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://mp-staging.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/ruby/gotchas.md

Ruby Gotchas

Ruby-specific mistakes and anti-patterns. See also Common Gotchas for language-agnostic concepts.

File Organization

Unlike Python, Ruby doesn't reload workflow files (no sandbox). Still best practice to separate workflows and activities for clarity and maintainability.

# BAD - Everything in one file
# app.rb
class MyWorkflow < Temporalio::Workflow::Definition
  def execute(name)
    Temporalio::Workflow.execute_activity(
      MyActivity,
      name,
      start_to_close_timeout: 30
    )
  end
end

class MyActivity < Temporalio::Activity::Definition
  def execute(name)
    # Heavy I/O, external calls, etc.
  end
end

# GOOD - Separate files
# workflows/my_workflow.rb
require 'temporalio/workflow'

class MyWorkflow < Temporalio::Workflow::Definition
  def execute(name)
    Temporalio::Workflow.execute_activity(
      MyActivity,
      name,
      start_to_close_timeout: 30
    )
  end
end

# activities/my_activity.rb
require 'temporalio/activity'

class MyActivity < Temporalio::Activity::Definition
  def execute(name)
    # Heavy I/O, external calls, etc.
  end
end

# worker.rb
require_relative 'workflows/my_workflow'
require_relative 'activities/my_activity'

Wrong Retry Classification

Transient network errors should be retried. Authentication errors should not be. See references/ruby/error-handling.md to understand how to classify errors with non_retryable: true and non_retryable_error_types.

Heartbeating

Forgetting to Heartbeat Long Activities

# BAD - No heartbeat, can't detect stuck activities
class ProcessLargeFile < Temporalio::Activity::Definition
  def execute(path)
    File.foreach(path).each_slice(1000) do |chunk|
      process(chunk)  # Takes hours, no heartbeat
    end
  end
end

# GOOD - Regular heartbeats with progress
class ProcessLargeFile < Temporalio::Activity::Definition
  def execute(path)
    File.foreach(path).each_slice(1000).with_index do |chunk, i|
      Temporalio::Activity::Context.current.heartbeat("Processing chunk #{i}")
      process(chunk)
    end
  end
end

Heartbeat Timeout Too Short

# BAD - Heartbeat timeout shorter than processing time between heartbeats
Temporalio::Workflow.execute_activity(
  ProcessChunk,
  start_to_close_timeout: 1800,
  heartbeat_timeout: 10  # Too short!
)

# GOOD - Heartbeat timeout allows for processing variance
Temporalio::Workflow.execute_activity(
  ProcessChunk,
  start_to_close_timeout: 1800,
  heartbeat_timeout: 120
)

Set heartbeat timeout as high as acceptable for your use case -- each heartbeat counts as an action.

Cancellation

Not Handling Workflow Cancellation

# BAD - Cleanup doesn't run on cancellation
class BadWorkflow < Temporalio::Workflow::Definition
  def execute
    Temporalio::Workflow.execute_activity(AcquireResource, start_to_close_timeout: 300)
    Temporalio::Workflow.execute_activity(DoWork, start_to_close_timeout: 300)
    Temporalio::Workflow.execute_activity(ReleaseResource, start_to_close_timeout: 300)  # Never runs if cancelled!
  end
end

# GOOD - Use ensure with detached cancellation for cleanup
class GoodWorkflow < Temporalio::Workflow::Definition
  def execute
    Temporalio::Workflow.execute_activity(AcquireResource, start_to_close_timeout: 300)
    Temporalio::Workflow.execute_activity(DoWork, start_to_close_timeout: 300)
  ensure
    # Create a detached cancellation (not tied to workflow cancellation)
    # so cleanup activity runs even after workflow is cancelled
    detached_cancel, _cancel_proc = Temporalio::Cancellation.new
    Temporalio::Workflow.execute_activity(
      ReleaseResource,
      start_to_close_timeout: 300,
      cancellation: detached_cancel
    )
  end
end

Not Handling Activity Cancellation

Activities must opt in to receive cancellation. This requires:

Heartbeating -- cancellation is delivered via the heartbeat response
Catching the cancellation exception -- Temporalio::Error::CancelledError is raised when a heartbeat detects cancellation

# BAD - Activity ignores cancellation
class LongActivity < Temporalio::Activity::Definition
  def execute
    do_expensive_work  # Runs to completion even if cancelled
  end
end

# GOOD - Heartbeat and handle cancellation
class LongActivity < Temporalio::Activity::Definition
  def execute
    items.each do |item|
      Temporalio::Activity::Context.current.heartbeat
      process(item)
    end
  rescue Temporalio::Error::CancelledError
    cleanup
    raise
  end
end

Testing

Not Testing Failures

Make sure workflows work as expected under failure paths, not just happy paths. See references/ruby/testing.md for more info.

Not Testing Replay

Replay tests help you detect hidden sources of non-determinism in your workflow code and should be considered in addition to standard testing. See references/ruby/testing.md for more info.

Timers and Sleep

Using Kernel.sleep

# BAD - Kernel.sleep raises NondeterminismError
class BadWorkflow < Temporalio::Workflow::Definition
  def execute
    sleep(60)          # NondeterminismError!
    Kernel.sleep(60)   # NondeterminismError!
  end
end

# GOOD - Use Temporalio::Workflow.sleep for durable timers
class GoodWorkflow < Temporalio::Workflow::Definition
  def execute
    Temporalio::Workflow.sleep(60)  # Deterministic, durable timer
  end
end

Why this matters: Kernel.sleep uses the system clock, which differs between original execution and replay. Temporalio::Workflow.sleep creates a durable timer in the event history, ensuring consistent behavior during replay.

Illegal Call Tracing Gotchas

Third-Party Gems Triggering NondeterminismError

The Ruby SDK uses TracePoint-based illegal call tracing on the workflow fiber. Any gem that internally uses Thread, IO, Socket, Net::HTTP, or other forbidden operations will trigger NondeterminismError -- even if the call is deep in the gem's internals.

# BAD - Logging gem that uses Thread internally
class MyWorkflow < Temporalio::Workflow::Definition
  def execute
    SomeFancyLogger.info("Starting workflow")  # NondeterminismError if gem uses Thread.new!
  end
end

Fix: Disable Illegal Call Tracing for Specific Code

# Wrap non-deterministic but safe code
Temporalio::Workflow::Unsafe.illegal_call_tracing_disabled do
  # Code here won't trigger NondeterminismError
  SomeFancyLogger.info("Starting workflow")
end

For code that performs IO that you know is safe and want to allow:

# Disable the durable scheduler for IO operations
Temporalio::Workflow::Unsafe.durable_scheduler_disabled do
  # IO operations allowed here
end

Side Effects and Replay Safety

Always check replaying status before performing side effects in workflows:

unless Temporalio::Workflow::Unsafe.replaying?
  # Only runs during original execution, not replay
  Temporalio::Workflow.logger.info("Processing started")
end

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

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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

2026-11-08

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

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