--- name: openai--temporal--temporal-developer description: >- Develop, debug, and manage Temporal applications across Python, TypeScript, Go, and Java. 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. origin: "openai/plugins — temporal/temporal-developer (MIT)" license: MIT version: "0.1.0" compatibility: "yamtam-engine >= 0.14.0" --- # 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, and Java. ## 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` - Java -> read `references/java/java.md` - Go -> read `references/go/go.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.