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 : Initial Execution - Worker runs workflow, generates Commands, stored as Events in history Recovery - On restart/failure, Worker re-executes workflow from beginning Matching - SDK compares generated Commands against stored Events 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 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 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 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.