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Temporal Ops

OpenAIOfficialCreated by TemporalDistributed by OpenAI

  • Skill
  • Tool integration
  • Cloud and DevOps
  • MIT
  • No issues found

From the author

Administer and diagnose running Temporal Cloud or self-hosted Temporal Server environments via CLI (temporal, tcld) — not SDK code. Operations: namespace CRUD, Cloud capacity/APS, API-key rotation, mTLS cert rotation, workflow health, batch cancel/terminate/reset, export, search attributes, Ops API, billing, audit logs, Terraform, SAML/SCIM, migration. Diagnosis: bottom-up triage of stuck workflows, non-determinism, worker-health, task-queue problems, HA failover, payload size limits, performance bottlenecks, missed schedules. Do NOT trigger for generic TLS/gRPC errors unrelated to Temporal, writing application code (temporal-developer), or worker tuning/sizing (temporal-workertuning).
— the description by Temporal

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach to managing and troubleshooting Temporal environments. It covers two primary modes: Operations, for administrative tasks like namespace management, API key rotation, and batch workflow actions; and Diagnosis, for investigating issues like stuck workflows, connectivity errors, and performance bottlenecks. It enforces strict operational discipline, including explicit confirmation for destructive commands and a bottom-up diagnostic methodology to ensure root causes are identified accurately.

What it does

  • Performs administrative operations on Temporal Cloud via tcld and self-hosted clusters via temporal operator.
  • Executes batch operations on workflows using query-based filtering with safety-first confirmation steps.
  • Diagnoses system issues using a layered, bottom-up approach from network connectivity to workflow logic.
  • Manages security configurations including API keys, mTLS certificates, and SAML/SCIM integrations.
  • Provides guidance on interpreting audit logs and cloud capacity metrics.
  • Routes complex tasks to specialized skills for development, worker tuning, or infrastructure configuration.

When to use it

  • When you need to perform administrative tasks like creating namespaces or rotating credentials.
  • When a Temporal environment is experiencing errors such as connection timeouts or authentication failures.
  • When you need to investigate or recover from stuck, non-deterministic, or unhealthy workflows.
  • When you need to perform bulk operations on workflows and require a safe, verified execution process.
  • When you need to audit or monitor the health and capacity of your Temporal Cloud account.

Advantages

  • Enforces safe operational practices by requiring explicit approval for destructive commands.
  • Provides a systematic diagnostic ladder to avoid speculative fixes.
  • Distinguishes clearly between Cloud and self-hosted administrative procedures.
  • Integrates safety checks like counting affected workflows before executing batch mutations.
  • Standardizes the interpretation of common gRPC and Temporal-specific error codes.

Usage examples

“How do I rotate my mTLS certificates for my Temporal Cloud namespace?”
The agent will guide you through the tcld commands for generating new certificates and updating the namespace configuration, ensuring you follow the correct rotation sequence.
“I am seeing 'context deadline exceeded' errors in my workflow. How do I debug this?”
The agent will initiate a bottom-up diagnosis, starting by verifying network connectivity and gRPC health before investigating specific workflow task execution logs.
“Terminate all workflows of type 'OrderProcessing' that are currently stuck.”
The agent will first run a count to identify the number of affected workflows, present this number to you for confirmation, and then execute the batch termination only after your approval.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/openai/temporal-ops/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip temporal-ops.zip -d .agents/skills

Documentation by Temporal

Skill: temporal-ops

Overview

This skill operates and diagnoses Temporal environments. It has two modes:

  • Operations: the user wants to do something — create a namespace, rotate a key, check capacity, find unhealthy workflows, cancel a batch, set up export. The skill executes the right commands and interprets the output.

  • Diagnosis: the user arrives with a symptom — a stuck workflow, a cert error, a connection timeout, a non-determinism panic. The skill routes the investigation through a layered, bottom-up diagnosis until a root cause is identified with a confidence score.

It does not teach how to write workflows or activities (use skill-temporal-developer for that), and it does not reproduce exhaustive CLI flag tables — run temporal <cmd> --help for those, and see cli-conventions.md for cross-command CLI conventions. The boundary is: if the user needs to administer or troubleshoot a running Temporal environment, this skill applies.

Out of scope

  • Writing workflows, activities, or SDK code → skill-temporal-developer.

  • Exhaustive CLI flags / command reference → run temporal <cmd> --help; cross-command CLI conventions → cli-conventions.md.

  • Worker performance tuning, sizing, capacity planning → skill-temporal-workertuning.

  • Helm, Kubernetes, database admin, monitoring stack config for self-hosted — beyond the CLI surface.

If the conversation drifts into one of these areas, hand off to the relevant sibling skill rather than improvising.

Philosophy

Operator discipline

When the user wants to perform an operational task:

  1. Identify the intent and backend. Is this a Cloud operation (tcld) or a self-hosted operation (temporal operator)? Data-plane operations (temporal workflow, temporal batch, etc.) work on both. If the backend is ambiguous, ask before proceeding — do not assume Cloud or self-hosted and do not output environment-specific commands until you know.

  2. Execute commands and interpret output. Run the documented command, read the result, and report what it means — or act on it if the user asked for an action. Read-only commands (get, list, describe, count, show) run freely. Anything listed under Destructive operations is proposed to the user first.

  3. Verify the result. After a mutating operation, confirm the new state matches the user's intent.

Destructive operations

An operation belongs to this tier if it is irreversible (tcld namespace delete), revokes access for a live identity (tcld apikey delete), moves production traffic (tcld namespace failover), or fans out to every match (any --query form). Apply the test to the operation in front of you — this is a rule, not a list, and a command's absence from any list in this skill does not place it outside the tier.

For anything in the tier: gather the evidence and propose. Do not run it on your own initiative, and do not run one to find out what it would do. The reference file for each command states its specific blast radius; read that before proposing, not after.

  1. Blast radius as a number, not a description. For any --query form, run temporal workflow count --query '<query>' with the byte-identical query first and carry the result into the proposal. A filter with no narrowing predicate beyond ExecutionStatus="Running" matches every open Execution in the Namespace.

  2. Name the target. State the exact command, the target, and the Namespace it resolves to. Connection settings can come from TEMPORAL_* env vars or a config-file profile, so the target is frequently not visible in the command text. If the backend or Namespace was inferred from context rather than stated by the user, say so — a destructive command aimed at the wrong Namespace is the most common way this goes wrong.

  3. Ask explicitly, then run it so it completes. Put the command, the target, and — for any --query form — the count from step 1 to the user as a direct question, and wait for an answer. Once they approve, run it with --yes on the --query batch forms; that flag is what lets an approved batch finish, since the interactive prompt needs a terminal and without one the command reports user denied confirmation and does nothing. --yes belongs in a command the user approved, never in a retry of one that failed its prompt. Do not substitute a loop over single-target workflow terminate --workflow-id. Approval covers one command against one target; it does not carry to the next command, a widened query, or a second Namespace.

  4. Verify, and know the abort path. Re-run the corresponding get, describe, or count. A batch job drains asynchronously: temporal batch describe --job-id <id> shows how far it has gotten and temporal batch terminate --job-id <id> stops it before it reaches the rest of its matches.

When a reversible sibling reaches the same goal, propose it alongside: cancel lets Workflow cleanup code run where terminate does not; apikey disable is reversible where delete is not; accepted-client-ca add appends where set replaces.

Assume nothing in the environment will stop a destructive command on your behalf. Credential scope, command denylists, and confirmation prompts may or may not be configured, and their possible presence is not a reason to skip any step above — you are the safeguard the user is relying on.

Diagnostic discipline

When the user arrives with a failure or anomaly:

  1. Bottom-up diagnosis. Verify the lower layer before blaming the upper one. The layers, from bottom to top:

    1. DNS / network path

    2. TCP / port reachability

    3. TLS handshake

    4. Authentication (API key or mTLS client cert)

    5. gRPC health and Temporal frontend reachability

    6. Temporal namespace, task queues, workers

    7. Workflow code (determinism, signals, timers, child workflows)

    The full ladder lives in diagnostic-ladder.md.

  2. Always verify the next layer up rather than prescribing a speculative fix. If TLS works, prove auth works before blaming the workflow. If pollers are present, prove the workflow's last event before blaming the worker.

  3. Attach a confidence score (1-10) to every proposed diagnosis:

    • 9-10: symptoms, operation, and confirming signals line up cleanly.

    • 6-8: evidence is good but at least one alternative remains plausible.

    • 1-5: the issue is still ambiguous; the "fix" is the next discriminating check, not a root cause.

  4. Name ambiguity explicitly. Errors like context deadline exceeded are not self-describing, and a single code such as RESOURCE_EXHAUSTED can mean more than one condition (account-limit throttling vs. per-Workflow lock contention). Surface that, gather more context, and scope the next step narrowly.

These are skill conventions, not docs-derived facts.

Intent routing

Operations

Find the row that matches the user's intent. The reference file contains the commands and procedures.

IntentCategoryReference
Create, get, list, delete a Cloud namespaceCloud namespace admincloud-namespace-admin.md
Add/remove region, failover, HA configCloud namespace admincloud-namespace-admin.md
Set retention, tags, codec-server, connectivity rulesCloud namespace admincloud-namespace-admin.md
Add or rename Cloud search attributesCloud namespace admincloud-namespace-admin.md
Check current APS / capacity modeCloud capacitycloud-capacity.md
Switch On-Demand ↔ Provisioned, set TRUsCloud capacitycloud-capacity.md
Understand APS / RPS / OPS limits and throttlingCloud capacitycloud-capacity.md
Create, disable, enable, delete an API keyCloud IAMcloud-iam.md
Invite, list, remove users; set roles/permissionsCloud IAMcloud-iam.md
Manage user groups and service accountsCloud IAMcloud-iam.md
Generate mTLS certs, upload CA, set cert filtersCloud certscloud-certs.md
Rotate mTLS certificatesCloud certscloud-certs.md
Set up Workflow History Export (S3 / GCS)Cloud namespace admincloud-namespace-admin.md
Set up PrivateLink / PSC, manage connectivity rulesCloud connectivitycloud-connectivity.md
Self-hosted cluster health, describe, namespace CRUDSelf-hosted adminself-hosted-admin.md
Self-hosted search attributes, Nexus endpointsSelf-hosted adminself-hosted-admin.md
Check or manage a Cloud Nexus Endpoint's caller-Namespace allowlist; the 1,000-caller defaultCloud namespace admincloud-namespace-admin.md#tcld-nexus-endpoint-allowed-namespace
Find stuck/hung/unhealthy workflows via list queriesWorkflow healthworkflow-health.md
Task queue poller status, workflow countsWorkflow healthworkflow-health.md
Cancel, terminate, or reset workflowsWorkflow recoveryworkflow-stuck.md#recovery-commands
Bulk / batch operations on workflows (--query)CLI conventionscli-conventions.md
Schedule CRUD, time-spec, and operationsCLI conventionscli-conventions.md
Complete or fail an activity externallyCLI conventionscli-conventions.md
Cloud Ops API access, rate limits, Go SDKCloud Ops APIcloud-ops-api.md
View billing, generate billing report, cost attributionCloud billingcloud-billing.md
Audit Logs: view, query via API, configure sink (AWS/GCP)Cloud audit logscloud-audit-logs.md
Terraform provider: Namespace/User/SA/API Key/Nexus CRUDCloud Terraformcloud-terraform.md
Expiry alerts (cert, API key, credit), status pageCloud notificationscloud-notifications.md
SAML SSO, SCIM provisioning, IdP integrationCloud SAML/SCIMcloud-saml-scim.md
Migrate self-hosted to Cloud (automated or manual), migrate between Cloud regionsCloud migrationcloud-migration.md
End-to-end ops playbook (setup, rotation, audit, billing, Terraform)Ops recipesops/recipes.md
Diagnosis

Find the row that matches the user's symptom. Start the investigation at the first check, then read the linked reference.

SymptomCategoryFirst checkReference
connection refused, cannot reach frontendConnectivitync -zvw10 <host> 7233connectivity.md#connection-refused
no such host, DNS resolution failsConnectivitydig +short <host> or nslookup <host>connectivity.md#dns
tls: handshake failure, server rejects handshakeCertificatesopenssl s_client -connect <host>:7233 -servername <host> </dev/nullcertificates.md#handshake-failure
x509: certificate has expired or not yet validCertificatesopenssl x509 -enddate -noout -in cert.pemcertificates.md#expired-or-not-yet-valid
x509: certificate signed by unknown authorityCertificatesopenssl verify -CAfile ca.pem client.pemcertificates.md#unknown-authority
tcld session / auth fails, Cloud role unclearAuthenticationtcld account getauthentication.md#cloud-role-and-permission-model
UNAUTHENTICATED, API key rejectedAuthenticationenv | grep -i TEMPORAL_API_KEY, then tcld apikey get --id <apikey_id>authentication.md#things-to-check-when-unauthenticated-is-returned-with-an-api-key
namespace not found / wrong namespace string with an API keyAuthenticationConfirm Regional Endpoint form <region>.<cloud_provider>.api.temporal.io:7233authentication.md#address-form-for-api-key-connections
RESOURCE_EXHAUSTED gRPC statusRate limitsIdentify which limit fired: throttle metrics on Cloud v1, the resource_exhausted_cause label on v0 / self-hostedrate-limits.md#identifying-which-limit-was-hit
Task queue shows no pollersWorker healthtemporal task-queue describe --task-queue <q>worker-health.md#what-no-pollers-looks-like
Workflow stuck on a pending activity / timer / child / signalWorkflow stucktemporal workflow describe --workflow-id <id>workflow-stuck.md#the-primary-inspection-command-temporal-workflow-describe
NondeterminismError, repeating WorkflowTaskFailedNon-determinismIdentify the last WorkflowTaskFailed cause in the Event Historynon-determinism.md#the-wft-failure-signature-of-non-determinism
Replay fails locally but prod workflow was runningNon-determinismFetch the history and run the SDK replayer in one testreplay.md#step-2--run-the-sdk-replayer-all-supported-sdks
HA failover did not route traffic to failover regionHA failovertcld namespace get --namespace <ns>.<acct> vs. DNS CNAMEha-failover.md#start-here-establish-ground-truth
Serverless Worker (AWS Lambda) stopped processing work after a Namespace failoverHA failoverConfirm a FailoverNamespace audit event, then compare the new active region against the Lambda ARN on the Worker Deployment Versionha-failover.md#symptom-serverless-workers-kept-running-in-the-old-region-after-failover
context deadline exceeded (unknown layer)Runtime errorsIdentify which operation and SDK emitted itruntime-errors.md#deadline-exceeded
Workflow is busy / ResourceExhausted on signal/update/query to one Workflow (BusyWorkflow)Runtime errorsRule out account-limit throttling, then split temporal_cloud_v1_resource_exhausted_error_count by operationruntime-errors.md#workflow-lock-contention-busyworkflow
PAYLOADS_TOO_LARGE, exceeds size limit, payload/gRPC blob size errorBlob size limitsCheck whether the issue is payload (2 MB) or gRPC message (4 MB)blob-size-limits.md
Workflow stuck in invisible retry loop (gRPC message too large)Blob size limitsCheck Worker logs for ResourceExhausted, reduce batch sizeblob-size-limits.md
High schedule-to-start latency, task slot depletion, slow Workflow TasksPerformance bottlenecksCheck temporal_workflow_task_schedule_to_start_latency P95performance-bottlenecks.md
High replay latency, cache evictions, deadlock detectedPerformance bottlenecksCheck workflow_task_replay_latency and sticky cache metricsperformance-bottlenecks.md
Schedule did not fire, missed catchup windowMissed Schedule ActionsAlert on temporal_cloud_v1_schedule_missed_catchup_window_countschedule-missed.md

If a symptom does not map to a row, start at diagnostic-ladder.md and work up from whichever layer was last known healthy.

The process

Operations path
Step 1: Identify intent and backend

Determine what the user wants to do and whether it targets:

  • Temporal Cloud → use tcld commands (requires tcld login)

  • Self-hosted cluster → use temporal operator commands

  • Data plane (either backend) → use temporal workflow, temporal batch, temporal schedule, etc.

If the backend is unambiguous from context — .tmprl.cloud address, tcld command, Cloud namespace format ns.account → Cloud; Kubernetes/Helm, docker-compose, self-hosted config files → self-hosted — proceed without asking. Otherwise, stop and ask: "Are you on Temporal Cloud or self-hosted?" before outputting any environment-specific commands. Do not default to either environment. Once known, save the answer to memory so you don't ask again in future conversations.

Step 2: Execute and interpret

Look up the intent in the Operations table above. Read the linked reference file for the exact commands, flags, and expected output. Run the command and interpret the result for the user.

Step 3: Verify

After a mutating operation (create, update, delete, rotate), confirm the new state:

  • Re-run the corresponding get or describe command

  • Confirm the output matches the user's intent

  • Report the result

Diagnosis path
Step 1: Identify the symptom

Ask the user for the exact, copy-pasted error text. Do not accept paraphrases — the exact string often encodes the layer (e.g., x509: prefix means TLS/cert layer, RESOURCE_EXHAUSTED: prefix means gRPC rate limit, NondeterminismError means workflow replay layer). Note that RESOURCE_EXHAUSTED alone does not tell you which condition fired — account-limit throttling and per-Workflow lock contention (Workflow is busy, i.e. BusyWorkflow) share the code. Split the resource-exhausted metric by its label (operation on the Cloud v1 family, resource_exhausted_cause on v0 and self-hosted) rather than parsing the free-text message.

Confirm three things before continuing:

  • What command was run, or what SDK call produced the error?

  • What environment produced it (local dev server, self-hosted cluster, Temporal Cloud)? If clear from context (addresses, commands, namespace format), don't ask — but if uncertain, ask now before proceeding with any diagnosis. Save the answer to memory for future conversations.

  • What changed recently (new deploy, new certs, new namespace, new region)?

Step 2: Gather context

The context the investigation needs depends on the category. At minimum:

  • For any Cloud auth / connectivity issue: auth method (API key vs mTLS), exact address, exact namespace, SDK + version. The endpoint family differs by auth method — see connectivity.md#endpoint-formats. For private connectivity (PrivateLink / PSC), TLS server name overrides also vary by auth method — see cloud-connectivity.md.

  • For a stuck workflow: namespace, workflow ID, run ID, and the output of temporal workflow describe --workflow-id <id> (pending-operation state lives here, not in the Event History alone). Event History via temporal workflow show is the companion view.

  • For a worker health issue: worker logs (registration errors, auth errors, panics), the output of temporal task-queue describe --task-queue <q>, and temporal worker describe --task-queue <q> for per-worker details.

  • For a non-determinism error: the worker log line containing the error, the workflow type name, and access to the history JSON for replay.

Step 3: Validate pasted SDK config (if any)

If the user has pasted SDK connection code — even just the address/namespace/auth fields — review it against sdk-snippet-review.md before descending the ladder. Wrong endpoint family, short namespace, or mismatched auth method will make every network-layer probe below look broken when nothing lower actually is.

Skip this step when the user has an established, previously-working config and the symptom is new — the snippet is not the culprit, the environment changed. Otherwise treat snippet validation as Layer 0.

Step 4: Descend the ladder

Use diagnostic-ladder.md to pick the right starting layer. As a rule of thumb:

  • Auth / connectivity / cert symptom → start at layer 1 (DNS) and walk up.

  • Worker / task-queue symptom → start at layer 6 (namespace + pollers).

  • Stuck workflow / determinism symptom → start at layer 7 (workflow code), but confirm layer 6 (worker is actually polling) first.

Each layer has a command that proves it healthy and a failure signature that tells you whether the problem lives at that layer or higher.

Step 5: Fix and verify

Prescribe the fix scoped to the root cause. Then verify by re-running the layer's healthy-check command and, if possible, the original user operation. Attach the confidence score to the diagnosis.

If the layer above the fix is still failing, return to step 4 and continue walking upward — the first broken layer is rarely the only one.

Prerequisites

  • Temporal CLI (temporal) — required for data-plane operations and self-hosted admin. Install: brew install temporal or see Temporal CLI docs.

  • tcld — required for Cloud operations. Install: brew install temporal-cloud-cli or see tcld docs. Authenticate with tcld login before use.

Reference files

Operations
  • cloud-namespace-admin.md — Cloud namespace lifecycle via tcld: create, get, list, delete, failover, add-region, retention, tags, codec-server, HA config, connectivity rules, search attributes, accepted-client-ca, certificate filters, export, and the tcld nexus endpoint allowed-namespace caller allowlist (1,000-caller Access Policy ceiling).

  • cloud-capacity.md — Capacity modes (On-Demand / Provisioned), APS/RPS/OPS definitions, TRUs, tcld namespace capacity update, default limits, throttling, APS management best practices.

  • cloud-iam.md — API key lifecycle (tcld apikey), users (tcld user), user groups (tcld user-group), service accounts, account operations (tcld account), roles, namespace permissions.

  • cloud-certs.md — mTLS cert management: generating certs with tcld generate-certificates, uploading CAs, certificate filters, cert rotation, switching mTLS ↔ API keys.

  • cloud-connectivity.md — Private connectivity (AWS PrivateLink / GCP PSC), connectivity rules: setup, rule parameters, tcld commands, attaching rules to namespaces.

  • cloud-migration.md — Migration paths: automated self-hosted→Cloud (S2S proxy, tcld migration commands, 5 phases), manual self-hosted→Cloud (client changes, workflow strategies), within-Cloud region-to-region (HA add-region/failover).

  • cloud-ops-api.md — Cloud Ops API: HTTP and gRPC endpoints (saas-api.tmprl.cloud), Go SDK, protobuf compilation, rate limits (160 RPS account, 40 user, 80 SA, 10 concurrent async), API version header, use cases.

  • cloud-billing.md — Cloud billing: Billing Center (invoices, credits, plans, cost by namespace), Usage Dashboards, Billing API: async CSV report generation, FOCUS-friendly format, 27-column report schema, date range constraints.

  • cloud-audit-logs.md — Cloud Audit Logs: supported control plane events (Account, API Keys, Connectivity Rules, Namespace, Export, Nexus, Service Accounts, User, User Groups), JSON format, API access (30-day retention), AWS Kinesis and GCP Pub/Sub sink configuration.

  • cloud-terraform.md — Terraform provider: setup (TEMPORAL_CLOUD_API_KEY), temporalcloud_namespace/temporalcloud_user/temporalcloud_service_account/temporalcloud_apikey/temporalcloud_nexus_endpoint CRUD, import, data sources (regions, namespaces), limitations (API keys not importable, cannot manage Account Owner).

  • cloud-notifications.md — Cloud notifications: certificate expiry (15/10/5 days), API key expiry (30/20/10 days), credit consumption/expiry alerts, plan changes, failover events, recipient roles, [email protected] sender.

  • cloud-saml-scim.md — SAML SSO (Entra ID, Okta): entity identifier (urn:auth0:prod-tmprl:ACCOUNT_ID-saml), callback URL (login.tmprl.cloud), IdP configuration steps, support ticket workflow. SCIM: supported vendors, prerequisites (SAML first), 10-minute sync window, group-to-role mapping.

  • self-hosted-admin.md — Self-hosted control plane via temporal operator: cluster health/describe, namespace CRUD, search-attribute create/list/remove, Nexus endpoint CRUD.

  • workflow-health.md — Data-plane health queries: temporal workflow list with List Filters, temporal workflow describe/show/count, temporal task-queue describe for poller status.

  • cli-conventions.md — Cross-command temporal CLI conventions: connection/identity (TEMPORAL_* env vars ↔ --address/--namespace/--api-key, --identity), output/formatting (--output, --time-format, payload shorthand), the --query ⇒ batch-job bridge (with temporal batch describe/list/terminate), and schedule time-spec forms. Ends with an operation→command index that routes each data-plane operation to its owner file. Delegates exhaustive flags to temporal <cmd> --help.

  • ops/recipes.md — End-to-end ops playbooks: set up new namespace, check APS, switch capacity mode, find hung workflows, rotate API key, audit access, rotate mTLS certs, check self-hosted health, view billing / generate billing report, configure audit log sink, provision resources with Terraform, set up SAML SSO.

Diagnosis
  • sdk-snippet-review.md — Layer-0 config check for pasted SDK connection snippets: endpoint form per auth method, namespace format, auth / TLS expectations, TEMPORAL_* env vars, common misconfigurations. Run before the diagnostic ladder.

  • diagnostic-ladder.md — the seven-layer bottom-up model, with one canonical command per layer and cross-links into the topical leaves.

  • connectivity.md — DNS, TCP, endpoint families (Namespace Endpoint for mTLS vs. Regional Endpoint for API keys), firewall/proxy shapes, PrivateLink/PSC, quick diagnostic scripts.

  • certificates.md — x509 and TLS alert strings, expiry / unknown-authority / hostname-mismatch / key-mismatch diagnosis, Cloud accepted-client-CA set via tcld namespace accepted-client-ca, Cloud mTLS certificate requirements, rotation and expiry notifications, openssl recipes.

  • authentication.md — UNAUTHENTICATED vs PERMISSION_DENIED, API-key lifecycle (tcld apikey commands, env var propagation, required Regional Endpoint form), mTLS after TLS (certificate filters, identity-to-role mapping), Cloud account-level roles and namespace-level permissions.

  • workflow-stuck.md — Workflow Execution Status values, temporal workflow describe as the primary inspection command, Event History via temporal workflow show, pending activities / child workflows / signals / Nexus operations / Workflow Tasks, WorkflowTaskFailed retry loops, recovery commands (signal, terminate, cancel, reset, pause/unpause).

  • non-determinism.md — determinism definition, WFT-failure signature, ND-inducing code patterns, per-SDK error shapes, identifying ND from Event History, local replay reproduction, remediation via Worker Versioning / patching / reset.

  • worker-health.md — no-pollers runbook via temporal task-queue describe, reachability and versioning, worker-level describe, schedule-to-start latency, worker task slots, sticky execution and sticky cache, worker heartbeating, Cloud namespace-level poller limits, worker log signatures.

  • rate-limits.md — what RESOURCE_EXHAUSTED means (and does not), Cloud APS / RPS / OPS under On-Demand and Provisioned capacity modes, self-hosted frontend.rps / frontend.namespaceRPS dynamic config, identifying which limit fired via the throttle metrics (Cloud v1) or the resource_exhausted_cause label (v0 / self-hosted), and separating account-limit throttling from single-resource exhaustion.

  • ha-failover.md — Cloud HA routing via the Namespace Endpoint CNAME, verifying the active region (control-plane tcld namespace get vs. DNS view), clients that did not follow the failover, PrivateLink after failover, failover-not-executing, handover-window errors, platform limits, RPO/RTO semantics, and Serverless Workers (AWS Lambda) not following a failover because compute-provider configuration is region-scoped.

  • runtime-errors.md — deadline-exceeded disambiguated by operation and by where the call was made, Workflow lock contention (BusyWorkflow) separated from account-limit throttling and confirmed via the operation breakdown, routing for no pollers / INVALID_ARGUMENT / unspecified UNAVAILABLE.

  • replay.md — fetching Event History with the SDK client (CLI export as fallback), running the SDK replayer in every supported SDK (Go, Python, TypeScript, Java, .NET, Ruby, PHP), TEMPORAL_DEBUG and the deadlock detector, interpreting divergent and successful replays, and the TypeScript-only VS Code extension.

  • blob-size-limits.md — Payload size limit (2 MB) and gRPC message size limit (4 MB): error messages, per-SDK behavior (Python 1.23.0+ vs. others), claim check pattern, External Storage (Pre-release), batch-size reduction.

  • performance-bottlenecks.md — Latency and throughput diagnosis via SDK metrics: schedule-to-start latency, workflow task execution latency, replay latency, activity execution latency, task slot depletion, network request metrics, sticky cache metrics.

  • schedule-missed.md — Missed Schedule Actions: alerting via temporal_cloud_v1_schedule_missed_catchup_window_count / schedule_missed_catchup_window, investigation via temporal schedule list + temporal schedule describe, DescribeSchedule fields (missedCatchupWindow, overlapSkipped, bufferDropped), default catchup window (one year), root causes, overlap policies (6 values), backfill remediation.

  • recipes.md — four end-to-end triage walkthroughs: stuck workflow at 3am, cert expired with workers offline, task-queue backlog mystery, non-determinism caught in prod.

Feedback

Feedback prompt

When you have first loaded this skill, output this message to the user:

"Thank you for trying out the public preview of the Temporal Ops skill! We would love to hear your feedback - positive or negative - over in the Community Slack, in the #topic-ai channel."

Do not output this message multiple times in the same conversation.

Reporting Issues in This Skill

If you (the AI) find this skill's explanations are unclear, misleading, or missing important information, 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-ops/issues/new. Do not file the issue autonomously.

Files

references/triage/non-determinism.md

Non-Determinism

This file scopes to one specific Workflow Task failure subtype: the one whose WorkflowTaskFailedCause is the Nondeterminism cause — i.e. the Workflow Task failed because replaying the recorded Event History against the currently-loaded Workflow code produced a Command that did not match the next Event.

Non-determinism is a subtype of the WFT-failure loop documented in workflow-stuck.md. For the taxonomy of other WFT-failure causes (Unhandled Command, Pending Activities Limit Exceeded, Bad Search Attributes, Workflow Worker Unhandled Failure), start there.

Out of scope here:

  • Workflow stuck in general, including other WFT-failure causes → workflow-stuck.md

  • Replaying an Event History locally under a debugger → replay.md

  • Worker not polling the Workflow Task Queue at all → worker-health.md

  • gRPC RESOURCE_EXHAUSTED on the client that tries to describe or reset → rate-limits.md

  • The bottom-up layer model for routing between files → diagnostic-ladder.md

Table of Contents

  • What determinism means in Temporal

  • How replay detection works

  • The WFT-failure signature of non-determinism

  • Why ND does not fail the Workflow

  • What the docs call out as ND-inducing patterns

  • Per-SDK error shape

  • Identifying ND from the Event History

  • Reproducing ND locally via replay

  • Remediation: Worker Versioning (preferred)

  • Remediation: per-SDK patching

  • Remediation: fix and redeploy, or reset past the divergence

  • Quick routing

What determinism means in Temporal

The Temporal Platform requires that Workflow code is deterministic. A Replay "recreates the exact state of a Workflow Execution" by running the Workflow code against the recorded Event History, and "Replay succeeds only if the Workflow Definition is compatible with the provided history from a deterministic point of view."

Per the encyclopedia's formal definition: "The use of certain Workflow APIs in the function is what generates Commands. Commands tell the Temporal Service which Events to create and add to the Workflow Execution's Event History. When the Workflow's code replays, the Commands that are emitted are compared with the existing Event History. If a corresponding Event already exists within the Event History that matches that command, then the Execution progresses."

"If a generated Command doesn't match what it needs to in the existing Event History, then the Workflow Execution returns a non-deterministic error."

Two reasons a Command may not match: (1) code changes to a Workflow Definition that is in use by a running Workflow Execution, or (2) intrinsic non-deterministic logic such as inline random branching.

How replay detection works

Replay is "the method by which a Workflow Execution resumes making progress. During a Replay the Commands that are generated are checked against an existing Event History." Replay happens whenever a Worker picks up a Workflow Task for a Workflow Execution whose in-memory state is not already present on that Worker — i.e. after a Worker restart, after a sticky-cache eviction, or when a different Worker picks up the next Workflow Task.

At Command-emission time, the Worker compares the Command it is about to emit against the next non-bookkeeping Event in the recorded history. A mismatch at any point (wrong Command type, wrong attributes like Activity name, or a Command appearing where none was recorded) surfaces as a non-determinism error inside the Worker. The Worker reports the Workflow Task as failed; the server appends a WorkflowTaskFailed Event with cause set to the Nondeterminism value of the WorkflowTaskFailedCause enum.

The WFT-failure signature of non-determinism

Each WorkflowTaskFailed Event corresponds to a value of the WorkflowTaskFailedCause enum, exposed in the Event's workflow_task_failed_event_attributes as cause. The Nondeterminism cause is documented in errors.mdx as:

The Workflow Task failed due to a nondeterminism error.

The event attributes schema — scheduled_event_id, started_event_id, failure, identity, base_run_id, new_run_id, fork_event_version, binary_checksum — is shared with every other WorkflowTaskFailed cause. The Nondeterminism-specific detail (which Command diverged from which Event) is carried inside the failure field, produced by the SDK.

Why ND does not fail the Workflow

Workflow Task failures do not fail the Workflow Execution. The server retries failed Workflow Tasks so that, once the Worker code is fixed and redeployed, the Workflow resumes. The observable effect of a Nondeterminism WFT failure is:

  • The Workflow Execution stays in the Running status.

  • pendingWorkflowTask.attempt (shown by temporal workflow describe) climbs.

  • The Event History accumulates repeating WorkflowTaskFailed Events with cause = Nondeterminism.

  • Worker logs repeat an SDK-specific non-determinism exception (see Per-SDK error shape).

This "the Workflow is durable, the bad deploy is not" behavior is intentional: fixing the Worker code and redeploying, or resetting the Workflow past the divergence point, resumes the execution without losing its progress to that point.

Contrast with ordinary Workflow failures. An unhandled exception inside an Activity produces ActivityTaskFailed and, depending on the Activity's Retry Policy, either retries or propagates to the Workflow. An unhandled exception inside the Workflow function itself produces the distinct Workflow Worker Unhandled Failure WFT cause, which is routed out of this file (worker logs for stack trace, fix and redeploy; see workflow-stuck.md). Neither of those is a Nondeterminism error — the classification lives in the cause field.

What the docs call out as ND-inducing patterns

Two categories, per the encyclopedia:

Code changes to a Workflow Definition in use

"The Workflow Definition can change in very limited ways once there is a Workflow Execution depending on it." The canonical example in the docs: a Workflow that was started under a definition of Timer then Activity cannot be migrated to a definition of Activity then Timer without versioning; on the next Workflow Task, "the first Command the Worker sees would be ScheduleActivityTask Command, which wouldn't match up to the expected TimerStarted Event. The Workflow Execution would fail and return a nondeterminism error."

Minor changes that do not cause ND on replay of histories that already contain the corresponding Events:

  • Changing the duration of a Timer, with language-specific exceptions: "In Java, Python, and Go, changing a Timer's duration from or to 0 is a non-deterministic behavior. In .NET, changing a Timer's duration from or to -1 (which means 'infinite') is a non-deterministic behavior."

  • Changing arguments to Activity Options, Child Workflow Options, or the Signal External Workflow Execution call.

  • Adding a Signal Handler for a Signal Type that has not been sent to this Workflow Execution.

Intrinsic non-determinism in the Workflow function

"Intrinsic non-determinism is when a Workflow Function Execution might emit a different sequence of Commands on re-execution, regardless of whether all the input parameters are the same." "A Workflow Definition can not have inline logic that branches (emits a different Command sequence) based off a local time setting or a random number."

Each SDK exposes replay-safe alternatives. The per-SDK constraint lists documented in develop/<sdk>/workflows/basics.mdx:

  • Go. "Iterate over maps using range, because with range the order of the map's iteration is randomized." Use sorted keys, a Side Effect, or an Activity instead. Direct calls to external APIs, file I/O, or other services must be wrapped in Activities. Replay-safe substitutes include workflow.Now() (for time.Now()), workflow.Sleep(), workflow.GetLogger(), workflow.Go() (for the go statement), workflow.Channel (for native chan), workflow.Selector (for select), and workflow.Context. Go's map iteration order is not specified by the language spec.

  • Python. "Workflow code must be deterministic because the Temporal Server may replay your Workflow to reconstruct its state. This means: no threading, no randomness, no external calls to processes, no network I/O, no global state mutation, no system date or time." Replay-safe substitutes: workflow.random() (for random.random()), workflow.uuid4() (for uuid.uuid4()), workflow.now() (for datetime.now() / time.time()), workflow.logger (for print() / logging).

  • TypeScript, Java, .NET, Ruby, PHP. Per-SDK constraint lists and replay-safe APIs are linked from the encyclopedia.

Also documented: changes to "Patched or GetVersion calls for Versioning (although they may be added or removed according to the patching rules)" are among the minor allowed changes. In other words, patching and removing patches have documented rules; ad-hoc additions of non-patched activity calls in the middle of a Workflow do not.

What the docs do not prescribe here: a ranked list of "most common causes." Treat the two categories above as exhaustive framings. Any diagnosis that does not map to one of them should be escalated to a VERIFY.

Per-SDK error shape

The error class or message that the Worker emits when replay diverges differs per SDK. Only the names actually verified in the docs are listed:

  • TypeScript. "When an Event History is replayed and non-determinism is detected (that is, the Workflow code is incompatible with the History), DeterminismViolationError is thrown. If replay fails for any other reason, ReplayError is thrown."

  • Go. The Go SDK emits a non-determinism error through the Workflow Task failure path; the docs for versioning describe the condition as "cause the Workflow to fail with a nondeterminism error" without pinning a public class name.

  • Java. The Java versioning doc describes the condition as "This would cause the Workflow to fail with a nondeterminism error" without pinning a public exception class.

  • Python. The Python versioning doc describes the condition as "cause a nondeterminism error" without pinning a public exception class.

  • .NET, Ruby, PHP. Per-SDK class names are not documented in the files consulted.

Server-side, regardless of SDK, the cause surfaces as the Nondeterminism WorkflowTaskFailedCause in the Event History. Rely on that server-side cause for cross-SDK classification; rely on the per-SDK error class only when debugging from worker logs.

Identifying ND from the Event History

The server-side signal that unambiguously marks ND is a WorkflowTaskFailed Event whose attributes carry the Nondeterminism cause. Extract via:

temporal workflow show \
    --workflow-id YourWorkflowId \
    --output json > history.json

Then filter the JSON for WorkflowTaskFailed events and inspect each one's workflowTaskFailedEventAttributes.cause and workflowTaskFailedEventAttributes.failure fields. The exact JSON field names come from the server proto; the encyclopedia identifies them as workflow_task_failed_event_attributes.

temporal workflow describe also surfaces a pendingWorkflowTask with a rising attempt when WFT failures are looping. A rising attempt count plus WorkflowTaskFailed Events with Nondeterminism cause is the confirming two-signal match.

If describe shows WFT failures but the history JSON does not report Nondeterminism as the cause, the WFT is failing for a different reason — return to workflow-stuck.md §Pending Workflow Task and WorkflowTaskFailed loops and match the actual cause.

Reproducing ND locally via replay

Running the recorded history through a local replayer against a Worker source tree is the canonical reproducer. "Replay recreates the exact state of a Workflow Execution. You can replay a Workflow from the beginning of its Event History. Replay succeeds only if the Workflow Definition is compatible with the provided history from a deterministic point of view."

Per-SDK replay APIs (names transcribed from the testing-suite pages; cross-check against the SDK version in use):

  • Go. worker.NewWorkflowReplayer() + replayer.ReplayWorkflowHistory(logger, history).

  • Python. Replayer(workflows=[YourWorkflow]) + replayer.replay_workflow(WorkflowHistory.from_json(history_json_str)), or replayer.replay_workflows(histories) for bulk.

  • TypeScript. Worker.runReplayHistory(options, history) for single histories, Worker.runReplayHistories(...) for bulk.

  • Java. WorkflowReplayer.replayWorkflowExecution(file, MyWorkflow.class) for single histories, WorkflowReplayer.replayWorkflowExecutions(...) for bulk.

For an interactive reproducer with breakpoints over the same replayer APIs, see replay.md.

Fetch the history with the SDK client in the same test that replays it — per-SDK calls in replay.md → Step 1. Where a file is needed instead:

temporal workflow show \
    --workflow-id YourWorkflowId \
    --run-id YourRunId \
    --output json > history.json

If local replay succeeds against the checked-out source but production Workers keep failing, the deployed Worker code is different from the local checkout. Find the deployed build and either roll it back or patch it forward.

If local replay fails with the same error, iterate on the Workflow source until replay succeeds.

Remediation: Worker Versioning (preferred)

"For most teams, Worker Versioning should be the default recommendation for deploying Workflow code changes in production. If you can run versioned worker deployments, prefer Worker Versioning over patching."

The mechanism: "Worker Versioning introduces Workflow Pinning. For pinned Workflow Types, each execution runs entirely on the Worker Deployment Version where it started. You need not worry about making breaking code changes to running, pinned Workflows." "Pinned Workflows don't need to be patched, as they run on the same worker and build until they complete."

Minimum versions for Worker Versioning (Public Preview as of the docs snapshot): Go v1.35.0, Python v1.11, Java v1.29, TypeScript v1.12, .NET v1.7.0, Ruby v0.5.0; Temporal CLI v1.4.1, Server v1.29.1, UI v2.38.0.

Adoption in the middle of triaging an ND incident is not a remediation — it is a prevention for future changes. For Workflows that are already in the ND loop and were started before Worker Versioning was enabled for their Workflow Type, use patching or reset.

Remediation: per-SDK patching

Patching lets Workflow code branch on whether it is replaying history recorded before or after a specific change. Patching APIs per SDK (each citation links to the canonical docs page; only names present in those pages are listed):

  • Go. workflow.GetVersion(ctx, "change-id", workflow.DefaultVersion, 1). "When workflow.GetVersion() is run for the new Workflow Execution, it records a marker in the Event History so that all future calls to GetVersion for this change Id ... will always return the given version number." A Workflow that has already passed this GetVersion() call before it was introduced returns DefaultVersion.

  • TypeScript. patched('change-id') to branch, deprecatePatch('change-id') to mark a patch as deprecated. "Using patched inserts a marker into the Workflow History. During Replay, if a Worker encounters a history with that marker, it will fail the Workflow task when the Workflow code doesn't produce the same patch marker."

  • Java. Workflow.getVersion("change-id", Workflow.DEFAULT_VERSION, 1). "Each call to Workflow.getVersion automatically upserts the TemporalChangeVersion Search Attribute with a keyword list of "<Change ID>-<Version>" entries."

  • Python. workflow.patched('change-id') to branch, workflow.deprecate_patch('change-id') to mark deprecated.

  • .NET. Workflow.Patched("change-id"), Workflow.DeprecatePatch("change-id").

The three-phase lifecycle, per each SDK's versioning page:

  1. Patch in new code using the branching API alongside the old code.

  2. Once no open Workflow Executions are on the old code, switch to the deprecate-patch API.

  3. Once all patched Workflows have left retention, remove the patch call entirely.

The patching behavior at replay time is documented in detail in docs/encyclopedia/workflow/patching.mdx. Key consequence: if the execution hits a patched() call during replay but the marker is after the current execution point in history, "it will throw a non-deterministic exception because the replay and original event histories don't match." Put the newest code at the top of patch blocks: "when patching in new code, always put the newest code at the top of an if-patched-block."

Remediation: fix and redeploy, or reset past the divergence

Because ND WFT failures retry server-side, there are two live-incident paths:

Fix the Worker code and redeploy

If the divergence was introduced by a recent deploy and the broken Worker can be replaced with one whose code matches the recorded history (either by reverting the change or by adding a patch branch), redeploy and let the server-retried Workflow Tasks succeed on the new Workers. The Workflow resumes from the point where it started failing; history up to that point is intact.

Reset the Workflow past the divergence

If the divergence is already embedded in the history (e.g. new workflows have executed the broken path and recorded it) and a simple redeploy cannot recover them, temporal workflow reset rewinds a Workflow to an earlier Event ID, terminates the current run, and starts a new run with history copied up to the reset point.

temporal workflow reset \
    --workflow-id YourWorkflowId \
    --event-id YourLastGoodEventId

Valid reset points per the Event encyclopedia are "WorkflowTaskStarted, WorkflowTaskCompleted, WorkflowTaskTimedOut, and WorkflowTaskFailed." Choose a WorkflowTaskCompleted immediately before the first failing WFT as the reset target: replay from that point against the fixed code avoids the divergence.

For batch resets, the --type values permitted are FirstWorkflowTask, LastWorkflowTask, and BuildId. The single-run reset example in the docs uses LastContinuedAsNew.

temporal workflow reset \
    --workflow-id YourWorkflowId \
    --type LastContinuedAsNew

--reapply-type controls which Events are reapplied after the reset point; accepted values are Signal, None.

Confirm with the business owner before resetting: the Events after the reset point are re-executed, which means any activity that was run post-reset-point will be re-scheduled on replay — fine for idempotent operations, hazardous for irreversible external side effects.

Quick routing

EvidenceGo to
WorkflowTaskFailed Events with cause = Nondeterminism in the Event HistoryThis file
pendingWorkflowTask.attempt climbing, but WorkflowTaskFailed Events show a different causeworkflow-stuck.md §Pending Workflow Task and WorkflowTaskFailed loops
Worker logs show a class like DeterminismViolationError (TypeScript) or a nondeterminism error (other SDKs)Per-SDK error shape, Reproducing ND locally
Need to reproduce the ND locally with breakpointsreplay.md
Need a CI regression test around a fixed ND bugReproducing ND locally via replay — use the SDK's bulk replayer
Planning a future-proof deploy strategyRemediation: Worker Versioning (preferred)
Live incident, pre-existing Workflows are stuck, deploy-the-fix is not enoughRemediation: fix and redeploy, or reset past the divergence
The describe call itself fails (cannot reach/auth/authorize)connectivity.md, certificates.md, authentication.md, rate-limits.md

For the whole-stack picture and where this file sits in the diagnostic order, see diagnostic-ladder.md.

Requirements and compatibility

Written for: OpenAI Codex, Claude Code

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