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/data-handling.md

Python SDK Data Handling

Overview

The Python SDK uses data converters to serialize/deserialize workflow inputs, outputs, and activity parameters.

Default Data Converter

The default converter handles:

None
bytes (as binary)
Protobuf messages
JSON-serializable types (dict, list, str, int, float, bool)

Pydantic Integration

Use Pydantic models for validated, typed data.

In your workflow definition, just use input and result types that subclass pydantic.BaseModel:

from pydantic import BaseModel

class OrderInput(BaseModel):
    order_id: str
    items: list[str]
    total: float
    customer_email: str

class OrderResult(BaseModel):
    order_id: str
    status: str
    tracking_number: str | None = None

@workflow.defn
class OrderWorkflow:
    @workflow.run
    async def run(self, input: OrderInput) -> OrderResult:
        # Pydantic validation happens automatically
        return OrderResult(
            order_id=input.order_id,
            status="completed",
            tracking_number="TRK123",
        )

And when you configure the client, pass the pydantic_data_converter:

from temporalio.contrib.pydantic import pydantic_data_converter
# Configure client with Pydantic support
client = await Client.connect(
    "localhost:7233",
    namespace="default",
    data_converter=pydantic_data_converter,
)

Custom Data Conversion

Usually the easiest way to do this is via implementing an EncodingPayloadConverter and CompositePayloadConverter. See:

for an extended example.

Payload Encryption

Encrypt sensitive workflow data.

from temporalio.converter import PayloadCodec
from temporalio.api.common.v1 import Payload
from cryptography.fernet import Fernet
from typing import Sequence

class EncryptionCodec(PayloadCodec):
    def __init__(self, key: bytes):
        self._fernet = Fernet(key)

    async def encode(self, payloads: Sequence[Payload]) -> list[Payload]:
        return [
            Payload(
                metadata={"encoding": b"binary/encrypted"},
                # Since encryption uses C extensions that give up the GIL, we can avoid blocking the async event loop here.
                data=await asyncio.to_thread(self._fernet.encrypt, p.SerializeToString()),
            )
            for p in payloads
        ]

    async def decode(self, payloads: Sequence[Payload]) -> list[Payload]:
        result = []
        for p in payloads:
            if p.metadata.get("encoding") == b"binary/encrypted":
                decrypted = await asyncio.to_thread(self._fernet.decrypt, p.data)
                decoded = Payload()
                decoded.ParseFromString(decrypted)
                result.append(decoded)
            else:
                result.append(p)
        return result

# Apply encryption codec
client = await Client.connect(
    "localhost:7233",
    namespace="default",
    data_converter=DataConverter(
        payload_codec=EncryptionCodec(encryption_key),
    ),
)

Search Attributes

Custom searchable fields for workflow visibility. These can be created at workflow start:

from temporalio.common import (
    SearchAttributeKey,
    SearchAttributePair,
    TypedSearchAttributes,
)
from datetime import datetime
from datetime import timezone

ORDER_ID = SearchAttributeKey.for_keyword("OrderId")
ORDER_STATUS = SearchAttributeKey.for_keyword("OrderStatus")
ORDER_TOTAL = SearchAttributeKey.for_float("OrderTotal")
CREATED_AT = SearchAttributeKey.for_datetime("CreatedAt")

# At workflow start
handle = await client.start_workflow(
    OrderWorkflow.run,
    order,
    id=f"order-{order.id}",
    task_queue="orders",
    search_attributes=TypedSearchAttributes([
        SearchAttributePair(ORDER_ID, order.id),
        SearchAttributePair(ORDER_STATUS, "pending"),
        SearchAttributePair(ORDER_TOTAL, order.total),
        SearchAttributePair(CREATED_AT, datetime.now(timezone.utc)),
    ]),
)

Or upserted during workflow execution:

from temporalio import workflow
from temporalio.common import SearchAttributeKey, SearchAttributePair, TypedSearchAttributes

ORDER_STATUS = SearchAttributeKey.for_keyword("OrderStatus")

@workflow.defn
class OrderWorkflow:
    @workflow.run
    async def run(self, order: Order) -> str:
        # ... process order ...

        # Update search attribute
        workflow.upsert_search_attributes(TypedSearchAttributes([
            SearchAttributePair(ORDER_STATUS, "completed"),
        ]))
        return "done"

Querying Workflows by Search Attributes

# List workflows using search attributes
async for workflow in client.list_workflows(
    'OrderStatus = "processing" OR OrderStatus = "pending"'
):
    print(f"Workflow {workflow.id} is still processing")

Workflow Memo

Store arbitrary metadata with workflows (not searchable).

# Set memo at workflow start
await client.execute_workflow(
    OrderWorkflow.run,
    order,
    id=f"order-{order.id}",
    task_queue="orders",
    memo={
        "customer_name": order.customer_name,
        "notes": "Priority customer",
    },
)

# Read memo from workflow
@workflow.defn
class OrderWorkflow:
    @workflow.run
    async def run(self, order: Order) -> str:
        notes: str = workflow.memo_value("notes", type_hint=str)
        ...

Deterministic APIs for Values

Use these APIs within workflows for deterministic random values and UUIDs:

@workflow.defn
class MyWorkflow:
    @workflow.run
    async def run(self) -> str:
        # Deterministic UUID (same on replay)
        unique_id = workflow.uuid4()

        # Deterministic random (same on replay)
        rng = workflow.random()
        value = rng.randint(1, 100)

        return str(unique_id)

Best Practices

Use Pydantic for input/output validation
Keep payloads small—see references/core/gotchas.md for limits
Encrypt sensitive data with PayloadCodec
Use dataclasses for simple data structures
Use workflow.uuid4() and workflow.random() for deterministic values

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/data-handling.md

Python SDK Data Handling

Overview

Default Data Converter

Pydantic Integration

Custom Data Conversion

Payload Encryption

Search Attributes

Querying Workflows by Search Attributes

Workflow Memo

Deterministic APIs for Values

Best Practices

Requirements and compatibility

Security

Attribution and licence

Related skills

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MCP Builder

Schema

Postgresql Table Design

SQL Optimization Patterns

API Scaffolding Fastapi Pro

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