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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/core/priority-fairness.md

Task Queue Priority and Fairness

Overview

Priority and Fairness control how Tasks are distributed within a Task Queue. Priority determines execution order. Fairness prevents one group of Tasks from starving others. They can be used independently or together.

Both features are in Public Preview. Priority is free. Fairness is a paid feature in Temporal Cloud.

Priority

Priority lets you control execution order within a single Task Queue by assigning a priority key (integer 1-5, lower = higher priority). Each priority level acts as a sub-queue. All priority-1 Tasks dispatch before priority-2, and so on. Tasks at the same priority level dispatch in FIFO order.

Default priority is 3. Activities inherit their parent workflow's priority unless explicitly overridden.

When to use Priority

Use Priority to differentiate execution order between types of work sharing a single Task Queue and Worker pool. For example, process payment-related Tasks before less time-sensitive inventory management Tasks, or ensure real-time Tasks run ahead of batch Tasks. You can also use it to run urgent Tasks immediately by assigning them priority 1.

CLI
temporal workflow start \
  --type ChargeCustomer \
  --task-queue my-task-queue \
  --workflow-id my-workflow-id \
  --input '{"customerId":"12345"}' \
  --priority-key 1
Go
workflowOptions := client.StartWorkflowOptions{
  ID:        "my-workflow-id",
  TaskQueue: "my-task-queue",
  Priority:  temporal.Priority{PriorityKey: 1},
}
we, err := c.ExecuteWorkflow(context.Background(), workflowOptions, MyWorkflow)
Java
WorkflowOptions options = WorkflowOptions.newBuilder()
  .setTaskQueue("my-task-queue")
  .setPriority(Priority.newBuilder().setPriorityKey(1).build())
  .build();
Python
await client.start_workflow(
  MyWorkflow.run,
  args="hello",
  id="my-workflow-id",
  task_queue="my-task-queue",
  priority=Priority(priority_key=1),
)
TypeScript
const handle = await startWorkflow(workflows.myWorkflow, {
  args: [false, 1],
  priority: { priorityKey: 1 },
});
.NET
var handle = await Client.StartWorkflowAsync(
  (MyWorkflow wf) => wf.RunAsync("hello"),
  new StartWorkflowOptions(id: "my-workflow-id", taskQueue: "my-task-queue")
  {
    Priority = new Priority(1),
  }
);

Fairness

Fairness prevents one group of Tasks from monopolizing Worker capacity. Each fairness key creates a "virtual queue" within the Task Queue. The server uses round-robin dispatch across virtual queues so no single key can block others, even with a much larger backlog.

When to use Fairness

Fairness solves the multi-tenant starvation problem. Without it, Tasks dispatch FIFO: if tenant-big enqueues 100k Tasks, tenant-small's 10 Tasks sit behind the entire backlog. With Fairness, each tenant gets its own virtual queue and Tasks are interleaved.

Common scenarios:

  • Multi-tenant applications where large tenants should not block small ones.

  • Tiered capacity bands where you want weighted distribution (e.g., 80% premium, 20% free) without limiting overall throughput when one band is empty.

  • Batch jobs where some jobs run far more frequently than others.

  • Multi-vendor processing where a few vendors generate the majority of work.

If all your Tasks can be dispatched immediately (no backlog), you don't need Fairness.

Fairness applies at Task dispatch time and considers each Task as having equal cost until dispatch. It does not account for Tasks currently being processed by Workers. So if you look at Tasks being processed by Workers, you might not see "fairness" across tenants — for example, if tenant-big already has Tasks being processed when tenant-small's Tasks are dispatched, it may still appear that tenant-big is using the most resources.

Fairness keys and weights

A fairness key is a string, typically a tenant ID or workload category. Each unique key creates a virtual queue.

A fairness weight (float, default 1.0) controls how often a key's Tasks are dispatched relative to others. A key with weight 2.0 dispatches twice as often as keys with weight 1.0.

Example with three tiers:

Fairness KeyWeightShare of Dispatches
premium-tier5.050%
basic-tier3.030%
free-tier2.020%

Tasks without a fairness key are grouped under an implicit empty-string key with weight 1.0. Adoption is incremental: unkeyed Tasks participate in round-robin alongside keyed Tasks.

Using Fairness with Priority

When combined, Priority determines which sub-queue Tasks go into (priority 1 before 2, etc.), and Fairness applies within each priority level.

SDK examples
CLI
temporal workflow start \
  --type ChargeCustomer \
  --task-queue my-task-queue \
  --workflow-id my-workflow-id \
  --input '{"customerId":"12345"}' \
  --priority-key 1 \
  --fairness-key tenant-123 \
  --fairness-weight 2.0
Go
workflowOptions := client.StartWorkflowOptions{
  ID:        "my-workflow-id",
  TaskQueue: "my-task-queue",
  Priority: temporal.Priority{
    PriorityKey:    1,
    FairnessKey:    "tenant-123",
    FairnessWeight: 2.0,
  },
}
we, err := c.ExecuteWorkflow(context.Background(), workflowOptions, MyWorkflow)

Activities:

ao := workflow.ActivityOptions{
  StartToCloseTimeout: time.Minute,
  Priority: temporal.Priority{
    PriorityKey:    1,
    FairnessKey:    "tenant-123",
    FairnessWeight: 2.0,
  },
}
ctx := workflow.WithActivityOptions(ctx, ao)
err := workflow.ExecuteActivity(ctx, MyActivity).Get(ctx, nil)
Java
WorkflowOptions options = WorkflowOptions.newBuilder()
  .setTaskQueue("my-task-queue")
  .setPriority(Priority.newBuilder()
    .setPriorityKey(1)
    .setFairnessKey("tenant-123")
    .setFairnessWeight(2.0)
    .build())
  .build();
Python
await client.start_workflow(
  MyWorkflow.run,
  args="hello",
  id="my-workflow-id",
  task_queue="my-task-queue",
  priority=Priority(priority_key=1, fairness_key="tenant-123", fairness_weight=2.0),
)

Activities:

await workflow.execute_activity(
  say_hello,
  "hi",
  priority=Priority(priority_key=1, fairness_key="tenant-123", fairness_weight=2.0),
  start_to_close_timeout=timedelta(seconds=5),
)
TypeScript
const handle = await startWorkflow(workflows.myWorkflow, {
  args: [false, 1],
  priority: { priorityKey: 1, fairnessKey: 'tenant-123', fairnessWeight: 2.0 },
});
.NET
var handle = await Client.StartWorkflowAsync(
  (MyWorkflow wf) => wf.RunAsync("hello"),
  new StartWorkflowOptions(id: "my-workflow-id", taskQueue: "my-task-queue")
  {
    Priority = new Priority(
      priorityKey: 1,
      fairnessKey: "tenant-123",
      fairnessWeight: 2.0
    )
  }
);
Child Workflows

Child workflows can set their own priority and fairness, overriding the parent.

Go:

cwo := workflow.ChildWorkflowOptions{
  WorkflowID: "child-workflow-id",
  TaskQueue:  "child-task-queue",
  Priority: temporal.Priority{
    PriorityKey:    1,
    FairnessKey:    "tenant-123",
    FairnessWeight: 2.0,
  },
}
ctx := workflow.WithChildOptions(ctx, cwo)
err := workflow.ExecuteChildWorkflow(ctx, MyChildWorkflow).Get(ctx, nil)

Java:

ChildWorkflowOptions childOptions = ChildWorkflowOptions.newBuilder()
  .setTaskQueue("child-task-queue")
  .setWorkflowId("child-workflow-id")
  .setPriority(Priority.newBuilder()
    .setPriorityKey(1)
    .setFairnessKey("tenant-123")
    .setFairnessWeight(2.0)
    .build())
  .build();
MyChildWorkflow child = Workflow.newChildWorkflowStub(MyChildWorkflow.class, childOptions);
child.run();

Python:

await workflow.execute_child_workflow(
  MyChildWorkflow.run,
  args="hello child",
  priority=Priority(priority_key=1, fairness_key="tenant-123", fairness_weight=2.0),
)

TypeScript:

const handle = await startChildWorkflow(workflows.myChildWorkflow, {
  args: [false, 1],
  priority: { priorityKey: 1, fairnessKey: 'tenant-123', fairnessWeight: 2.0 },
});

.NET:

await Workflow.ExecuteChildWorkflowAsync(
  (MyChildWorkflow wf) => wf.RunAsync("hello child"),
  new() {
    Priority = new(
      priorityKey: 1,
      fairnessKey: "tenant-123",
      fairnessWeight: 2.0
    )
  }
);
Rate limiting

Two rate-limiting controls work alongside Fairness:

  • queue-rps-limit — overall dispatch rate for the entire Task Queue.

  • fairness-key-rps-limit-default — per-key rate limit, scaled by weight. If the default is 10 rps and a key has weight 2.5, that key's effective limit is 25 rps.

temporal task-queue config set \
    --task-queue my-task-queue \
    --task-queue-type activity \
    --namespace my-namespace \
    --queue-rps-limit 500 \
    --queue-rps-limit-reason "overall limit" \
    --fairness-key-rps-limit-default 33.3 \
    --fairness-key-rps-limit-reason "per-key limit"

If both limits are set, the more restrictive one applies.

Fairness weight overrides

You can override the weights of up to 1000 keys through the config API. When an override is set for a key, the SDK-supplied weight is ignored. Overrides are per Task Queue and type (workflow vs. activity), so set them for both if needed.

Enabling Fairness

When you start using fairness keys, it switches your active Task Queues to fairness mode. Existing queued Tasks are processed before any new fairness-mode ones.

Temporal Cloud: automatically enabled when you start using fairness keys.

Self-hosted: set these dynamic config flags to true:

  • matching.useNewMatcher

  • matching.enableFairness

  • matching.enableMigration (to drain existing backlogs after enabling)

Limitations

  • Accuracy can degrade with a very large number of distinct fairness keys.

  • Task Queue partitioning can interfere with fairness distribution. Contact Temporal Support to set a Task Queue to a single partition if needed.

  • Weights apply at schedule time, not dispatch time. Changing a weight does not reorder already-backlogged Tasks.

  • Fairness is not guaranteed across different Worker versions when using Worker Versioning.

  • After server restarts, less-active keys may briefly dispatch new Tasks ahead of their existing backlog until ordering normalizes.

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

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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)
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