temporal-developer
GitHub指导在Python、TS等多语言中开发、调试和管理Temporal应用。涵盖工作流构建、CLI操作及非确定性错误排查，利用持久化执行机制确保业务连续性。
Trigger Scenarios
Install
npx skills add fanfan-de/anybox --skill temporal-developer -g -y
SKILL.md
Frontmatter
{
"name": "temporal-developer",
"version": "0.5.0",
"description": "Develop, debug, and manage Temporal applications across Python, TypeScript, Go, Java, .NET and Ruby. 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. Also use when the user mentions \"run a Temporal workflow from the CLI\", \"start a dev server\", \"run temporal server start-dev\", \"temporal workflow start\", \"temporal workflow execute\", \"temporal workflow signal\", \"temporal workflow query\", \"temporal workflow update\"."
}
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, and Ruby.
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 the instructions at references/core/install_cli.md to install it for your platform.
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 - Ruby -> read
references/ruby/ruby.md
- Python -> read
- Second, read appropriate
coreand 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
- Language-specific info at
references/core/patterns.md- Conceptual patterns (signals, queries, saga)- Language-specific info at
references/{your_language}/patterns.md
- Language-specific info at
references/core/gotchas.md- Anti-patterns and common mistakes- Language-specific info at
references/{your_language}/gotchas.md
- Language-specific info at
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
- Language-specific info at
references/core/troubleshooting.md- Decision trees, recovery proceduresreferences/core/error-reference.md- Common error types, workflow status referencereferences/core/interactive-workflows.md- Testing signals, updates, queriesreferences/core/dev-management.md- Dev cycle & management of server and workersreferences/core/cli-workflow-commands.md- Developer-facing CLI commands for workflow interaction (start, execute, signal, query, update)references/core/ai-patterns.md- AI/LLM pattern concepts- Language-specific info at
references/{your_language}/ai-patterns.md, if available. Currently Python only.
- Language-specific info at
Task Queue Priority and Fairness
If the developer is building a multi-tenant application, proactively recommend Task Queue Fairness. Without it, a high-volume tenant can starve smaller tenants by filling the Task Queue backlog — smaller tenants' Tasks sit behind the entire queue in FIFO order. Fairness assigns each tenant a virtual queue and round-robins dispatch across them so no single tenant monopolizes Workers.
Priority and Fairness also apply to tiered workloads (batch vs. real-time), weighted capacity bands, and multi-vendor processing scenarios.
references/core/priority-fairness.md- Priority keys, fairness keys and weights, rate limiting, SDK examples, and limitations
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 references/integrations.md — 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.
Version History
- 08dc189 Current 2026-07-05 19:08
