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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

  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/core/versioning.md

Workflow Versioning Concepts

This document provides core conceptual explanations of workflow versioning in Temporal. For language-specific implementation details see references/{your_language}/versioning.md, for the language you are working in.

Overview

Workflow versioning allows safe deployment of code changes without breaking running workflows. Three approaches available:

  1. Patching API - Code-level version branching

  2. Workflow Type Versioning - New workflow types for incompatible changes

  3. Worker Versioning - Deployment-level routing with Worker Deployment Versions

Why Versioning is Needed

When workers restart after deployment, they resume open workflows through history replay. If updated code produces different Commands than the original code, it causes non-determinism errors.

Original Code (recorded in history):
  await activity_a()
  await activity_b()

Updated Code (during replay):
  await activity_a()
  await activity_c()  ← Different! NondeterminismError

Approach 1: Patching API

Concept

The patching API lets you branch code based on whether a workflow was started before or after a code change.

if patched("my-change"):
    // New code path (for new and replaying new workflows)
else:
    // Old code path (for replaying old workflows)
Three-Phase Lifecycle

Phase 1: Patch In

  • Add both old and new code paths

  • New workflows take new path, old workflows take old path

Phase 2: Deprecate

  • After all old workflows complete, remove old code

  • Keep deprecation marker for history compatibility

Phase 3: Remove

  • After all deprecated workflows complete

  • Remove patch entirely, only new code remains

When to Use
  • Adding, removing, or reordering activities/child workflows

  • Changing which activity/child workflow is called

  • Any change that alters the Command sequence

When NOT to Use
  • Changing activity implementations (activities aren't replayed)

  • Changing arguments passed to activities or child workflows

  • Changing retry policies

  • Changing timer durations

  • Adding new signal/query/update handlers (additive changes are safe)

  • Bug fixes that don't change Command sequence

Unnecessary patching adds complexity and can make workflow code unmanageable.

Approach 2: Workflow Type Versioning

Concept

Create a new workflow type (e.g., OrderWorkflowV2) instead of patching.

// Old: OrderWorkflow
// New: OrderWorkflowV2 (completely new implementation)
When to Use
  • Major incompatible changes

  • Complete rewrites

  • When patching would be too complex

  • When you want clean separation

Process
  1. Create new workflow type with new name

  2. Register both with worker

  3. Start new workflows with new type

  4. Wait for old workflows to complete

  5. Remove old workflow type

Approach 3: Worker Versioning

Concept

Manage versions through Worker Deployments. Multiple Worker Deployment Versions can run simultaneously, and each version is identified by a deployment name and Build ID.

[!IMPORTANT] This is the current Worker Deployment-based versioning model. Do not confuse it with the legacy Build ID-based Worker Versioning APIs, which manage compatibility sets directly. Those APIs are deprecated.

Worker Deployment Version (deployment: order-service, build: abc123)
  └── Handles workflows started on this version

Worker Deployment Version (deployment: order-service, build: def456)
  └── Handles new workflows
  └── Can also handle upgraded old workflows
Key Concepts

Worker Deployment: Logical service grouping (e.g., "order-service")

Worker Deployment Version: A specific snapshot identified by a Worker Deployment name and a Build ID

Build ID: The code-version component of a Worker Deployment Version (e.g., a git commit hash)

Versioning Behaviors:

  • PINNED - Workflows stay on original worker version

  • AUTO_UPGRADE - Workflows can move to newer versions

When to Use PINNED
  • Short-running workflows (minutes to hours)

  • Consistency is critical

  • Want simplest development experience

  • Building new applications

When to Use AUTO_UPGRADE
  • Long-running workflows (weeks or months)

  • Workflows need bug fixes during execution

  • Still requires patching for version transitions

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.

Long-running Pinned Workflows that use Continue-as-New can upgrade to newer Worker Deployment Versions at the Continue-as-New boundary without patching.

This pattern is for:

  • Entity Workflows that run for months or years

  • Batch processing Workflows that checkpoint with Continue-as-New

  • AI agent Workflows with long sleeps waiting for user input

How it works

By default, Pinned Workflows stay on their original Worker Deployment Version even when they Continue-as-New. With the upgrade option enabled:

  1. Each Workflow run remains pinned to its version (no patching needed during a run).

  2. The Temporal Server tells the Workflow when a new Target Version becomes available — that is, when the Workflow's Worker Deployment gets a new Current or Ramping Version that the Workflow would move to next.

  3. When the Workflow performs Continue-as-New with the upgrade option, the new run starts on the Target Version.

Detection flag

Active Workflows detect a Target Version change by checking a per-Workflow flag exposed on WorkflowInfo (called target_worker_deployment_version_changed in the docs). The flag is refreshed after each Workflow Task completes; check it from code that runs as part of a Workflow Task (for example, before accepting an Update, starting an Activity, or starting a child Workflow). See the per-language references/{your_language}/versioning.md for the SDK-specific call.

Triggering the new run

When the flag is set, return a Continue-as-New error with the new run's initial Versioning Behavior set to AutoUpgrade. This makes the new run start on the Target Version of its Worker Deployment. The Workflow Type itself retains its Pinned annotation; only the initial behavior of the new run is overridden so it picks up the Target Version. Once the new run is on the new version, the per-Workflow-type annotation continues to apply on subsequent CaN.

Limitations
  • Lazy moving only — sleeping Workflows do not auto-upgrade. Send a Signal to wake an idle Workflow so it can check the flag.

  • Interface compatibility is your responsibility. When continuing as new to a different version, the previous version's Workflow input must be compatible with the new version's Workflow definition. If incompatible, the new run may fail on its first Workflow Task.

  • Pinned Workflows only. Auto-Upgrade Workflows already move to the Target Version at Workflow Task boundaries; this pattern adds nothing for them.

When to use this pattern
  • Workflow Type is Pinned and

  • Workflow runs longer than your Worker Deployment Version lifetime and

  • Workflow already uses Continue-as-New to bound Event History size.

For long-running Workflows that cannot use Continue-as-New (e.g., compliance audits that need full history), use AUTO_UPGRADE with patching instead.

Choosing an Approach

ScenarioRecommended Approach
Small change, few running workflowsPatching API
Major rewriteWorkflow Type Versioning
Many short workflows, frequent deploysWorker Versioning (PINNED)
Long-running workflows, uses Continue-as-NewWorker Versioning (PINNED) + upgrade on Continue-as-New
Long-running workflows, no Continue-as-NewWorker Versioning (AUTO_UPGRADE) + Patching
Quick fix, can wait for completionWait for workflows to complete

Best Practices

  1. Check for open executions before removing old code

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

  3. Deploy incrementally: patch → deprecate → remove

  4. Test replay compatibility before deploying changes

  5. Monitor old workflow counts during migration

Finding Workflows by Version

# Find workflows with specific patch
temporal workflow list --query \
  'WorkflowType = "OrderWorkflow" AND TemporalChangeVersion = "add-fraud-check"'

# Find pre-patch workflows
temporal workflow list --query \
  'WorkflowType = "OrderWorkflow" AND TemporalChangeVersion IS NULL'

# Find workflows on specific worker version
temporal workflow list --query \
  'TemporalWorkerDeploymentVersion = "my-service:v1.0.0"'

Common Mistakes

  1. Removing old code too early - Breaks replaying workflows

  2. Not testing with replay - Catches issues before production

  3. Patching non-Command changes - Unnecessary complexity

  4. Forgetting to deprecate - Accumulates dead code

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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