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://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/python/advanced-features.md

Python SDK Advanced Features

Schedules

Create recurring workflow executions.

from temporalio.client import (
    Schedule,
    ScheduleActionStartWorkflow,
    ScheduleSpec,
    ScheduleIntervalSpec,
)

# Create a schedule
schedule_id = "daily-report"
await client.create_schedule(
    schedule_id,
    Schedule(
        action=ScheduleActionStartWorkflow(
            DailyReportWorkflow.run,
            id="daily-report",
            task_queue="reports",
        ),
        spec=ScheduleSpec(
            intervals=[ScheduleIntervalSpec(every=timedelta(days=1))],
        ),
    ),
)

# Manage schedules
schedule = client.get_schedule_handle(schedule_id)
await schedule.pause("Maintenance window")
await schedule.unpause()
await schedule.trigger()  # Run immediately
await schedule.delete()

Async Activity Completion

For activities that complete asynchronously (e.g., human tasks, external callbacks). If you configure a heartbeat_timeout on this activity, the external completer is responsible for sending heartbeats via the async handle. If you do NOT set a heartbeat_timeout, no heartbeats are required.

Note: If the external system that completes the asynchronous action can reliably be trusted to do the task and Signal back with the result, and it doesn't need to Heartbeat or receive Cancellation, then consider using signals instead.

from temporalio import activity
from temporalio.client import Client

@activity.defn
async def request_approval(request_id: str) -> None:
    # Get task token for async completion
    task_token = activity.info().task_token

    # Store task token for later completion (e.g., in database)
    await store_task_token(request_id, task_token)

    # Mark this activity as waiting for external completion
    activity.raise_complete_async()

# Later, complete the activity from another process
async def complete_approval(request_id: str, approved: bool):
    client = await Client.connect("localhost:7233", namespace="default")
    # Retrieve the task token from external storage (e.g., database)
    task_token = await get_task_token(request_id)

    handle = client.get_async_activity_handle(task_token=task_token)

    # Optional: if a heartbeat_timeout was set, you can periodically:
    # await handle.heartbeat(progress_details)

    if approved:
        await handle.complete("approved")
    else:
        # You can also fail or report cancellation via the handle
        await handle.fail(ApplicationError("Rejected"))

Sandbox Customization

The Python SDK runs workflows in a sandbox to help you ensure determinism. You can customize sandbox restrictions when needed. See references/python/determinism-protection.md

Gevent Compatibility Warning

The Python SDK is NOT compatible with gevent. Gevent's monkey patching modifies Python's asyncio event loop in ways that break the SDK's deterministic execution model.

If your application uses gevent:

You cannot run Temporal workers in the same process
Consider running workers in a separate process without gevent
Use a message queue or HTTP API to communicate between gevent and Temporal processes

Worker Tuning

Configure worker performance settings.

from concurrent.futures import ThreadPoolExecutor

worker = Worker(
    client,
    task_queue="my-queue",
    workflows=[MyWorkflow],
    activities=[my_activity],
    # Workflow task concurrency
    max_concurrent_workflow_tasks=100,
    # Activity task concurrency
    max_concurrent_activities=100,
    # Executor for sync activities
    activity_executor=ThreadPoolExecutor(max_workers=50),
    # Graceful shutdown timeout
    graceful_shutdown_timeout=timedelta(seconds=30),
)

DNS Resolver Configuration

DnsLoadBalancingConfig makes Core periodically re-resolve the client's target host and round-robin requests across the resolved addresses . Use it when target_host resolves to multiple A/AAAA records (e.g., a load-balanced gRPC frontend, multi-address private endpoints) and you want the client to spread RPCs across them.

Configuration

from temporalio.client import Client
from temporalio.service import DnsLoadBalancingConfig

client = await Client.connect(
    "frontend.example.internal:7233",
    dns_load_balancing_config=DnsLoadBalancingConfig(
        resolution_interval_millis=5000,  # re-resolve every 5 seconds
    ),
)

The only field is resolution_interval_millis: int = 30000 — how often to re-resolve DNS, in milliseconds.
DnsLoadBalancingConfig.default is a pre-built instance with the default 30-second interval.
dns_load_balancing_config defaults to 30 seconds if you don't pass anything explicitly.
Pass dns_load_balancing_config=None to disable DNS load balancing entirely.

Mutual exclusion with HTTP CONNECT proxy

DNS load balancing and HttpConnectProxyConfig cannot be used together. When http_connect_proxy_config is set on the same client, DNS load balancing is silently disabled — there is no error and no precedence flag. If you need both, you cannot have both; choose the one your network requires.

Workflow Init Decorator

You should always put state initialization logic in the __init__ of your workflow class, so that it happens before signals/updates arrive.

Normally, your __init__ must have no arguments. However, if you add the @workflow.init decorator, then your __init__ instead receives the same workflow arguments that @workflow.run receives:

@workflow.defn
class MyWorkflow:
    @workflow.init
    def __init__(self, initial_value: str) -> None:
        # This runs when the Workflow is instantiated, including during replay
        self._value = initial_value
        self._items: list[str] = []

    @workflow.run
    async def run(self, initial_value: str) -> str:
        # self._value and self._items are already initialized
        return self._value

__init__ (with @workflow.init) and @workflow.run must have the same parameters with the same types. You cannot make blocking calls (activities, sleeps, etc.) from the __init__.

Workflow Failure Exception Types

Control which exceptions cause workflow task failures vs workflow failures.

Special case: if you include temporalio.workflow.NondeterminismError (or a superclass), non-determinism errors will fail the workflow instead of leaving it in a retrying state
Tip for testing: Set to [Exception] in tests so any unhandled exception fails the workflow immediately rather than retrying the workflow task forever. This surfaces bugs faster.

Per-Workflow Configuration

@workflow.defn(
    # These exception types will fail the workflow execution (not just the task)
    failure_exception_types=[ValueError, CustomBusinessError]
)
class MyWorkflow:
    @workflow.run
    async def run(self) -> str:
        raise ValueError("This fails the workflow, not just the task")

Worker-Level Configuration

worker = Worker(
    client,
    task_queue="my-queue",
    workflows=[MyWorkflow],
    workflow_failure_exception_types=[ValueError, CustomBusinessError],
)

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

2026-11-09

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

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