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
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references/python/patterns.md
Python SDK Patterns
Signals
@workflow.defn
class OrderWorkflow:
def __init__(self):
self._approved = False
self._items = []
@workflow.signal
async def approve(self) -> None:
self._approved = True
@workflow.signal
async def add_item(self, item: str) -> None:
self._items.append(item)
@workflow.run
async def run(self) -> str:
# Wait for approval
await workflow.wait_condition(lambda: self._approved)
return f"Processed {len(self._items)} items"Dynamic Signal Handlers
For handling signals with names not known at compile time. Use cases for this pattern are rare — most workflows should use statically defined signal handlers.
@workflow.defn
class DynamicSignalWorkflow:
def __init__(self):
self._signals: dict[str, list[Any]] = {}
@workflow.signal(dynamic=True)
async def handle_signal(self, name: str, args: Sequence[RawValue]) -> None:
if name not in self._signals:
self._signals[name] = []
self._signals[name].append(workflow.payload_converter().from_payload(args[0]))Queries
Important: Queries must NOT modify workflow state or have side effects.
@workflow.defn
class StatusWorkflow:
def __init__(self):
self._status = "pending"
self._progress = 0
@workflow.query
def get_status(self) -> str:
return self._status
@workflow.query
def get_progress(self) -> int:
return self._progress
@workflow.run
async def run(self) -> str:
self._status = "running"
for i in range(100):
self._progress = i
await workflow.execute_activity(
process_item, i,
start_to_close_timeout=timedelta(minutes=1)
)
self._status = "completed"
return "done"Dynamic Query Handlers
For handling queries with names not known at compile time. Use cases for this pattern are rare — most workflows should use statically defined query handlers.
@workflow.query(dynamic=True)
def handle_query(self, name: str, args: Sequence[RawValue]) -> Any:
if name == "get_field":
field_name = workflow.payload_converter().from_payload(args[0])
return getattr(self, f"_{field_name}", None)Updates
@workflow.defn
class OrderWorkflow:
def __init__(self):
self._items: list[str] = []
@workflow.update
async def add_item(self, item: str) -> int:
self._items.append(item)
return len(self._items) # Returns new count to caller
@add_item.validator
def validate_add_item(self, item: str) -> None:
if not item:
raise ValueError("Item cannot be empty")
if len(self._items) >= 100:
raise ValueError("Order is full")Important: Validators must NOT mutate workflow state or do anything blocking (no activities, sleeps, or other commands). They are read-only, similar to query handlers. Raise an exception to reject the update; return None to accept.
Child Workflows
@workflow.defn
class MyWorkflow:
@workflow.run
async def run(self, orders: list[Order]) -> list[str]:
results = []
for order in orders:
result = await workflow.execute_child_workflow(
ProcessOrderWorkflow.run,
order,
id=f"order-{order.id}",
# Control what happens to child when parent completes
parent_close_policy=workflow.ParentClosePolicy.ABANDON,
)
results.append(result)
return resultsHandles to External Workflows
@workflow.defn
class MyWorkflow:
@workflow.run
async def run(self, target_workflow_id: str) -> None:
# Get handle to external workflow
handle = workflow.get_external_workflow_handle(target_workflow_id)
# Signal the external workflow
await handle.signal(TargetWorkflow.data_ready, data_payload)
# Or cancel it
await handle.cancel()Parallel Execution
@workflow.defn
class MyWorkflow:
@workflow.run
async def run(self, items: list[str]) -> list[str]:
# Execute activities in parallel
tasks = [
workflow.execute_activity(
process_item, item,
start_to_close_timeout=timedelta(minutes=5)
)
for item in items
]
return await asyncio.gather(*tasks)Deterministic Alternatives to asyncio
Generally, asyncio is OK to use in Temoral workflows. But some asyncio calls are non-deterministic. Use Temporal's deterministic alternatives for safer concurrent operations:
# workflow.wait() - like asyncio.wait()
done, pending = await workflow.wait(
futures,
return_when=workflow.WaitConditionResult.FIRST_COMPLETED
)
# workflow.as_completed() - like asyncio.as_completed()
async for future in workflow.as_completed(futures):
result = await future
# Process each result as it completesContinue-as-New
@workflow.defn
class MyWorkflow:
@workflow.run
async def run(self, state: WorkflowState) -> str:
while True:
state = await process_batch(state)
if state.is_complete:
return "done"
# Continue with fresh history before hitting limits
if workflow.info().is_continue_as_new_suggested():
workflow.continue_as_new(args=[state])Saga Pattern (Compensations)
Important: Compensation activities should be idempotent - they may be retried (as with ALL activities).
@workflow.defn
class MyWorkflow:
@workflow.run
async def run(self, order: Order) -> str:
compensations: list[Callable[[], Awaitable[None]]] = []
try:
# Note - we save the compensation before running the activity,
# because the following could happen:
# 1. reserve_inventory starts running
# 2. it does successfully reserve inventory
# 3. but then fails for some other reason (timeout, reporting metrics, etc.)
# 4. in that case, the activity would have failed, but we still did the effect of reserving inventory
# So, we need to make sure we have a compensation already on the stack to handle that.
# This means the compensation needs to handle both the cases of reserved or unreserved inventory.
compensations.append(lambda: workflow.execute_activity(
release_inventory_if_reserved, order,
start_to_close_timeout=timedelta(minutes=5)
))
await workflow.execute_activity(
reserve_inventory, order,
start_to_close_timeout=timedelta(minutes=5)
)
compensations.append(lambda: workflow.execute_activity(
refund_payment_if_charged, order,
start_to_close_timeout=timedelta(minutes=5)
))
await workflow.execute_activity(
charge_payment, order,
start_to_close_timeout=timedelta(minutes=5)
)
await workflow.execute_activity(
ship_order, order,
start_to_close_timeout=timedelta(minutes=5)
)
return "Order completed"
except Exception as e:
workflow.logger.error(f"Order failed: {e}, running compensations")
# asyncio.shield ensures compensations run even if the workflow is cancelled.
async def run_compensations():
for compensate in reversed(compensations):
try:
await compensate()
except Exception as comp_err:
workflow.logger.error(f"Compensation failed: {comp_err}")
await asyncio.shield(asyncio.ensure_future(run_compensations()))
raiseCancellation Handling - leverages standard asyncio cancellation
@workflow.defn
class MyWorkflow:
@workflow.run
async def run(self) -> str:
try:
await workflow.execute_activity(
long_running_activity,
start_to_close_timeout=timedelta(hours=1),
)
return "completed"
except asyncio.CancelledError:
# Workflow was cancelled - perform cleanup
workflow.logger.info("Workflow cancelled, running cleanup")
# Cleanup activities still run even after cancellation
await workflow.execute_activity(
cleanup_activity,
start_to_close_timeout=timedelta(minutes=5),
)
raise # Re-raise to mark workflow as cancelledWait Condition with Timeout
@workflow.defn
class MyWorkflow:
@workflow.run
async def run(self) -> str:
self._approved = False
# Wait for approval with 24-hour timeout
try:
await workflow.wait_condition(
lambda: self._approved,
timeout=timedelta(hours=24)
)
return "approved"
except asyncio.TimeoutError:
return "auto-rejected due to timeout"Waiting for All Handlers to Finish
Signal and update handlers should generally be non-async (avoid running activities from them). Otherwise, the workflow may complete before handlers finish their execution. However, making handlers non-async sometimes requires workarounds that add complexity.
When async handlers are necessary, use wait_condition(all_handlers_finished) at the end of your workflow (or before continue-as-new) to prevent completion until all pending handlers complete.
@workflow.defn
class MyWorkflow:
@workflow.run
async def run(self) -> str:
# ... main workflow logic ...
# Before exiting, wait for all handlers to finish
await workflow.wait_condition(workflow.all_handlers_finished)
return "done"Activity Heartbeat Details
WHY:
Support activity cancellation - Cancellations are delivered via heartbeat; activities that don't heartbeat won't know they've been cancelled
Resume progress after worker failure - Heartbeat details persist across retries
Cancellation exceptions:
Async activities:
asyncio.CancelledErrorSync threaded activities:
temporalio.exceptions.CancelledError
WHEN:
Cancellable activities - Any activity that should respond to cancellation
Long-running activities - Track progress for resumability
Checkpointing - Save progress periodically
from temporalio.exceptions import CancelledError
@activity.defn
def process_large_file(file_path: str) -> str:
# Get heartbeat details from previous attempt (if any)
heartbeat_details = activity.info().heartbeat_details
start_line = heartbeat_details[0] if heartbeat_details else 0
try:
with open(file_path) as f:
for i, line in enumerate(f):
if i < start_line:
continue # Skip already processed lines
process_line(line)
# Heartbeat with progress
# If cancelled, heartbeat() raises CancelledError
activity.heartbeat(i + 1)
return "completed"
except CancelledError:
# Perform cleanup on cancellation
cleanup()
raiseTimers
@workflow.defn
class MyWorkflow:
@workflow.run
async def run(self) -> str:
await workflow.sleep(timedelta(hours=1))
return "Timer fired"Local Activities
Purpose: Reduce latency for short, lightweight operations by skipping the task queue. ONLY use these when necessary for performance. Do NOT use these by default, as they are not durable and distributed.
@workflow.defn
class MyWorkflow:
@workflow.run
async def run(self) -> str:
result = await workflow.execute_local_activity(
quick_lookup,
"key",
start_to_close_timeout=timedelta(seconds=5),
)
return resultUsing Pydantic Models
See references/python/data-handling.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
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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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