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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://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/ruby/versioning.md

Ruby SDK Versioning

For conceptual overview, see references/core/versioning.md.

Patching API

The patched() Method

Temporalio::Workflow.patched('my-patch') returns true/false to branch between new and old code paths:

class MyWorkflow < Temporalio::Workflow::Definition
  def execute
    if Temporalio::Workflow.patched('my-patch')
      # New code path
      Temporalio::Workflow.execute_activity(
        PostPatchActivity,
        start_to_close_timeout: 100
      )
    else
      # Old code path (for replay of existing workflows)
      Temporalio::Workflow.execute_activity(
        PrePatchActivity,
        start_to_close_timeout: 100
      )
    end
  end
end

How it works:

  • For new executions: patched() returns true and records a marker in the Workflow history

  • For replay with the marker: patched() returns true (history includes this patch)

  • For replay without the marker: patched() returns false (history predates this patch)

Note: The patched() return value is memoized per patch ID. This means you cannot reliably use patched() in loops—it will return the same value every iteration. Workaround: append a sequence number to the patch ID for each iteration (e.g., "my-change-#{i}").

Three-Step Patching Process

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 with both old and new code paths:

class OrderWorkflow < Temporalio::Workflow::Definition
  def execute(order)
    if Temporalio::Workflow.patched('add-fraud-check')
      # New: Run fraud check before payment
      Temporalio::Workflow.execute_activity(
        CheckFraudActivity,
        order,
        start_to_close_timeout: 120
      )
    end

    # Original payment logic runs for both paths
    Temporalio::Workflow.execute_activity(
      ProcessPaymentActivity,
      order,
      start_to_close_timeout: 300
    )
  end
end

Step 2: Deprecate the Patch

Once all pre-patch Workflow Executions have completed, remove the old code and use deprecate_patch():

class OrderWorkflow < Temporalio::Workflow::Definition
  def execute(order)
    Temporalio::Workflow.deprecate_patch('add-fraud-check')

    # Only new code remains
    Temporalio::Workflow.execute_activity(
      CheckFraudActivity,
      order,
      start_to_close_timeout: 120
    )

    Temporalio::Workflow.execute_activity(
      ProcessPaymentActivity,
      order,
      start_to_close_timeout: 300
    )
  end
end

Step 3: Remove the Patch

After all workflows with the deprecated patch marker have completed, remove the deprecate_patch() call entirely:

class OrderWorkflow < Temporalio::Workflow::Definition
  def execute(order)
    Temporalio::Workflow.execute_activity(
      CheckFraudActivity,
      order,
      start_to_close_timeout: 120
    )

    Temporalio::Workflow.execute_activity(
      ProcessPaymentActivity,
      order,
      start_to_close_timeout: 300
    )
  end
end
Query Filters for Finding Workflows by Version
# Find running workflows with a specific patch
temporal workflow list --query \
  'WorkflowType = "OrderWorkflow" AND ExecutionStatus = "Running" AND TemporalChangeVersion = "add-fraud-check"'

# Find running workflows without any patch (pre-patch versions)
temporal workflow list --query \
  'WorkflowType = "OrderWorkflow" AND ExecutionStatus = "Running" AND TemporalChangeVersion IS NULL'

Workflow Type Versioning

For incompatible changes, create a new Workflow Type by duplicating the class:

class MyWorkflow < Temporalio::Workflow::Definition
  def execute
    # Original implementation
    Temporalio::Workflow.execute_activity(
      OriginalActivity,
      start_to_close_timeout: 100
    )
  end
end

class MyWorkflowV2 < Temporalio::Workflow::Definition
  def execute
    # New implementation with incompatible changes
    Temporalio::Workflow.execute_activity(
      NewActivity,
      start_to_close_timeout: 100
    )
  end
end

Register both with the Worker:

worker = Temporalio::Worker.new(
  client: client,
  task_queue: 'my-task-queue',
  workflows: [MyWorkflow, MyWorkflowV2],
  activities: [OriginalActivity, NewActivity]
)

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

# Old workflows continue on MyWorkflow
# New workflows use MyWorkflowV2
handle = client.start_workflow(
  MyWorkflowV2,
  input,
  id: SecureRandom.uuid,
  task_queue: 'my-task-queue'
)

Check for open executions before removing the old type:

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

Worker Versioning

Worker Versioning manages versions at the deployment level, allowing multiple Worker versions to run simultaneously. Requires Ruby SDK v0.5.0+.

Key Concepts

Worker Deployment: A logical service grouping similar Workers together (e.g., "order-processor"). All versions of your code live under this umbrella.

Worker Deployment Version: A specific snapshot of your code identified by a deployment name and Build ID (e.g., "order-processor:v1.0" or "order-processor:abc123").

Configuring Workers for Versioning
worker = Temporalio::Worker.new(
  client: client,
  task_queue: 'my-task-queue',
  workflows: [MyWorkflow],
  activities: [MyActivity],
  deployment_options: Temporalio::Worker::DeploymentOptions.new(
    version: Temporalio::WorkerDeploymentVersion.new(
      deployment_name: 'my-service',
      build_id: 'v1.0.0' # or git commit hash
    ),
    use_worker_versioning: true
  )
)
PINNED vs AUTO_UPGRADE Behaviors

PINNED Behavior

Workflows stay locked to their original Worker version. Set on the workflow definition:

class StableWorkflow < Temporalio::Workflow::Definition
  workflow_versioning_behavior :pinned

  def execute
    Temporalio::Workflow.execute_activity(
      ProcessOrderActivity,
      start_to_close_timeout: 300
    )
  end
end

When to use PINNED:

  • Short-running workflows (minutes to hours)

  • Consistency is critical (e.g., financial transactions)

  • You want to eliminate version compatibility complexity

  • Building new applications and want simplest development experience

AUTO_UPGRADE Behavior

Workflows can move to newer versions:

class LongRunningWorkflow < Temporalio::Workflow::Definition
  workflow_versioning_behavior :auto_upgrade

  def execute
    # This workflow may be picked up by a newer Worker version
    Temporalio::Workflow.execute_activity(
      ProcessActivity,
      start_to_close_timeout: 300
    )
  end
end

When to use AUTO_UPGRADE:

  • Long-running workflows (weeks or months)

  • Workflows need to benefit from bug fixes during execution

  • Migrating from traditional rolling deployments

  • You are already using patching APIs for version transitions

Important: AUTO_UPGRADE workflows still need patching to handle version transitions safely since they can move between Worker versions.

Worker Configuration with Default Behavior
worker = Temporalio::Worker.new(
  client: client,
  task_queue: 'orders-task-queue',
  workflows: [OrderWorkflow],
  activities: [ProcessOrderActivity],
  deployment_options: Temporalio::Worker::DeploymentOptions.new(
    version: Temporalio::WorkerDeploymentVersion.new(
      deployment_name: 'order-service',
      build_id: ENV.fetch('BUILD_ID')
    ),
    use_worker_versioning: true,
    default_versioning_behavior: Temporalio::VersioningBehavior::PINNED
  )
)
Deployment Strategies

Blue-Green Deployments

Maintain two environments and switch traffic between them:

  1. Deploy new code to idle environment

  2. Run tests and validation

  3. Switch traffic to new environment

  4. Keep old environment for instant rollback

Rainbow Deployments

Multiple versions run simultaneously:

  • New workflows use latest version

  • Existing workflows complete on their original version

  • Add new versions alongside existing ones

  • Gradually sunset old versions as workflows complete

This works well with Kubernetes where you manage multiple ReplicaSets running different Worker versions.

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

Best Practices

  1. Check for open executions before removing old code paths

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

  3. Deploy patches incrementally: patch, deprecate, remove

  4. Use PINNED for short workflows to simplify version management

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

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

  7. Avoid rolling deployments for high-availability services with long-running workflows

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

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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

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

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