AutoGineer
Generated banner for Temporal Developer
Generated logo for Temporal Developer

Temporal Developer

OpenAIOfficialCreated by TemporalDistributed by OpenAI

  • Skill
  • Tool integration
  • Software development
  • MIT
  • Low risk
Download ZIP

From the author

Develop, debug, and manage Temporal applications across Python, TypeScript, Go, Java, .NET, Ruby, and Rust. 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".
— the description by Temporal

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Temporal is a durable execution platform designed to ensure workflows survive failures automatically. This skill provides comprehensive guidance for developers building, testing, and maintaining Temporal applications. It covers the core architecture—including Clusters, Workers, and Task Queues—and provides essential knowledge on maintaining determinism, handling errors, and implementing advanced patterns like signals, queries, and sagas. The skill supports development across Python, TypeScript, Go, Java, .NET, Ruby, and Rust, offering specific references for each language.

What it does

  • Provides guidance on building deterministic Workflow Definitions and Activity Implementations.
  • Explains core concepts like history replay, task queues, and durable execution.
  • Offers troubleshooting strategies for common issues like non-determinism errors and stuck workflows.
  • Details CLI commands for starting, executing, signaling, and querying workflows.
  • Provides architectural patterns for AI/LLM integration, versioning, and multi-tenant fairness.
  • Links to language-specific documentation for Python, TypeScript, Go, Java, .NET, Ruby, and Rust.

When to use it

  • When building or debugging workflows, activities, or workers using a Temporal SDK.
  • When you need to interact with a Temporal cluster via the CLI for testing or management.
  • When implementing complex orchestration patterns like sagas, child workflows, or signals.
  • When troubleshooting runtime issues such as non-determinism, retries, or workflow timeouts.
  • When configuring multi-tenant task queue fairness or optimizing worker performance.

Advantages

  • Ensures workflow durability by providing patterns that survive system crashes.
  • Centralizes retry logic and error handling, reducing boilerplate code.
  • Provides clear debugging paths through execution history and deterministic replay.
  • Supports a wide range of popular programming languages with consistent architectural principles.
  • Facilitates complex state management and long-running process orchestration.

Usage examples

“How do I start a new workflow from the command line?”
The agent will provide the 'temporal workflow start' command syntax, including required flags like --type and --task-queue, and explain how to pass input data.
“I am getting a non-determinism error in my workflow. How do I fix it?”
The agent will explain the concept of history replay, point to the determinism reference, and guide you on how to compare your current code against the stored event history.
“How should I handle LLM API calls within a Temporal workflow?”
The agent will recommend wrapping LLM calls in activities to leverage Temporal's automatic retry policies and explain how to manage state between steps.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/openai/temporal-developer/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip temporal-developer.zip -d .agents/skills

Documentation by Temporal

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:

  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 CodeCommandEvent
Execute activityScheduleActivityTaskActivityTaskScheduled
Sleep/timerStartTimerTimerStarted
Child workflowStartChildWorkflowExecutionChildWorkflowExecutionStarted

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

  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

    • 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

    • Rust -> read references/rust/rust.md (in Public Preview)

  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/standalone-activities.md - Standalone Activities: run an Activity directly from a Client without a Workflow (Public Preview)

    • Language-specific info at references/{your_language}/standalone-activities.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/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.

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 Temporal

  • references/{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.

Files

references/typescript/integrations/mastra.md

Temporal Mastra Integration (TypeScript)

Overview

Mastra is a TypeScript agent / workflow framework. The @mastra/temporal package transforms Mastra workflow and step definitions into Temporal Workflows and Activities at build time, then auto-registers them on a Temporal Worker via the MastraPlugin. Each createStep becomes a Temporal Activity and each createWorkflow becomes a Temporal Workflow.

Mastra appears on the Temporal TypeScript integrations page as the "Mastra | Agent framework" row, which links out to the upstream Mastra deployment guide.

[!NOTE] This feature is in Public Preview. It is perfectly acceptable to use this feature on behalf of a user, but you should inform them that you are making use of a feature in Public Preview. The upstream @mastra/temporal package is also flagged as "experimental and not ready for production use"; the API may change between releases.

For Temporal TypeScript SDK fundamentals (Worker, Workflow, Activity, Task Queue, replay), see references/typescript/typescript.md and references/typescript/determinism.md.

Install

npm install @mastra/temporal@latest @temporalio/client @temporalio/worker @temporalio/envconfig

pnpm, yarn, and bun equivalents are all supported.

Initialize the integration

Wire up a Temporal Client once at module scope and pass it to init() from @mastra/temporal. init() returns Mastra's createWorkflow and createStep factories bound to that client and task queue.

// src/temporal.ts
import { init } from '@mastra/temporal'
import { Client, Connection } from '@temporalio/client'
import { loadClientConnectConfig } from '@temporalio/envconfig'

const config = loadClientConnectConfig()
const connection = await Connection.connect(config.connectionOptions)
const client = new Client({ connection })

export const { createWorkflow, createStep } = init({
  client,
  taskQueue: 'mastra',
})

init() parameters:

  • client — a @temporalio/client Client instance.

  • taskQueue — the Task Queue name Mastra-derived Workflows and Activities run on. The same value must be passed to the Worker (below).

  • startToCloseTimeout — optional. Maximum activity runtime. Default: 1 minute. Accepts string values like '5 minutes'.

loadClientConnectConfig() from @temporalio/envconfig reads TEMPORAL_ADDRESS, TEMPORAL_NAMESPACE, and TEMPORAL_API_KEY from the environment.

Define a step and a workflow

Use the bound createStep and createWorkflow from src/temporal.ts. Each createStep becomes a Temporal Activity; each createWorkflow becomes a Temporal Workflow.

// src/mastra/workflows.ts
import { z } from 'zod'
import { createWorkflow, createStep } from '../temporal'

const incrementStep = createStep({
  id: 'increment',
  inputSchema: z.object({
    value: z.number(),
  }),
  outputSchema: z.object({
    value: z.number(),
  }),
  execute: async ({ inputData }) => {
    return { value: inputData.value + 1 }
  },
})

const workflow = createWorkflow({
  id: 'increment-workflow',
  steps: [incrementStep],
  inputSchema: z.object({
    value: z.number(),
  }),
  outputSchema: z.object({
    value: z.number(),
  }),
}).then(incrementStep)

workflow.commit()

export { workflow as incrementWorkflow }

  • Workflow id must be a static string literal. The build-time transformer derives each Workflow's Temporal export name from this id, so it cannot be a variable, template, or computed value.

  • Call workflow.commit() before exporting. Workflows without .commit() are not picked up by the build-time transformer.

Register workflows with Mastra

// src/mastra/index.ts
import { Mastra } from '@mastra/core'
import { PinoLogger } from '@mastra/loggers'
import { incrementWorkflow } from './workflows'

export const mastra = new Mastra({
  workflows: { incrementWorkflow },
  logger: new PinoLogger({ name: 'Mastra', level: 'info' }),
})

Worker

Construct a MastraPlugin from @mastra/temporal/worker, run its build-time prebuild step pointing at the Mastra entry file, then pass the plugin into Worker.create({ plugins: [...] }).

// src/mastra/worker.ts
import { MastraPlugin } from '@mastra/temporal/worker'
import { NativeConnection, Worker } from '@temporalio/worker'

const connection = await NativeConnection.connect({
  address: 'localhost:7233',
})

const mastraPlugin = new MastraPlugin()

await mastraPlugin.prebuild({
  entryFile: import.meta.resolve('./index.ts'),
})

const worker = await Worker.create({
  connection,
  namespace: 'default',
  taskQueue: 'mastra',
  plugins: [mastraPlugin],
})

await worker.run()

  • Don't pass activities to Worker.create. MastraPlugin auto-registers every Activity derived from createStep after prebuild runs.

  • taskQueue on the Worker must match the taskQueue passed to init(). Both sides target the same queue.

  • prebuild({ entryFile }) is a build-time transform. It must complete before Worker.create; pass the path to the Mastra entry file (src/mastra/index.ts above).

Run a workflow

Resolve the workflow off the configured mastra instance and start a run. The bound client routes execution through Temporal.

// scripts/run.ts
import { mastra } from '../src/mastra'

const run = await mastra.getWorkflow('incrementWorkflow').createRun()
const result = await run.start({ inputData: { value: 5 } })

console.log(result)

Local development

docker run --rm -p 7233:7233 -p 8080:8080 temporalio/auto-setup:latest

Run the worker in another terminal:

npx tsx src/mastra/worker.ts

The Temporal UI is available at http://localhost:8080.

Environment variables

@temporalio/envconfig's loadClientConnectConfig() consumes these variables when wiring the Client connection:

  • TEMPORAL_ADDRESS

  • TEMPORAL_NAMESPACE

  • TEMPORAL_API_KEY

Hard constraints

  • Workflow id must be a static string literal. Pass a literal to createWorkflow({ id: 'my-workflow', ... }); the build-time transformer derives the Temporal export name from it.

  • Don't pass activities to Worker.create. MastraPlugin auto-registers Activities; manual registration conflicts with the plugin.

  • mastraPlugin.prebuild({ entryFile }) must run before Worker.create. The transform produces the Workflow and Activity definitions the Worker hosts.

  • Temporal Workers require a long-lived process. Don't deploy the worker to serverless platforms that hibernate between requests.

Common mistakes

  • Importing MastraPlugin from @mastra/temporal instead of @mastra/temporal/worker.

  • Passing activities to Worker.create alongside MastraPlugin.

  • Forgetting workflow.commit() after .then(step) — the transformer skips uncommitted workflows.

  • Using a computed id on createWorkflow — breaks the build-time transformer's Temporal export naming.

  • Mismatching taskQueue between init() and Worker.create.

  • Calling MastraPlugin without first running prebuild({ entryFile }).

Out of scope

The upstream Mastra Temporal guide covers createWorkflow and createStep only. Mastra Agents, Tools, Memory, RAG, evals, and Mastra Studio are not documented as participating in this Temporal integration.

For language-agnostic AI/LLM orchestration patterns (centralized retries, tool placement, multi-agent), see references/core/ai-patterns.md.

Resources

Requirements and compatibility

Written for: OpenAI Codex

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

Security

What our scanner noticed· Low risk

  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Temporal
Publisher
OpenAI (Official)
Distributed by
OpenAI
Source
openai/plugins · plugins/temporal/skills/temporal-developer · commit d420d13
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from plugins/temporal/LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Temporal or OpenAI.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download temporal-developer.zip