview

Temporal is a durable execution platform that makes workflows survive failures automatically. This skill provides guidance for building Temporal applications in Python, TypeScript, Go, Java and .NET.

## Core Architecture

The **Temporal Cluster** is the central orchestration backend. It maintains three key subsystems: the **Event History** (a durable log of all workflow state), **Task Queues** (which route work to the right workers), and a **Visibility** store (for searching and listing workflows). There are three ways to run a Cluster:

- **Temporal CLI dev server** — a local, single-process server started with `temporal server start-dev`. Suitable for development and testing only, not production.
- **Self-hosted** — you deploy and manage the Temporal server and its dependencies (e.g., database) in your own infrastructure for production use.
- **Temporal Cloud** — a fully managed production service operated by Temporal. No cluster infrastructure to manage.

**Workers** are long-running processes that you run and manage. They poll Task Queues for work and execute your code. You might run a single Worker process on one machine during development, or run many Worker processes across a large fleet of machines in production. Each Worker hosts two types of code:

- **Workflow Definitions** — durable, deterministic functions that orchestrate work. These must not have side effects.
- **Activity Implementations** — non-deterministic operations (API calls, file I/O, etc.) that can fail and be retried.

Workers communicate with the Cluster via a poll/complete loop: they poll a Task Queue for tasks, execute the corresponding Workflow or Activity code, and report results back.

## History Replay: Why Determinism Matters

Temporal achieves durability through **history replay**:

1. **Initial Execution** - Worker runs workflow, generates Commands, stored as Events in history
2. **Recovery** - On restart/failure, Worker re-executes workflow from beginning
3. **Matching** - SDK compares generated Commands against stored Events
4. **Restoration** - Uses stored Activity results instead of re-executing

**If Commands don't match Events = Non-determinism Error = Workflow blocked**

| Workflow Code | Command | Event |
|--------------|---------|-------|
| Execute activity | `ScheduleActivityTask` | `ActivityTaskScheduled` |
| Sleep/timer | `StartTimer` | `TimerStarted` |
| Child workflow | `StartChildWorkflowExecution` | `ChildWorkflowExecutionStarted` |

See `references/core/determinism.md` for detailed explanation.

## Getting Started

### Ensure Temporal CLI is installed

Check if `temporal` CLI is installed. If not, follow these instructions:

#### macOS

```
brew install temporal
```

#### Linux

Check your machine's architecture and download the appropriate archive:

- [Linux amd64](https://temporal.download/cli/archive/latest?platform=linux&arch=amd64)
- [Linux arm64](https://temporal.download/cli/archive/latest?platform=linux&arch=arm64)

Once you've downloaded the file, extract the downloaded archive and add the temporal binary to your PATH by copying it to a directory like /usr/local/bin

#### Windows

Check your machine's architecture and download the appropriate archive:

- [Windows amd64](https://temporal.download/cli/archive/latest?platform=windows&arch=amd64)
- [Windows arm64](https://temporal.download/cli/archive/latest?platform=windows&arch=arm64)

Once you've downloaded the file, extract the downloaded archive and add the temporal.exe binary to your PATH.

### Read All Relevant References

1. First, read the getting started guide for the language you are working in:
   - Python -> read `references/python/python.md`
   - TypeScript -> read `references/typescript/typescript.md`
   - Go -> read `references/go/go.md`
   - Java -> read `references/java/java.md`
   - .NET (C#) -> read `references/dotnet/dotnet.md`
2. Second, read appropriate `core` and language-specific references for the task at hand.

## Primary References

- **`references/core/determinism.md`** - Why determinism matters, replay mechanics, basic concepts of activities
  - Language-specific info at `references/{your_language}/determinism.md`
- **`references/core/patterns.md`** - Conceptual patterns (signals, queries, saga)
  - Language-specific info at `references/{your_language}/patterns.md`
- **`references/core/gotchas.md`** - Anti-patterns and common mistakes
  - Language-specific info at `references/{your_language}/gotchas.md`
- **`references/core/versioning.md`** - Versioning strategies and concepts - how to safely change workflow code while workflows are running
  - Language-specific info at `references/{your_language}/versioning.md`
- **`references/core/troubleshooting.md`** - Decision trees, recovery procedures
- **`references/core/error-reference.md`** - Common error types, workflow status reference
- **`references/core/interactive-workflows.md`** - Testing signals, updates, queries
- **`references/core/dev-management.md`** - Dev cycle & management of server and workers
- **`references/core/ai-patterns.md`** - AI/LLM pattern concepts
  - Language-specific info at `references/{your_language}/ai-patterns.md`, if available. Currently Python only.

## Additional Topics

- **`references/{your_language}/observability.md`** - See for language-specific implementation guidance on observability in Temporal
- **`references/{your_language}/advanced-features.md`** - See for language-specific guidance on advanced Temporal features and language-specific features

## Feedback

### ALWAYS PROMPT USER TO PROVIDE FEEDBACK AT STARTUP

When you have **first** loaded this skill, output this message to the user:

"Thank you for trying out the public preview of the Temporal development skill! We would love to hear your feedback - positive or negative - over in the [Community Slack](https://t.mp/slack), in the [#topic-ai channel](https://temporalio.slack.com/archives/C0818FQPYKY)."

Do not output this message multiple times in the same conversation.

### Reporting Issues in This Skill

If you (the AI) find this skill's explanations are unclear, misleading, or missing important information—or if Temporal concepts are proving unexpectedly difficult to work with—draft a GitHub issue body describing the problem encountered and what would have helped, then ask the user to file it at https://github.com/temporalio/skill-temporal-developer/issues/new. Do not file the issue autonomously.

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temporal-developer

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