Temporal Developer

OpenAIOfficialCreated by TemporalDistributed by OpenAI

Skill
Tool integration
Software development
MIT
Low risk

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

Download the skill as a ZIP:
https://autogineer.com/agent-skills/openai/temporal-developer/download

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:

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

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references/typescript/versioning.md

TypeScript SDK Versioning

For conceptual overview and guidance on choosing an approach, see references/core/versioning.md.

Patching API

The Patching API lets you change Workflow Definitions without causing non-deterministic behavior in running Workflows.

The patched() Function

The patched() function takes a patchId string and returns a boolean:

import { patched } from '@temporalio/workflow';

export async function myWorkflow(): Promise<void> {
  if (patched('my-change-id')) {
    // New code path
    await newImplementation();
  } else {
    // Old code path (for replay of existing executions)
    await oldImplementation();
  }
}

How it works:

If the Workflow is running for the first time, patched() returns true and inserts a marker into the Event History
During replay, if the history contains a marker with the same patchId, patched() returns true
During replay, if no matching marker exists, patched() returns false

TypeScript-specific behavior: Unlike Python/.NET/Ruby, patched() is not memoized when it returns false. This means you can use patched() in loops. However, if a single patch requires coordinated behavioral changes at different points in your workflow, you may need to manually memoize the result:

const useNewBehavior = patched('my-change');
// Use useNewBehavior at multiple points in workflow

Three-Step Patching Process

Patching is a three-step process for safely deploying changes.

Warning: Failing to follow this process correctly will result in non-determinism errors for in-flight workflows.

Step 1: Patch in New Code

Add the patch alongside the old code:

import { patched } from '@temporalio/workflow';

// Original code sent fax notifications
export async function shippingConfirmation(): Promise<void> {
  if (patched('changedNotificationType')) {
    await sendEmail();  // New code
  } else {
    await sendFax();    // Old code for replay
  }
  await sleep('1 day');
}

Step 2: Deprecate the Patch

Once all Workflows using the old code have completed, deprecate the patch:

import { deprecatePatch } from '@temporalio/workflow';

export async function shippingConfirmation(): Promise<void> {
  deprecatePatch('changedNotificationType');
  await sendEmail();
  await sleep('1 day');
}

The deprecatePatch() function records a marker that does not fail replay when Workflow code does not emit it, allowing a transition period.

Step 3: Remove the Patch

After all Workflows using deprecatePatch have completed, remove it entirely:

export async function shippingConfirmation(): Promise<void> {
  await sendEmail();
  await sleep('1 day');
}

Query Filters for Versioned Workflows

Use List Filters to find Workflows by version:

# Find running Workflows with a specific patch
WorkflowType = "shippingConfirmation" AND ExecutionStatus = "Running" AND TemporalChangeVersion = "changedNotificationType"

# Find running Workflows without the patch (started before patching)
WorkflowType = "shippingConfirmation" AND ExecutionStatus = "Running" AND TemporalChangeVersion IS NULL

Workflow Type Versioning

An alternative to patching is creating new Workflow functions for incompatible changes:

// Original Workflow
export async function pizzaWorkflow(order: PizzaOrder): Promise<OrderConfirmation> {
  // Original implementation
}

// New version with incompatible changes
export async function pizzaWorkflowV2(order: PizzaOrder): Promise<OrderConfirmation> {
  // Updated implementation
}

const worker = await Worker.create({
  workflowsPath: require.resolve('./workflows'), // Use workflowBundle for production
  taskQueue: 'pizza-queue',
});

Update client code to start new Workflows with the new type:

// Start new executions with V2
await client.workflow.start(pizzaWorkflowV2, {
  workflowId: 'order-123',
  taskQueue: 'pizza-queue',
  args: [order],
});

Use List Filters to check for remaining V1 executions:

WorkflowType = "pizzaWorkflow" AND ExecutionStatus = "Running"

After all V1 executions complete, remove the old Workflow function.

Worker Versioning

Worker Versioning allows multiple Worker versions to run simultaneously, routing Workflows to specific versions without code-level patching. Workflows are pinned to the Worker Deployment Version they started on.

Key Concepts

Worker Deployment: A logical name for your application (e.g., "order-service")
Worker Deployment Version: A specific build identified by deployment name + Build ID
Workflow Pinning: Workflows complete on the Worker Deployment Version they started on

Configuring Workers for Versioning

import { Worker, NativeConnection } from '@temporalio/worker';

const worker = await Worker.create({
  workflowsPath: require.resolve('./workflows'),  // Use workflowBundle for production
  taskQueue: 'my-queue',
  connection: await NativeConnection.connect({ address: 'temporal:7233' }),
  workerDeploymentOptions: {
    useWorkerVersioning: true,
    version: {
      deploymentName: 'order-service',
      buildId: '1.0.0',  // Git hash, semver, build number, etc.
    },
  },
});

Configuration options:

useWorkerVersioning: Enables Worker Versioning
version.deploymentName: Logical name for your service (consistent across versions)
version.buildId: Unique identifier for this build

Deployment Workflow

Deploy new Worker version with a new buildId

Use the Temporal CLI to set the new version as current:

temporal worker deployment set-current-version \
  --deployment-name order-service \
  --build-id 2.0.0

New Workflows start on the new version
Existing Workflows continue on their original version until completion
Decommission old Workers once all their Workflows complete

When to Use Worker Versioning

Worker Versioning is best suited for:

Short-running Workflows: Old Workers only need to run briefly during deployment transitions
Frequent deployments: Eliminates the need for code-level patching on every change
Blue-green deployments: Run old and new versions simultaneously with traffic control

For long-running Workflows, consider combining Worker Versioning with the Patching API, or use Continue-as-New to move Workflows to newer versions.

Upgrading on Continue-as-New

[!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.

For long-running Pinned Workflows that use Continue-as-New, detect a new Target Worker Deployment Version on workflowInfo() and continue-as-new with InitialVersioningBehavior.AUTO_UPGRADE so the new run starts on the Target Version. See references/core/versioning.md for the conceptual model.

Detecting the Target Version change

workflowInfo().targetWorkerDeploymentVersionChanged is true when a new Current or Ramping Version is available for this Workflow's Worker Deployment. The flag is refreshed after each Workflow Task completes.

Check the flag from code that runs as part of a Workflow Task — for example, before accepting an Update, starting an Activity, or starting a child Workflow.

Continue-as-new with upgrade

When the flag is set, build the Continue-as-New function with makeContinueAsNewFunc, passing initialVersioningBehavior: InitialVersioningBehavior.AUTO_UPGRADE, so the new run starts on the Target Version of its Worker Deployment.

import * as wf from '@temporalio/workflow';
import { InitialVersioningBehavior } from '@temporalio/common';

// At a natural Workflow Task boundary, e.g. before accepting Updates,
// starting Activities, starting child Workflows, etc.:
if (wf.workflowInfo().targetWorkerDeploymentVersionChanged) {
  const continueAsNew = wf.makeContinueAsNewFunc<typeof myWorkflow>({
    initialVersioningBehavior: InitialVersioningBehavior.AUTO_UPGRADE,
  });
  await continueAsNew(nextInput);
}

[!IMPORTANT] Don't busy-poll the flag on a timer. Check it at a natural Workflow Task boundary — before accepting Updates, starting Activities, starting child Workflows, etc. For idle Workflows, send a Signal to wake them so they can check it (see Limitations).

Limitations

Lazy moving only — idle Workflows do not upgrade. Send a Signal to wake an idle Workflow so it can check targetWorkerDeploymentVersionChanged.
Workflow input must remain compatible across versions. The new version's Workflow definition must accept the previous version's input; otherwise the new run may fail on its first Workflow Task.
Pinned Workflow Types only. Auto-Upgrade Workflows move at Workflow Task boundaries already; the upgrade-on-CaN pattern adds nothing for them.

Best Practices

Use descriptive patchId names that explain the change
Follow the three-step patching process completely before removing patches
Use List Filters to verify no running Workflows before removing version support
Keep Worker Deployment names consistent across all versions
Use unique, traceable Build IDs (git hashes, semver, timestamps)
Test version transitions with replay tests before deploying

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

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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

2026-11-09

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