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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://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/standalone-activities.md

[!NOTE] Standalone Activities are 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.

Standalone Activities (Concepts)

This document provides core conceptual explanations of Standalone Activities in Temporal. For language-specific implementation details, see references/{your_language}/standalone-activities.md for the language you are working in (Python, TypeScript, Java, .NET, Go).

What is a Standalone Activity?

A Standalone Activity is a top-level Activity Execution started directly by a Client, without using a Workflow. It is Temporal's job queue — the simplest way to run a single durable, retryable task.

The rule of thumb:

  • Need to orchestrate multiple Activities? Use a Workflow.

  • Just need to execute a single Activity? Use a Standalone Activity.

The same Activity Function code runs in both modes with no changes — the only difference is how it is invoked. An Activity defined for a Workflow can also be executed standalone, and the Worker that hosts it does not need to know how it will be invoked.

Compared to wrapping a single Activity in a Workflow, a Standalone Activity:

  • Reduces billable actions in Temporal Cloud.

  • Lowers latency for short-lived executions (fewer Worker round-trips).

  • Lives in a separate ID space from Workflows.

Use cases

Standalone Activities fit durable single-job processing where you don't need multi-step orchestration:

  • Sending an email

  • Processing a webhook

  • Syncing data

  • Any single-function task that benefits from built-in retries and timeouts

Key features
  • Execute Activities as a top-level primitive, without Workflow overhead.

  • Native async job lifecycle: schedule → dispatch → process → result.

  • Arbitrary-length jobs, with heartbeats for progress tracking.

  • At-least-once execution by default, with native retry policy and timeouts.

  • At-most-once execution when the retry policy's maximum attempts is 1.

  • Addressable by Activity ID / Run ID for result retrieval, cancellation, and termination.

  • Deduplication via configurable conflict policies.

  • Priority and fairness support.

  • Full visibility — list and count executions.

Using Standalone Activities

Defining activities

Defining standalone activities is IDENTICAL to defining activities callable from a workflow - there is no distinction AT ALL between the two at activity definition or worker configuration site. Follow language-specific guidance for how to normally define activities and configure workers to run them.

Calling and Interacting with Standalone Activities

The CLI and every SDK exposes the same conceptual operations against a Standalone Activity (method names differ per language — see the language reference):

  • Execute — durably enqueue the Activity, wait for a Worker to run it, and return the result.

  • Start — durably enqueue the Activity and return a handle immediately, without waiting.

  • Get handle — rebind a handle to a previously started Activity by ID (and optionally Run ID).

  • Get result — wait on a handle for completion. execute is equivalent to start followed by awaiting the handle's result.

  • Cancel / Terminate — via the handle or CLI.

Choosing an Activity ID. Every Standalone Activity call requires an Activity ID, which uniquely identifies that one call. It is the key you use later to get the result, describe, cancel, or terminate the Activity, and it is what conflict/reuse policies dedupe against. Use a business-logic identifier that uniquely identifies the call — for example send-welcome-email:user-42, sync-invoice:INV-2026-001, or process-webhook:<event-id>. This makes Activities addressable and naturally deduplicated by your domain. Only if you genuinely have no meaningful business-level identifier should you generate a UUID to use as the Activity ID.

Visibility operations are available as well:

  • List — enumerate Standalone Activity Executions matching a query. Only Standalone Activities are returned; Activities running inside Workflows are not included.

  • Count — return the total number of executions matching a query (running, completed, failed, etc. — not the number of queued tasks).

  • Describe — via the handle or CLI.

See below for a quick reference how to call these operations from the CLI rather than SDKs.

[!IMPORTANT] When using an SDK, these operations are owned by the Temporal Client, and belong in your non-workflow application code. It is INVALID to call an activity as a standalone activity from within a workflow: you instead should use standard within-workflow activity calls.

Currently Supported SDKs: Python, TypeScript, Java, .NET, Go

Quick CLI Standalone Activity Man Page

Ultimately, any standalone activity invocation code should live in your application code and use the appropriate SDK, but the Temporal CLI is a quick and easy way to test invoking standalone activities during development. All subcommands live under temporal activity.

The key operations are:

Execute (start and wait for the result). Blocks until the Activity completes and prints the result to stdout. Requires --activity-id, --type, --task-queue, and at least one of --start-to-close-timeout / --schedule-to-close-timeout:

temporal activity execute \
  --activity-id my-activity-id \
  --type ComposeGreeting \
  --task-queue my-task-queue \
  --start-to-close-timeout 10s \
  --input '{"some-key": "some-value"}'

--input takes a JSON value; pass it multiple times for multiple positional arguments. --input-file is also a convenient option for larger inputs. The same required flags apply to start below.

Reminder: --activity-id must be unique across all activity calls, as discussed above.

Start (do not wait). Enqueues the Activity and prints the Activity ID and Run ID without blocking:

temporal activity start \
  --activity-id my-activity-id \
  --type ComposeGreeting \
  --task-queue my-task-queue \
  --start-to-close-timeout 10s \
  --input '{"some-key": "some-value"}'

Outputs this JSON shape:

{
  "activityId": "my-activity-id",
  "runId": "019e84d3-949a-7a0e-ae78-63b8a0b172bd",
  "namespace": "default"
}

Result (wait for a started Activity). Waits for completion and prints the result. --run-id is optional and defaults to the latest run of that Activity ID:

temporal activity result --activity-id my-activity-id

Describe (current state of one Activity). Shows status, run state, task queue, timeouts, attempt count, etc.:

temporal activity describe --activity-id my-activity-id

List / Count (visibility across many Activities). Only Standalone Activity Executions are returned (Activities running inside Workflows are not):

temporal activity list
temporal activity count

Cancel / Terminate (stop an Activity). cancel requests cooperative cancellation (surfaced to the Activity on its next heartbeat response); terminate forcefully ends it (Activity code cannot see or respond to it). Both accept --reason:

temporal activity cancel    --activity-id my-activity-id --reason "no longer needed"
temporal activity terminate --activity-id my-activity-id --reason "no longer needed"

Observability

All existing Activity metrics apply to Standalone Activities (scheduled, started, completed, failed, timed out, canceled).

Public Preview limitations

  • Pause, reset, and update options are not supported (scheduled for GA).

  • The TerminateExisting conflict policy and TerminateIfRunning reuse policy are not yet supported.

Temporal CLI support

  • Requires Temporal CLI v1.7.0+ and Temporal Server v1.31.0+. See references/core/install_cli.md if you need to update the CLI.

  • The Temporal Dev Server (temporal server start-dev) has Standalone Activities enabled by default.

Temporal Cloud support

Standalone Activities are available in Temporal Cloud as a Public Preview feature. Because the SDK client config loaders read environment variables and TOML profiles, the same code runs against a local server or Temporal Cloud with no code changes.

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

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