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, .NET, Ruby, and Rust.
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 Temporal determinism rules for detailed explanation.
Getting Started
Ensure Temporal CLI is installed
Check if temporal CLI is installed. If not, follow the instructions at Temporal CLI installation guide to install it for your platform.
Read All Relevant References
- First, read the getting started guide for the language you are working in:
- Second, read appropriate
core and language-specific references for the task at hand.
Primary References
- Temporal determinism rules - Why determinism matters, replay mechanics, basic concepts of activities
- Language-specific info at
references/{your_language}/determinism.md
- Temporal workflow patterns - Conceptual patterns (signals, queries, saga)
- Language-specific info at
references/{your_language}/patterns.md
- Temporal common pitfalls - Anti-patterns and common mistakes
- Language-specific info at
references/{your_language}/gotchas.md
- Temporal versioning guide - Versioning strategies and concepts - how to safely change workflow code while workflows are running
- Language-specific info at
references/{your_language}/versioning.md
- Temporal standalone Activities guide - Standalone Activities: run an Activity directly from a Client without a Workflow — Temporal's job queue
- Language-specific info at
references/{your_language}/standalone-activities.md
- Temporal Task Queue priority and fairness guide - Task Queue Priority and Fairness concepts, configuration, and limitations
- Language-specific info at
references/{your_language}/priority-fairness.md
- Temporal troubleshooting guide - Decision trees, recovery procedures
- Temporal error reference - Common error types, workflow status reference
- Temporal interactive workflow guide - Testing signals, updates, queries
- Temporal development management guide - Dev cycle & management of server and workers
- Temporal CLI workflow command guide - Developer-facing CLI commands for workflow interaction (start, execute, signal, query, update)
- Temporal AI integration patterns - AI/LLM pattern concepts
- Language-specific info at
references/{your_language}/ai-patterns.md, if available. Currently Python only.
Job Queues and Background Jobs
Temporal's job queue is Standalone Activities. When the developer asks for a job queue, background or async jobs, a work queue, or whether Temporal can replace Celery, Sidekiq, BullMQ, Resque, Hangfire, or SQS-plus-workers, build it with a Standalone Activity — not a Workflow wrapping a single Activity, and not a dispatcher Workflow that receives jobs by Signal.
Temporal Task Queues are the routing mechanism Workers poll, not a queue that producers push jobs into. Do not answer a job queue question by describing Temporal Task Queues.
When a developer says "task queue" they may mean "job queue": Celery, Dramatiq, Huey, and Asynq all use Task nomenclature, while Sidekiq, Hangfire, BullMQ, Resque, RQ, and Faktory use Job. Read "can I use Temporal as a task queue?" as a job queue question, and reserve Temporal's Task Queue meaning for your own reply.
- Temporal job queue guide - Job-queue vocabulary mapped to Temporal, migrating off an existing job queue, anti-patterns, and per-language SDK guides and runnable samples
Additional Topics
references/{your_language}/observability.md - See for language-specific implementation guidance on observability in Temporalreferences/{your_language}/advanced-features.md - See for language-specific guidance on advanced Temporal features and language-specific features
Third-Party Integrations
For Temporal plugins and integrations with third-party frameworks and SDKs (Spring Boot, Spring AI, OpenAI Agents SDK, Google ADK, etc.), see integrations catalog — a single catalog table with the language, what each integration does, and a pointer to its reference file under references/{language}/integrations/.
Feedback
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.