Temporal Developer
OpenAIOfficialCreated by TemporalDistributed by OpenAI
- Skill
- Tool integration
- Software development
- MIT
- Low risk
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/downloadUnzip 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.mdTypeScript -> read
references/typescript/typescript.mdGo -> read
references/go/go.mdJava -> read
references/java/java.md.NET (C#) -> read
references/dotnet/dotnet.mdRuby -> read
references/ruby/ruby.mdRust -> read
references/rust/rust.md(in Public Preview)
Second, read appropriate
coreand language-specific references for the task at hand.
Primary References
references/core/determinism.md- Why determinism matters, replay mechanics, basic concepts of activitiesLanguage-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 mistakesLanguage-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 runningLanguage-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 proceduresreferences/core/error-reference.md- Common error types, workflow status referencereferences/core/interactive-workflows.md- Testing signals, updates, queriesreferences/core/dev-management.md- Dev cycle & management of server and workersreferences/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 conceptsLanguage-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 Temporalreferences/{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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- LICENSELicence file added by AutoGineer1.1 KiB
- SKILL.md7.4 KiB
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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:
Patching API - Code-level version branching
Workflow Type Versioning - New workflow types for incompatible changes
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! NondeterminismErrorApproach 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
Create new workflow type with new name
Register both with worker
Start new workflows with new type
Wait for old workflows to complete
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 workflowsKey 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 versionAUTO_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:
Each Workflow run remains pinned to its version (no patching needed during a run).
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.
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
| Scenario | Recommended Approach |
|---|---|
| Small change, few running workflows | Patching API |
| Major rewrite | Workflow Type Versioning |
| Many short workflows, frequent deploys | Worker Versioning (PINNED) |
| Long-running workflows, uses Continue-as-New | Worker Versioning (PINNED) + upgrade on Continue-as-New |
| Long-running workflows, no Continue-as-New | Worker Versioning (AUTO_UPGRADE) + Patching |
| Quick fix, can wait for completion | Wait for workflows to complete |
Best Practices
Check for open executions before removing old code
Use descriptive patch IDs (e.g., "add-fraud-check" not "patch-1")
Deploy incrementally: patch → deprecate → remove
Test replay compatibility before deploying changes
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
Removing old code too early - Breaks replaying workflows
Not testing with replay - Catches issues before production
Patching non-Command changes - Unnecessary complexity
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
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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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