Temporal Developer
OpenAIOfficialCreated by TemporalDistributed by OpenAI
- Skill
- Tool integration
- Software development
- MIT
- Low risk
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/downloadUnzip 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.mdTypeScript -> read
references/typescript/typescript.mdGo -> read
references/go/go.mdJava -> read
references/java/java.md.NET (C#) -> read
references/dotnet/dotnet.mdRuby -> read
references/ruby/ruby.mdRust -> read
references/rust/rust.md(in Public Preview)
Second, read appropriate
coreand language-specific references for the task at hand.
Primary References
references/core/determinism.md- Why determinism matters, replay mechanics, basic concepts of activitiesLanguage-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 mistakesLanguage-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 runningLanguage-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 proceduresreferences/core/error-reference.md- Common error types, workflow status referencereferences/core/interactive-workflows.md- Testing signals, updates, queriesreferences/core/dev-management.md- Dev cycle & management of server and workersreferences/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 conceptsLanguage-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 Temporalreferences/{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
- ATTRIBUTION.mdAdded by AutoGineer900 B
- LICENSELicence file added by AutoGineer1.1 KiB
- SKILL.md7.4 KiB
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references/typescript/gotchas.md
TypeScript Gotchas
TypeScript-specific mistakes and anti-patterns. See also Common Gotchas for language-agnostic concepts.
Activity Imports
Importing Implementations Instead of Types
The Problem: Importing activity implementations brings Node.js code into the V8 workflow sandbox, causing bundling errors or runtime failures.
// BAD - Brings actual code into workflow sandbox
import * as activities from './activities';
const { greet } = proxyActivities<typeof activities>({
startToCloseTimeout: '1 minute',
});
// GOOD - Type-only import
import type * as activities from './activities';
const { greet } = proxyActivities<typeof activities>({
startToCloseTimeout: '1 minute',
});Importing Node.js Modules in Workflows
// BAD - fs is not available in workflow sandbox
import * as fs from 'fs';
export async function myWorkflow(): Promise<void> {
const data = fs.readFileSync('file.txt'); // Will fail!
}
// GOOD - File I/O belongs in activities
export async function myWorkflow(): Promise<void> {
const data = await activities.readFile('file.txt');
}Bundling Issues
Using workflowsPath in Production
workflowsPath runs the bundler at Worker startup, which is slow and not suitable for production. Use workflowBundle with pre-bundled code instead.
// OK for development/testing, BAD for production - bundles at startup
const worker = await Worker.create({
workflowsPath: require.resolve('./workflows'),
// ...
});
// GOOD for production - use pre-bundled code
import { bundleWorkflowCode } from '@temporalio/worker';
// Build step (run once at build time)
const bundle = await bundleWorkflowCode({
workflowsPath: require.resolve('./workflows'),
});
await fs.promises.writeFile('./workflow-bundle.js', bundle.code);
// Worker startup (fast, no bundling)
const worker = await Worker.create({
workflowBundle: {
codePath: require.resolve('./workflow-bundle.js'),
},
// ...
});Missing Dependencies in Workflow Bundle
// If using external packages in workflows, ensure they're bundled
// worker.ts
const worker = await Worker.create({
workflowsPath: require.resolve('./workflows'),
bundlerOptions: {
// Exclude Node.js-only packages that cause bundling errors
// WARNING: Modules listed here will be completely unavailable
// at workflow runtime - any imports will fail
ignoreModules: ['some-node-only-package'],
},
});Package Version Mismatches
All @temporalio/* packages must have the same version. This can be verified by running npm ls or the appropriate command for your package manager.
Package Version Constraints - Prod vs. Non-Prod
For production apps, you should use ~ version constraints (bug fixes only) on Temporal packages. For non-production apps, you may use ^ constraints (the npm default) instead.
Wrong Retry Classification
A common mistake is treating transient errors as permanent (or vice versa):
Transient errors (retry): network timeouts, temporary service unavailability, rate limits
Permanent errors (don't retry): invalid input, authentication failure, resource not found
// BAD: Retrying a permanent error
throw ApplicationFailure.create({ message: 'User not found' });
// This will retry indefinitely!
// GOOD: Mark permanent errors as non-retryable
throw ApplicationFailure.nonRetryable('User not found');For detailed guidance on error classification and retry policies, see error-handling.md.
Cancellation
Not Handling Workflow Cancellation
// BAD - Cleanup doesn't run on cancellation
export async function workflowWithCleanup(): Promise<void> {
await activities.acquireResource();
await activities.doWork();
await activities.releaseResource(); // Never runs if cancelled!
}
// GOOD - Use CancellationScope for cleanup
import { CancellationScope } from '@temporalio/workflow';
export async function workflowWithCleanup(): Promise<void> {
await activities.acquireResource();
try {
await activities.doWork();
} finally {
// Run cleanup even on cancellation
await CancellationScope.nonCancellable(async () => {
await activities.releaseResource();
});
}
}Not Handling Activity Cancellation
Activities must opt in to receive cancellation. This requires:
Heartbeating - Cancellation is delivered via heartbeat
Checking for cancellation - Either await
Context.current().cancelledor usecancellationSignal()
// BAD - Activity ignores cancellation
export async function longActivity(): Promise<void> {
await doExpensiveWork(); // Runs to completion even if cancelled
}// GOOD - Heartbeat in background and race work against cancellation promise
import { Context, CancelledFailure } from '@temporalio/activity';
export async function longActivity(): Promise<void> {
// Heartbeat in background so cancellation can be delivered
let heartbeatEnabled = true;
(async () => {
while (heartbeatEnabled) {
await Context.current().sleep(5000);
Context.current().heartbeat();
}
})().catch(() => {});
try {
await Promise.race([
Context.current().cancelled, // Rejects with CancelledFailure
doExpensiveWork(),
]);
} catch (err) {
if (err instanceof CancelledFailure) {
await cleanup();
}
throw err;
} finally {
heartbeatEnabled = false;
}
}// GOOD - Use AbortSignal with libraries that support it
import fetch from 'node-fetch';
import { cancellationSignal, heartbeat } from '@temporalio/activity';
import type { AbortSignal as FetchAbortSignal } from 'node-fetch/externals';
export async function cancellableFetch(url: string): Promise<Uint8Array> {
const response = await fetch(url, { signal: cancellationSignal() as FetchAbortSignal });
const contentLength = parseInt(response.headers.get('Content-Length')!);
let bytesRead = 0;
const chunks: Buffer[] = [];
for await (const chunk of response.body) {
if (!(chunk instanceof Buffer)) throw new TypeError('Expected Buffer');
bytesRead += chunk.length;
chunks.push(chunk);
heartbeat(bytesRead / contentLength); // Heartbeat to keep cancellation delivery alive
}
return Buffer.concat(chunks);
}Note: Promise.race doesn't stop the losing promise—it continues running. Use cancellationSignal() or explicitly abort sub-operations when cleanup requires stopping in-flight work.
Heartbeating
Forgetting to Heartbeat Long Activities
// BAD - No heartbeat, can't detect stuck activities
export async function processLargeFile(path: string): Promise<void> {
for await (const chunk of readChunks(path)) {
await processChunk(chunk); // Takes hours, no heartbeat
}
}
// GOOD - Regular heartbeats with progress
import { heartbeat } from '@temporalio/activity';
export async function processLargeFile(path: string): Promise<void> {
let i = 0;
for await (const chunk of readChunks(path)) {
heartbeat(`Processing chunk ${i++}`);
await processChunk(chunk);
}
}Heartbeat Timeout Too Short
// BAD - Heartbeat timeout shorter than processing time
const { processChunk } = proxyActivities<typeof activities>({
startToCloseTimeout: '30 minutes',
heartbeatTimeout: '10 seconds', // Too short!
});
// GOOD - Heartbeat timeout allows for processing variance
const { processChunk } = proxyActivities<typeof activities>({
startToCloseTimeout: '30 minutes',
heartbeatTimeout: '2 minutes',
});Set heartbeat timeout as high as acceptable for your use case — each heartbeat counts as an action.
Testing
Not Testing Failures
import { TestWorkflowEnvironment } from '@temporalio/testing';
import { Worker } from '@temporalio/worker';
test('handles activity failure', async () => {
const env = await TestWorkflowEnvironment.createTimeSkipping();
const worker = await Worker.create({
connection: env.nativeConnection,
taskQueue: 'test',
workflowsPath: require.resolve('./workflows'),
activities: {
// Activity that always fails
riskyOperation: async () => {
throw ApplicationFailure.nonRetryable('Simulated failure');
},
},
});
await worker.runUntil(async () => {
await expect(
env.client.workflow.execute(riskyWorkflow, {
workflowId: 'test-failure',
taskQueue: 'test',
})
).rejects.toThrow('Simulated failure');
});
await env.teardown();
});Not Testing Replay
import { Worker } from '@temporalio/worker';
import * as fs from 'fs';
test('replay compatibility', async () => {
const history = JSON.parse(await fs.promises.readFile('./fixtures/workflow_history.json', 'utf8'));
// Fails if current code is incompatible with history
await Worker.runReplayHistory(
{
workflowsPath: require.resolve('./workflows'),
},
history,
);
});Timers and Sleep
setTimeout works in workflows (the SDK mocks it), but sleep() from @temporalio/workflow is preferred because its interaction with cancellation scopes is more intuitive. See Timers in references/typescript/patterns.md.
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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