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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://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/python/sync-vs-async.md

Python SDK: Sync vs Async Activities

Overview

The Temporal Python SDK supports multiple ways of implementing Activities:

  • Asynchronous using asyncio

  • Synchronous multithreaded using concurrent.futures.ThreadPoolExecutor

  • Synchronous multiprocess using concurrent.futures.ProcessPoolExecutor

Choosing the correct approach is critical—incorrect usage can cause sporadic failures and difficult-to-diagnose bugs.

Recommendation: Default to Synchronous

Activities should be synchronous by default. Use async only when certain the code doesn't block the event loop.

The Event Loop Problem

The Python async event loop runs in a single thread. When any task runs, no other tasks can execute until an await is reached. If code makes a blocking call (file I/O, synchronous HTTP, etc.), the entire event loop freezes.

Consequences of blocking the event loop:

  • Worker cannot communicate with Temporal Server

  • Workflow progress blocks across the worker

  • Potential deadlocks and unpredictable behavior

  • Difficult-to-diagnose bugs

How the SDK Handles Each Type

Synchronous Activities

  • Run in the activity_executor, which you must provide

  • Protected from accidentally blocking the global event loop

  • Multiple activities run in parallel via OS thread scheduling

  • Thread pool provides preemptive switching between tasks

from concurrent.futures import ThreadPoolExecutor
from temporalio.worker import Worker

with ThreadPoolExecutor(max_workers=100) as executor:
    worker = Worker(
        client,
        task_queue="my-queue",
        workflows=[MyWorkflow],
        activities=[my_sync_activity],
        activity_executor=executor,
    )
    await worker.run()
Asynchronous Activities

  • Share the default asyncio event loop with the Temporal worker

  • Any blocking call freezes the entire loop

  • Require async-safe libraries throughout

@activity.defn
async def my_async_activity(name: str) -> str:
    # Must use async-safe libraries only
    async with aiohttp.ClientSession() as session:
        async with session.get(f"http://api.example.com/{name}") as response:
            return await response.text()

HTTP Libraries: A Critical Choice

LibraryTypeSafe in Async Activity?
requestsBlockingNo - blocks event loop
urllib3BlockingNo - blocks event loop
aiohttpAsyncYes
httpxBothYes (use async mode)

Example: Wrong way (blocks event loop)

@activity.defn
async def bad_activity(url: str) -> str:
    import requests
    response = requests.get(url)  # BLOCKS the event loop!
    return response.text

Example: Correct way (async-safe)

@activity.defn
async def good_activity(url: str) -> str:
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return await response.text()

Running Blocking Code in Async Activities

If blocking code must run in an async activity, offload it to a thread:

import asyncio

@activity.defn
async def activity_with_blocking_call() -> str:
    # Run blocking code in a thread pool
    loop = asyncio.get_event_loop()
    result = await loop.run_in_executor(None, blocking_function)
    return result

# Or use asyncio.to_thread (Python 3.9+)
@activity.defn
async def activity_with_blocking_call_v2() -> str:
    result = await asyncio.to_thread(blocking_function)
    return result

When to Use Async Activities

Use async activities only when:

  1. All code paths are async-safe (no blocking calls)

  2. Using async-native libraries (aiohttp, asyncpg, motor, etc.)

  3. Performance benefits are needed for I/O-bound operations

  4. The team understands async constraints

When to Use Sync Activities

Use sync activities when:

  1. Making HTTP calls with requests or similar blocking libraries

  2. Performing file I/O operations

  3. Using database drivers that aren't async-native

  4. Uncertain whether code is async-safe

  5. Integrating with legacy or third-party synchronous code

Debugging Tip

If experiencing sporadic bugs, hangs, or timeouts:

  1. Convert async activities to sync

  2. Test thoroughly

  3. If bugs disappear, the original async activity had blocking calls

Threading Considerations

Multi-Core Usage

For CPU-bound work and multi-core usage:

  • Prefer multiple worker processes and/or threaded synchronous activities.

  • Use ProcessPoolExecutor for synchronous activities only if you understand and accept the extra complexity and different cancellation semantics.

Separate Workers for Workflows vs Activities

Some teams deploy:

  • Workflow-only workers (CPU-bound, need deadlock detection)

  • Activity-only workers (I/O-bound, may need more parallelism)

This prevents resource contention and allows independent scaling.

Complete Example: Sync Activity with ThreadPoolExecutor

import urllib.parse
import requests
from concurrent.futures import ThreadPoolExecutor
from temporalio import activity
from temporalio.client import Client
from temporalio.worker import Worker

@activity.defn
def greet_in_spanish(name: str) -> str:
    """Synchronous activity using requests library."""
    url = f"http://localhost:9999/get-spanish-greeting?name={urllib.parse.quote(name)}"
    response = requests.get(url)
    return response.text

async def main():
    client = await Client.connect("localhost:7233", namespace="default")

    with ThreadPoolExecutor(max_workers=100) as executor:
        worker = Worker(
            client,
            task_queue="greeting-tasks",
            workflows=[GreetingWorkflow],
            activities=[greet_in_spanish],
            activity_executor=executor,
        )
        await worker.run()

Complete Example: Async Activity with aiohttp

import aiohttp
import urllib.parse
from temporalio import activity
from temporalio.client import Client
from temporalio.worker import Worker

class TranslateActivities:
    def __init__(self, session: aiohttp.ClientSession):
        self.session = session

    @activity.defn
    async def greet_in_spanish(self, name: str) -> str:
        """Async activity using aiohttp - safe for event loop."""
        url = f"http://localhost:9999/get-spanish-greeting?name={urllib.parse.quote(name)}"
        async with self.session.get(url) as response:
            return await response.text()

async def main():
    client = await Client.connect("localhost:7233", namespace="default")

    async with aiohttp.ClientSession() as session:
        activities = TranslateActivities(session)
        worker = Worker(
            client,
            task_queue="greeting-tasks",
            workflows=[GreetingWorkflow],
            activities=[activities.greet_in_spanish],
        )
        await worker.run()

Summary

AspectSync ActivitiesAsync Activities
Default choiceYesOnly when certain
Blocking callsSafe (runs in thread pool)Dangerous (blocks event loop)
HTTP libraryrequests, httpxaiohttp, httpx (async)
Executor neededYes (ThreadPoolExecutor)No
DebuggingEasierHarder (timing issues)

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

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

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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