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Temporal Developer

OpenAIOfficialCreated by TemporalDistributed by OpenAI

  • Skill
  • Tool integration
  • Software development
  • MIT
  • Low risk
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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://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/java/versioning.md

Java SDK Versioning

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

Patching API

Workflow.getVersion()

Workflow.getVersion(String changeId, int minSupported, int maxSupported) returns the version to use for a given change:

import io.temporal.workflow.Workflow;

@WorkflowInterface
public interface ShippingWorkflow {
    @WorkflowMethod
    void run();
}

public class ShippingWorkflowImpl implements ShippingWorkflow {
    @Override
    public void run() {
        int version = Workflow.getVersion(
            "send-email-instead-of-fax",
            Workflow.DEFAULT_VERSION,  // minSupported (no change)
            1                          // maxSupported (current version)
        );

        if (version == 1) {
            // New code path
            Workflow.newActivityStub(MyActivities.class, options).sendEmail();
        } else {
            // Old code path (for replay of existing workflows)
            Workflow.newActivityStub(MyActivities.class, options).sendFax();
        }
    }
}

How it works:

  • For new executions: returns maxSupported and records a marker in history

  • For replay with the marker: returns the recorded version

  • For replay without the marker: returns DEFAULT_VERSION (-1)

Three-Step Patching Process

Step 1: Patch in New Code

Add the version check with both old and new code paths:

public class OrderWorkflowImpl implements OrderWorkflow {
    @Override
    public String run(Order order) {
        int version = Workflow.getVersion(
            "add-fraud-check",
            Workflow.DEFAULT_VERSION,
            1);

        if (version >= 1) {
            activities.checkFraud(order);
        }

        return activities.processPayment(order);
    }
}

Step 2: Remove Old Code Path

Once all pre-patch Workflow Executions have completed, remove the old branch and set minSupported to 1:

public class OrderWorkflowImpl implements OrderWorkflow {
    @Override
    public String run(Order order) {
        Workflow.getVersion("add-fraud-check", 1, 1);

        activities.checkFraud(order);
        return activities.processPayment(order);
    }
}

Step 3: Remove the Patch

After all workflows with the patch marker have completed, remove the getVersion call entirely:

public class OrderWorkflowImpl implements OrderWorkflow {
    @Override
    public String run(Order order) {
        activities.checkFraud(order);
        return activities.processPayment(order);
    }
}
Recording TemporalChangeVersion Search Attribute

Unlike the Python and TypeScript SDKs, the Java SDK does not automatically record the TemporalChangeVersion search attribute. You must manually upsert it:

import io.temporal.workflow.Workflow;
import io.temporal.common.SearchAttributeKey;
import java.util.List;

public class OrderWorkflowImpl implements OrderWorkflow {
    private static final SearchAttributeKey<List<String>> TEMPORAL_CHANGE_VERSION =
        SearchAttributeKey.forKeywordList("TemporalChangeVersion");

    @Override
    public String run(Order order) {
        int version = Workflow.getVersion("add-fraud-check", Workflow.DEFAULT_VERSION, 1);

        // Manually record for query filtering
        Workflow.upsertTypedSearchAttributes(
            TEMPORAL_CHANGE_VERSION.valueSet(List.of("add-fraud-check-1")));

        if (version >= 1) {
            activities.checkFraud(order);
        }
        return activities.processPayment(order);
    }
}

Query with:

temporal workflow list --query \
  'TemporalChangeVersion = "add-fraud-check-1" AND ExecutionStatus = "Running"'

Workflow Type Versioning

For incompatible changes, create a new Workflow Type:

@WorkflowInterface
public interface PizzaWorkflow {
    @WorkflowMethod
    String run(PizzaOrder order);
}

// Original implementation
public class PizzaWorkflowImpl implements PizzaWorkflow {
    @Override
    public String run(PizzaOrder order) {
        return processOrderV1(order);
    }
}

// New workflow type for incompatible changes
@WorkflowInterface
public interface PizzaWorkflowV2 {
    @WorkflowMethod
    String run(PizzaOrder order);
}

public class PizzaWorkflowV2Impl implements PizzaWorkflowV2 {
    @Override
    public String run(PizzaOrder order) {
        return processOrderV2(order);
    }
}

Register both with the Worker:

worker.registerWorkflowImplementationTypes(
    PizzaWorkflowImpl.class,
    PizzaWorkflowV2Impl.class);

Start new workflows with the new type:

PizzaWorkflowV2 workflow = client.newWorkflowStub(
    PizzaWorkflowV2.class,
    WorkflowOptions.newBuilder()
        .setTaskQueue("pizza-task-queue")
        .build());
workflow.run(order);

Check for open executions before removing the old type:

temporal workflow list --query 'WorkflowType = "PizzaWorkflow" AND ExecutionStatus = "Running"'

Worker Versioning

Worker Versioning manages versions at the deployment level. Available since Java SDK v1.29.

Key Concepts

  • Worker Deployment: A logical group of Workers processing the same Task Queue, identified by a deployment name (e.g., "order-service").

  • Worker Deployment Version: A specific version within a deployment, identified by the combination of deployment name and Build ID (e.g., "order-service:v1.0.0"). Each version corresponds to a particular code revision.

Configuring Workers
import io.temporal.worker.Worker;
import io.temporal.worker.WorkerFactory;
import io.temporal.worker.WorkerOptions;
import io.temporal.worker.WorkerDeploymentOptions;
import io.temporal.worker.WorkerDeploymentVersion;

WorkerDeploymentVersion version = WorkerDeploymentVersion.newBuilder()
    .setDeploymentName("order-service")
    .setBuildId("v1.0.0")  // or git commit hash
    .build();

WorkerDeploymentOptions deploymentOptions = WorkerDeploymentOptions.newBuilder()
    .setVersion(version)
    .setUseWorkerVersioning(true)
    .build();

WorkerFactory factory = WorkerFactory.newInstance(client);
Worker worker = factory.newWorker(
    "my-task-queue",
    WorkerOptions.newBuilder()
        .setDeploymentOptions(deploymentOptions)
        .build());

worker.registerWorkflowImplementationTypes(MyWorkflowImpl.class);
worker.registerActivitiesImplementations(new MyActivitiesImpl());
factory.start();
PINNED vs AUTO_UPGRADE Behaviors

Set the versioning behavior on the workflow definition:

import io.temporal.workflow.VersioningBehavior;
import io.temporal.workflow.Workflow;

public class MyWorkflowImpl implements MyWorkflow {
    @Override
    public String run(String input) {
        Workflow.setVersioningBehavior(VersioningBehavior.PINNED);
        // ... workflow logic
    }
}

PINNED: Workflow stays on the Worker version that started it. Use for short-running workflows or when consistency within a single execution is critical. New workflows start on the current version; existing ones stay put.

AUTO_UPGRADE: Workflow moves to the latest Worker version on the next Workflow Task. Use for long-running workflows that need bug fixes or feature updates. Combine with Workflow.getVersion() patching to handle version transitions safely.

Deployment Strategies

Blue-Green: Run two deployment versions simultaneously. Set the new version as the current deployment. PINNED workflows finish on the old version; new workflows start on the new version. Drain the old version once all its workflows complete.

Rainbow: Run multiple versions concurrently for gradual rollouts. Each version handles its own workflows. Useful when you have many long-running PINNED workflows across several code revisions.

Querying Workflows by Worker Version
# List workflows running on a specific version
temporal workflow list --query \
  'TemporalWorkerDeploymentVersion = "order-service:v1.0.0" AND ExecutionStatus = "Running"'

# Count workflows per version to monitor drain progress
temporal workflow count --query \
  'TemporalWorkerDeploymentVersion = "order-service:v1.0.0" AND ExecutionStatus = "Running"'

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 Workflow.getInfo() 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

Workflow.getInfo().isTargetWorkerDeploymentVersionChanged() returns 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, call Workflow.continueAsNew with a ContinueAsNewOptions whose InitialVersioningBehavior is AUTO_UPGRADE so the new run starts on the Target Version of its Worker Deployment.

import io.temporal.common.InitialVersioningBehavior;
import io.temporal.workflow.ContinueAsNewOptions;
import io.temporal.workflow.Workflow;

// At a natural Workflow Task boundary, e.g. before accepting Updates,
// starting Activities, starting child Workflows, etc.:
if (Workflow.getInfo().isTargetWorkerDeploymentVersionChanged()) {
  Workflow.continueAsNew(
      ContinueAsNewOptions.newBuilder()
          .setInitialVersioningBehavior(InitialVersioningBehavior.AUTO_UPGRADE)
          .build(),
      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 isTargetWorkerDeploymentVersionChanged.

  • 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

  1. Check for open executions before removing old code paths

  2. Use descriptive change IDs that explain the change (e.g., "add-fraud-check" not "patch-1")

  3. Deploy patches incrementally: patch, remove old path, remove getVersion

  4. Manually upsert TemporalChangeVersion search attribute when using getVersion if you need query filtering

  5. Use PINNED for short workflows to simplify version management

  6. Use AUTO_UPGRADE with patching for long-running workflows that need updates

  7. Generate Build IDs from code (git hash) to ensure changes produce new versions

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

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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

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