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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/integrations/braintrust.md

Temporal Braintrust Integration (Python)

Overview

Braintrust is an LLM observability and prompt-management platform. The Temporal Python SDK integrates with it through braintrust.contrib.temporal.BraintrustPlugin, which traces every Workflow and Activity as a span in Braintrust and links client-initiated spans to the Workflows they start.

[!NOTE] This feature is 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.

For Python AI patterns (Pydantic data converter, disabling client-side LLM retries, generic LLM Activity shape) read references/python/ai-patterns.md. For conceptual LLM patterns shared across SDKs read references/core/ai-patterns.md.

Prerequisites

  • An existing Temporal Python development environment as described in references/python/python.md.

Install

uv add "braintrust[temporal]"

Initialize the logger before the Client or Worker

The Braintrust logger must be initialized before the Temporal Client and Worker are constructed so that spans connect correctly.

import os
from braintrust import init_logger

init_logger(project=os.environ.get("BRAINTRUST_PROJECT", "my-project"))

init_logger takes a project argument that names the Braintrust project traces are written to.

Register BraintrustPlugin on the Client and the Worker

Register BraintrustPlugin on both the Client and every Worker. The Worker registration produces Workflow/Activity spans; the Client registration propagates span context so client-side spans link to the Workflow they start.

Client:

from temporalio.client import Client
from braintrust.contrib.temporal import BraintrustPlugin

client = await Client.connect(
    "localhost:7233",
    plugins=[BraintrustPlugin()],
)

Worker:

from braintrust.contrib.temporal import BraintrustPlugin
from temporalio.worker import Worker

worker = Worker(
    client,
    task_queue="my-task-queue",
    workflows=[MyWorkflow],
    activities=[my_activity],
    plugins=[BraintrustPlugin()],
)

API credentials

The Worker process needs BRAINTRUST_API_KEY in its environment. The Client process that starts Workflow Executions does not need the Braintrust API key.

export BRAINTRUST_API_KEY="your-api-key"
python worker.py

Trace LLM calls with wrap_openai

Wrap the OpenAI client with braintrust.wrap_openai so every chat/completion call is captured as a span with inputs, outputs, token counts, and latency. Pass max_retries=0 so Temporal — not the OpenAI client — owns retries.

from braintrust import wrap_openai
from openai import AsyncOpenAI
from temporalio import activity

@activity.defn
async def invoke_model(prompt: str) -> str:
    client = wrap_openai(AsyncOpenAI(max_retries=0))

    response = await client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": prompt},
        ],
    )

    return response.choices[0].message.content

The resulting trace nests the OpenAI span under the Activity span, which sits under the Workflow span, which sits under the client-side span:

my-workflow-request (client span)
└── temporal.workflow.MyWorkflow
    └── temporal.activity.invoke_model
        └── Chat Completion (gpt-4o)

Add custom spans with start_span

Use braintrust.start_span from client code to capture application-level context (the user query, the final result) alongside the Workflow/Activity spans the plugin produces.

import uuid
from braintrust import start_span

async def run_research(query: str):
    with start_span(name="research-request", type="task") as span:
        span.log(input={"query": query})

        result = await client.execute_workflow(
            ResearchWorkflow.run,
            query,
            id=f"research-{uuid.uuid4()}",
            task_queue="research-task-queue",
        )

        span.log(output={"result": result})
        return result

Manage prompts with load_prompt

braintrust.load_prompt(project=..., slug=...) fetches a prompt managed in the Braintrust UI, so prompt edits go live without redeploying Workflow or Activity code. Call it from an Activity (model calls live in Activities), then call prompt.build() to get the prompt configuration; extract the message you need before invoking the LLM.

import os
import braintrust
from braintrust import wrap_openai
from openai import AsyncOpenAI
from temporalio import activity

@activity.defn
async def invoke_model(prompt_slug: str, user_input: str) -> str:
    prompt = braintrust.load_prompt(
        project=os.environ.get("BRAINTRUST_PROJECT", "my-project"),
        slug=prompt_slug,
    )

    built = prompt.build()

    system_content = "You are a helpful assistant."
    for msg in built.get("messages", []):
        if msg.get("role") == "system" and msg.get("content"):
            system_content = msg["content"]
            break

    client = wrap_openai(AsyncOpenAI(max_retries=0))

    response = await client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": system_content},
            {"role": "user", "content": user_input},
        ],
    )

    return response.choices[0].message.content
Fallback prompt for resilience

Wrap load_prompt in a try/except and fall back to a hardcoded prompt so the Activity still runs if Braintrust is unreachable.

DEFAULT_SYSTEM_PROMPT = "You are a helpful assistant."

try:
    prompt = braintrust.load_prompt(project="my-project", slug="my-prompt")
    system_content = extract_system_message(prompt.build())
except Exception as e:
    activity.logger.warning(f"Failed to load prompt: {e}. Using fallback.")
    system_content = DEFAULT_SYSTEM_PROMPT

Common mistakes

  • Initializing the Braintrust logger after constructing the Client or Worker. Call init_logger(...) first; otherwise spans don't connect to the Worker process.

  • Registering BraintrustPlugin on only the Worker (or only the Client). Register on both — the Client registration is what links client-side spans to Workflow executions.

  • Forgetting max_retries=0 on the wrapped OpenAI client. Temporal owns retries; leaving the OpenAI client's built-in retries on duplicates work and obscures retry counts in traces.

  • Calling load_prompt from inside a Workflow. Prompt loading is an external I/O call; keep it in an Activity.

  • Setting BRAINTRUST_API_KEY only on the Client process. The Worker is what calls Braintrust; the Client doesn't need the key.

Additional Resources

  • references/python/ai-patterns.md — Python LLM patterns (Pydantic, retry discipline, generic LLM Activity shape).

  • references/core/ai-patterns.md — Conceptual LLM patterns shared across SDKs.

  • Deep research sample — end-to-end agent showing BraintrustPlugin, wrap_openai, start_span, and load_prompt.

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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