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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LICENSELicence file added by AutoGineer1.1 KiB
SKILL.md7.4 KiB
references/core/ai-patterns.md5.5 KiB
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references/python/determinism-protection.md

Python Workflow Sandbox

Overview

The Python SDK runs workflows in a sandbox that provides automatic protection against non-deterministic operations. This is unique to the Python SDK.

How the Sandbox Works

The sandbox:

Isolates global state via exec compilation
Restricts non-deterministic library calls via proxy objects
Passes through standard library with restrictions
Reloads workflow files on each execution

Forbidden Operations in Workflows

These operations are forbidden inside workflow code (appropriate in activities) and will fail in the sandbox:

Direct I/O: Network calls, file reads/writes
Threading: threading module operations
Subprocess: subprocess calls
Global state: Modifying mutable global variables
Blocking sleep: time.sleep() (use workflow.sleep(timedelta(...)))

Pass-Through Pattern

Third-party libraries that aren't sandbox-aware need explicit pass-through:

from temporalio import workflow

with workflow.unsafe.imports_passed_through():
    import pydantic
    from my_module import my_dataclass

When to use pass-through:

Data classes and models (Pydantic, dataclasses)
Serialization libraries
Type definitions
Any library that doesn't do I/O or non-deterministic operations
Performance, as many non-passthrough imports can be slower

Note: The imports, even when using imports_passed_through, should all be at the top of the file. Runtime imports are an anti-pattern.

Importing Activities

Activities should be imported through pass-through since they're defined outside the sandbox:

# workflows/order.py
from temporalio import workflow

with workflow.unsafe.imports_passed_through():
    from activities.payment import process_payment
    from activities.shipping import ship_order

@workflow.defn
class OrderWorkflow:
    @workflow.run
    async def run(self, order_id: str) -> str:
        await workflow.execute_activity(
            process_payment,
            order_id,
            start_to_close_timeout=timedelta(minutes=5),
        )
        return await workflow.execute_activity(
            ship_order,
            order_id,
            start_to_close_timeout=timedelta(minutes=10),
        )

Disabling the Sandbox

@workflow.defn
class MyWorkflow:
    @workflow.run
    async def run(self) -> str:
        with workflow.unsafe.sandbox_unrestricted():
            # Unrestricted code block
            pass
        return "result"

Per‑block escape hatch from runtime restrictions; imports unchanged.
Use when: You need to call something the sandbox would normally block (e.g., a restricted stdlib call) in a very small, controlled section.
IMPORTANT: Use it sparingly; you lose determinism checks inside the block
Genuinely non-deterministic code still MUST go into activities.

Customizing Invalid Module Members

invalid_module_members includes modules that cannot be accessed.

Checks are compared against the fully qualified path to the item.

import dataclasses
from temporalio.worker import Worker
from temporalio.worker.workflow_sandbox import (
  SandboxedWorkflowRunner,
  SandboxMatcher,
  SandboxRestrictions,
)

# Example 1: Remove a restriction on datetime.date.today():
restrictions = dataclasses.replace(
    SandboxRestrictions.default,
    invalid_module_members=SandboxRestrictions.invalid_module_members_default.with_child_unrestricted(
      "datetime", "date", "today",
    ),
)

# Example 2: Restrict the datetime.date class from being used
restrictions = dataclasses.replace(
    SandboxRestrictions.default,
    invalid_module_members=SandboxRestrictions.invalid_module_members_default | SandboxMatcher(
      children={"datetime": SandboxMatcher(use={"date"})},
    ),
)

worker = Worker(
    ...,
    workflow_runner=SandboxedWorkflowRunner(restrictions=restrictions),
)

Import Notification Policy

Control warnings/errors for sandbox import issues. Recommended for catching potential problems:

from temporalio import workflow
from temporalio.worker.workflow_sandbox import SandboxedWorkflowRunner, SandboxRestrictions

restrictions = SandboxRestrictions.default.with_import_notification_policy(
    workflow.SandboxImportNotificationPolicy.WARN_ON_DYNAMIC_IMPORT
    | workflow.SandboxImportNotificationPolicy.WARN_ON_UNINTENTIONAL_PASSTHROUGH
)

worker = Worker(
    ...,
    workflow_runner=SandboxedWorkflowRunner(restrictions=restrictions),
)

WARN_ON_DYNAMIC_IMPORT (default) - warns on imports after initial workflow load
WARN_ON_UNINTENTIONAL_PASSTHROUGH - warns when modules are imported into sandbox without explicit passthrough (not default, but highly recommended for catching missing passthroughs)
RAISE_ON_UNINTENTIONAL_PASSTHROUGH - raise instead of warn

Override per-import with the context manager:

with workflow.unsafe.sandbox_import_notification_policy(
    workflow.SandboxImportNotificationPolicy.SILENT
):
    import pydantic  # No warning for this import

Disable Lazy sys.modules Passthrough

By default, passthrough modules are lazily added to the sandbox's sys.modules when accessed. To require explicit imports:

import dataclasses
from temporalio.worker.workflow_sandbox import SandboxedWorkflowRunner, SandboxRestrictions

restrictions = dataclasses.replace(
    SandboxRestrictions.default,
    disable_lazy_sys_module_passthrough=True,
)

worker = Worker(
    ...,
    workflow_runner=SandboxedWorkflowRunner(restrictions=restrictions),
)

When True, passthrough modules must be explicitly imported to appear in the sandbox's sys.modules.

File Organization

Critical: Keep workflow definitions in separate files from activity definitions.

The sandbox reloads workflow definition files on every execution. Minimizing file contents improves Worker performance.

my_temporal_app/
├── workflows/
│   └── order.py         # Only workflow classes
├── activities/
│   └── payment.py       # Only activity functions
├── models/
│   └── order.py         # Shared data models
├── worker.py            # Worker setup, imports both
└── starter.py           # Client code

Common Issues

Import Errors

Error: Cannot import 'pydantic' in sandbox

Fix: Use pass-through:

with workflow.unsafe.imports_passed_through():
    import pydantic

Non-Determinism from Libraries

Some libraries do internal caching or use current time:

# May cause non-determinism
import some_library
result = some_library.cached_operation()  # Cache changes between replays

Fix: Move to activity or use pass-through with caution.

Best Practices

Separate workflow and activity files for performance
Use pass-through explicitly for third-party libraries
Keep workflow files small to minimize reload time
Move I/O to activities always
Test with replay to catch sandbox issues early

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

2026-11-09

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

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download temporal-developer.zip

From the author

AutoGineer summary

What it does

When to use it

Advantages

Usage examples

Install

Documentation by Temporal

Skill: temporal-developer

Overview

Core Architecture

History Replay: Why Determinism Matters

Getting Started

Ensure Temporal CLI is installed

Read All Relevant References

Primary References

Task Queue Priority and Fairness

Additional Topics

Third-Party Integrations

Feedback

Reporting Issues in This Skill

Files

references/python/determinism-protection.md

Python Workflow Sandbox

Overview

How the Sandbox Works

Forbidden Operations in Workflows

Pass-Through Pattern

Importing Activities

Disabling the Sandbox

Customizing Invalid Module Members

Import Notification Policy

Disable Lazy sys.modules Passthrough

File Organization

Common Issues

Import Errors

Non-Determinism from Libraries

Best Practices

Requirements and compatibility

Security

Attribution and licence

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MCP Builder

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