name: 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.

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

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
• Linux 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
• Windows 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

1. 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, in the #topic-ai channel."

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.