Temporal Ops
OpenAIOfficialCreated by TemporalDistributed by OpenAI
- Skill
- Tool integration
- Cloud and DevOps
- MIT
- No issues found
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
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/openai/temporal-ops/downloadUnzip 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:
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.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.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.
Blast radius as a number, not a description. For any
--queryform, runtemporal workflow count --query '<query>'with the byte-identical query first and carry the result into the proposal. A filter with no narrowing predicate beyondExecutionStatus="Running"matches every open Execution in the Namespace.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.Ask explicitly, then run it so it completes. Put the command, the target, and — for any
--queryform — the count from step 1 to the user as a direct question, and wait for an answer. Once they approve, run it with--yeson the--querybatch forms; that flag is what lets an approved batch finish, since the interactive prompt needs a terminal and without one the command reportsuser denied confirmationand does nothing.--yesbelongs in a command the user approved, never in a retry of one that failed its prompt. Do not substitute a loop over single-targetworkflow 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.Verify, and know the abort path. Re-run the corresponding
get,describe, orcount. A batch job drains asynchronously:temporal batch describe --job-id <id>shows how far it has gotten andtemporal 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:
Bottom-up diagnosis. Verify the lower layer before blaming the upper one. The layers, from bottom to top:
DNS / network path
TCP / port reachability
TLS handshake
Authentication (API key or mTLS client cert)
gRPC health and Temporal frontend reachability
Temporal namespace, task queues, workers
Workflow code (determinism, signals, timers, child workflows)
The full ladder lives in diagnostic-ladder.md.
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.
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.
Name ambiguity explicitly. Errors like
context deadline exceededare not self-describing, and a single code such asRESOURCE_EXHAUSTEDcan 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.
| Intent | Category | Reference |
|---|---|---|
| Create, get, list, delete a Cloud namespace | Cloud namespace admin | cloud-namespace-admin.md |
| Add/remove region, failover, HA config | Cloud namespace admin | cloud-namespace-admin.md |
| Set retention, tags, codec-server, connectivity rules | Cloud namespace admin | cloud-namespace-admin.md |
| Add or rename Cloud search attributes | Cloud namespace admin | cloud-namespace-admin.md |
| Check current APS / capacity mode | Cloud capacity | cloud-capacity.md |
| Switch On-Demand ↔ Provisioned, set TRUs | Cloud capacity | cloud-capacity.md |
| Understand APS / RPS / OPS limits and throttling | Cloud capacity | cloud-capacity.md |
| Create, disable, enable, delete an API key | Cloud IAM | cloud-iam.md |
| Invite, list, remove users; set roles/permissions | Cloud IAM | cloud-iam.md |
| Manage user groups and service accounts | Cloud IAM | cloud-iam.md |
| Generate mTLS certs, upload CA, set cert filters | Cloud certs | cloud-certs.md |
| Rotate mTLS certificates | Cloud certs | cloud-certs.md |
| Set up Workflow History Export (S3 / GCS) | Cloud namespace admin | cloud-namespace-admin.md |
| Set up PrivateLink / PSC, manage connectivity rules | Cloud connectivity | cloud-connectivity.md |
| Self-hosted cluster health, describe, namespace CRUD | Self-hosted admin | self-hosted-admin.md |
| Self-hosted search attributes, Nexus endpoints | Self-hosted admin | self-hosted-admin.md |
| Check or manage a Cloud Nexus Endpoint's caller-Namespace allowlist; the 1,000-caller default | Cloud namespace admin | cloud-namespace-admin.md#tcld-nexus-endpoint-allowed-namespace |
| Find stuck/hung/unhealthy workflows via list queries | Workflow health | workflow-health.md |
| Task queue poller status, workflow counts | Workflow health | workflow-health.md |
| Cancel, terminate, or reset workflows | Workflow recovery | workflow-stuck.md#recovery-commands |
Bulk / batch operations on workflows (--query) | CLI conventions | cli-conventions.md |
| Schedule CRUD, time-spec, and operations | CLI conventions | cli-conventions.md |
| Complete or fail an activity externally | CLI conventions | cli-conventions.md |
| Cloud Ops API access, rate limits, Go SDK | Cloud Ops API | cloud-ops-api.md |
| View billing, generate billing report, cost attribution | Cloud billing | cloud-billing.md |
| Audit Logs: view, query via API, configure sink (AWS/GCP) | Cloud audit logs | cloud-audit-logs.md |
| Terraform provider: Namespace/User/SA/API Key/Nexus CRUD | Cloud Terraform | cloud-terraform.md |
| Expiry alerts (cert, API key, credit), status page | Cloud notifications | cloud-notifications.md |
| SAML SSO, SCIM provisioning, IdP integration | Cloud SAML/SCIM | cloud-saml-scim.md |
| Migrate self-hosted to Cloud (automated or manual), migrate between Cloud regions | Cloud migration | cloud-migration.md |
| End-to-end ops playbook (setup, rotation, audit, billing, Terraform) | Ops recipes | ops/recipes.md |
Diagnosis
Find the row that matches the user's symptom. Start the investigation at the first check, then read the linked reference.
| Symptom | Category | First check | Reference |
|---|---|---|---|
connection refused, cannot reach frontend | Connectivity | nc -zvw10 <host> 7233 | connectivity.md#connection-refused |
no such host, DNS resolution fails | Connectivity | dig +short <host> or nslookup <host> | connectivity.md#dns |
tls: handshake failure, server rejects handshake | Certificates | openssl s_client -connect <host>:7233 -servername <host> </dev/null | certificates.md#handshake-failure |
x509: certificate has expired or not yet valid | Certificates | openssl x509 -enddate -noout -in cert.pem | certificates.md#expired-or-not-yet-valid |
x509: certificate signed by unknown authority | Certificates | openssl verify -CAfile ca.pem client.pem | certificates.md#unknown-authority |
tcld session / auth fails, Cloud role unclear | Authentication | tcld account get | authentication.md#cloud-role-and-permission-model |
UNAUTHENTICATED, API key rejected | Authentication | env | 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 key | Authentication | Confirm Regional Endpoint form <region>.<cloud_provider>.api.temporal.io:7233 | authentication.md#address-form-for-api-key-connections |
RESOURCE_EXHAUSTED gRPC status | Rate limits | Identify which limit fired: throttle metrics on Cloud v1, the resource_exhausted_cause label on v0 / self-hosted | rate-limits.md#identifying-which-limit-was-hit |
| Task queue shows no pollers | Worker health | temporal task-queue describe --task-queue <q> | worker-health.md#what-no-pollers-looks-like |
| Workflow stuck on a pending activity / timer / child / signal | Workflow stuck | temporal workflow describe --workflow-id <id> | workflow-stuck.md#the-primary-inspection-command-temporal-workflow-describe |
NondeterminismError, repeating WorkflowTaskFailed | Non-determinism | Identify the last WorkflowTaskFailed cause in the Event History | non-determinism.md#the-wft-failure-signature-of-non-determinism |
| Replay fails locally but prod workflow was running | Non-determinism | Fetch the history and run the SDK replayer in one test | replay.md#step-2--run-the-sdk-replayer-all-supported-sdks |
| HA failover did not route traffic to failover region | HA failover | tcld namespace get --namespace <ns>.<acct> vs. DNS CNAME | ha-failover.md#start-here-establish-ground-truth |
| Serverless Worker (AWS Lambda) stopped processing work after a Namespace failover | HA failover | Confirm a FailoverNamespace audit event, then compare the new active region against the Lambda ARN on the Worker Deployment Version | ha-failover.md#symptom-serverless-workers-kept-running-in-the-old-region-after-failover |
context deadline exceeded (unknown layer) | Runtime errors | Identify which operation and SDK emitted it | runtime-errors.md#deadline-exceeded |
Workflow is busy / ResourceExhausted on signal/update/query to one Workflow (BusyWorkflow) | Runtime errors | Rule out account-limit throttling, then split temporal_cloud_v1_resource_exhausted_error_count by operation | runtime-errors.md#workflow-lock-contention-busyworkflow |
PAYLOADS_TOO_LARGE, exceeds size limit, payload/gRPC blob size error | Blob size limits | Check 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 limits | Check Worker logs for ResourceExhausted, reduce batch size | blob-size-limits.md |
| High schedule-to-start latency, task slot depletion, slow Workflow Tasks | Performance bottlenecks | Check temporal_workflow_task_schedule_to_start_latency P95 | performance-bottlenecks.md |
| High replay latency, cache evictions, deadlock detected | Performance bottlenecks | Check workflow_task_replay_latency and sticky cache metrics | performance-bottlenecks.md |
| Schedule did not fire, missed catchup window | Missed Schedule Actions | Alert on temporal_cloud_v1_schedule_missed_catchup_window_count | schedule-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
tcldcommands (requirestcld login)Self-hosted cluster → use
temporal operatorcommandsData 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
getordescribecommandConfirm 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 viatemporal workflow showis 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>, andtemporal 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 temporalor see Temporal CLI docs.tcld — required for Cloud operations. Install:
brew install temporal-cloud-clior see tcld docs. Authenticate withtcld loginbefore 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 thetcld nexus endpoint allowed-namespacecaller 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 migrationcommands, 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_endpointCRUD, 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 listwith List Filters,temporal workflow describe/show/count,temporal task-queue describefor poller status.cli-conventions.md — Cross-command
temporalCLI conventions: connection/identity (TEMPORAL_*env vars ↔--address/--namespace/--api-key,--identity), output/formatting (--output,--time-format, payload shorthand), the--query⇒ batch-job bridge (withtemporal 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 totemporal <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 —
UNAUTHENTICATEDvsPERMISSION_DENIED, API-key lifecycle (tcld apikeycommands, 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 describeas the primary inspection command, Event History viatemporal 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_EXHAUSTEDmeans (and does not), Cloud APS / RPS / OPS under On-Demand and Provisioned capacity modes, self-hostedfrontend.rps/frontend.namespaceRPSdynamic config, identifying which limit fired via the throttle metrics (Cloud v1) or theresource_exhausted_causelabel (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 getvs. 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
operationbreakdown, routing forno pollers/INVALID_ARGUMENT/ unspecifiedUNAVAILABLE.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_DEBUGand 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 viatemporal 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
- ATTRIBUTION.mdAdded by AutoGineer888 B
- LICENSELicence file added by AutoGineer1.1 KiB
- SKILL.md33.7 KiB
- agents/openai.yaml177 B
- references/ops/cli-conventions.md10.0 KiB
- references/ops/cloud-audit-logs.md7.7 KiB
- references/ops/cloud-billing.md9.7 KiB
- references/ops/cloud-capacity.md10.1 KiB
- references/ops/cloud-certs.md11.3 KiB
- references/ops/cloud-connectivity.md9.0 KiB
- references/ops/cloud-iam.md15.4 KiB
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- references/ops/recipes.md19.1 KiB
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- references/triage/authentication.md19.3 KiB
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- references/triage/certificates.md32.9 KiB
- references/triage/connectivity.md14.0 KiB
- references/triage/diagnostic-ladder.md18.5 KiB
- references/triage/ha-failover.md17.8 KiB
- references/triage/non-determinism.md20.7 KiB
- references/triage/performance-bottlenecks.md8.9 KiB
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- references/triage/replay.md13.5 KiB
- references/triage/runtime-errors.md16.6 KiB
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references/triage/replay.md
Replay a Workflow Execution locally
This file is about the tooling that reproduces a recorded Workflow Execution in a debugger. For what replay divergence means as a concept — why the Worker Task fails, how the server classifies the cause, and how to remediate — see non-determinism.md.
A 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." Running a recorded history through a local replayer against your Worker source tree replays the Workflow Execution "to replicate errors" — how you reproduce a non-determinism error under a debugger and, via the bulk replayer, pin a CI regression test.
The SDK replayer is the general-purpose tool — documented for every supported SDK, it runs headless, attaches to any debugger, and drops into CI as a regression guard. The VS Code extension is a TypeScript-only convenience wrapper around the same replayer.
Out of scope:
What non-determinism means, how to identify it in an Event History, and remediation options → non-determinism.md
Worker not polling the Task Queue at all — nothing to replay, fix the Worker first → worker-health.md
Can't reach the server to fetch the history → connectivity.md, authentication.md
The bottom-up layer model for routing between files → diagnostic-ladder.md
Table of Contents
Prerequisites
Step 1 — Get the Event History
Step 2 — Run the SDK replayer (all supported SDKs)
TEMPORAL_DEBUG: suppress the deadlock detector while stepping
Interpreting a replay that diverges
Interpreting a replay that succeeds
The VS Code extension (TypeScript only, interactive)
Prerequisites
The Workflow source tree at the commit that was deployed when the recorded Workflow Execution ran. Replaying current
mainagainst an older recording can produce divergence for a different reason than the bug you're triaging.The SDK installed and importable in that workspace (the replayer is part of the SDK, not a standalone binary).
A client connection from that workspace to the Namespace holding the run, with read access to its history (see authentication.md) — or, failing that, a history file exported by someone who has it.
Step 1 — Get the Event History
The replayer takes an Event History object, and every SDK client can fetch one. Fetch it in the same test that replays it.
| SDK | Fetch from the server |
|---|---|
| Go | c.GetWorkflowHistory(ctx, id, runID, false, enums.HISTORY_EVENT_FILTER_TYPE_ALL_EVENT) returns an iterator; accumulate iter.Next() into a history.History |
| Python | client.list_workflows(<query>).map_histories() |
| TypeScript | client.workflow.getHandle(<workflow-id>).fetchHistory(); client.workflow.list({ query }).intoHistories() for many |
| Java | service.blockingStub().getWorkflowExecutionHistory(<request>), wrapped as new WorkflowExecutionHistory(response.getHistory(), <workflow-id>) |
| .NET | client.ListWorkflowHistoriesAsync(<query>) |
| Ruby | client.list_workflows(<query>) |
| PHP | $replayer->replayFromServer(workflowType: ..., execution: ...) fetches and replays in one call |
The list-based fetches require Advanced Visibility on the server.
Export a JSON file instead when the workspace has no client access to the Namespace, the history is being committed as a CI fixture, or the run has aged out of retention:
temporal workflow show \
--workflow-id YourWorkflowId \
--run-id YourRunId \
--output json > history.json--output accepts text, json, jsonl, none (default text); replay requires json. --run-id is not required.
If the run id is unknown, find it via temporal workflow describe --workflow-id YourWorkflowId (see workflow-stuck.md) or the Web UI.
For Cloud or any non-default target, add the usual connection flags (--address, --namespace, and either --api-key or the mTLS --tls-* flags). See authentication.md.
Step 2 — Run the SDK replayer (all supported SDKs)
Each SDK's testing-suite page documents a replayer. Names and signatures below are transcribed from those pages; they are what the docs state, not what the runtime type system exports at any given version. The history argument is whatever step 1 produced — a fetched object or a parsed file.
All of these run headless and the failure surfaces as a thrown error, which is enough to identify the divergence. A debugger is only needed to step through Workflow code; recommend that to the user rather than treating it as the default path.
Go
replayer := worker.NewWorkflowReplayer()
replayer.RegisterWorkflow(YourWorkflow)
err := replayer.ReplayWorkflowHistory(nil, hist) Use worker.WorkflowReplayer to "replay an existing Workflow Execution from its Event History to replicate errors." "If a noticeably different code path was followed or some code caused a deadlock, it will be returned in the error code."
To step through the Workflow function, attach Delve or an IDE debugger to the test process that calls ReplayWorkflowHistory, with TEMPORAL_DEBUG=true (see below).
Python
replayer = Replayer(workflows=[YourWorkflow])
await replayer.replay_workflows(histories) replay_workflows takes the iterator from step 1; if any replay fails, it raises. Set fail_fast to false to replay every history before reporting. From a file, the single-history form is replayer.replay_workflow(WorkflowHistory.from_json(history_json_str)). Histories fetched from the server or exported can be protobuf-encoded (bytes) while the Replayer works with decoded histories (like a dict); a dict-vs-bytes TypeError during replay means the history needs decoding first.
TypeScript
const history = JSON.parse(await fs.promises.readFile('./history.json', 'utf8'));
await Worker.runReplayHistory(
{ workflowsPath: require.resolve('./your/workflows') },
history,
); For bulk replay, use Worker.runReplayHistories.
Two error classes are documented: "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."
Java
File file = new File("history.json");
WorkflowReplayer.replayWorkflowExecution(file, MyWorkflow.class); Use WorkflowReplayer from the temporal-testing package. For bulk replay, WorkflowReplayer.replayWorkflowExecutions. "In both examples, if Event History is non-deterministic, an error is thrown. You can choose to wait until all histories have been replayed with replayWorkflowExecutions by setting the failFast argument to false."
.NET
var replayer = new WorkflowReplayer(
new WorkflowReplayerOptions().AddWorkflow<MyWorkflow>());
await replayer.ReplayWorkflowAsync(
WorkflowHistory.FromJson("my-workflow-id", historyJson)); For bulk replay, iterate replayer.ReplayWorkflowsAsync(...) and check each result.ReplayFailure.
Ruby
replayer = Temporalio::Worker::WorkflowReplayer.new(workflows: [MyWorkflow])
replayer.replay_workflow(history) For bulk replay, pass client.list_workflows(...) to replayer.replay_workflows(...); set raise_on_replay_failure: true, or inspect each result.replay_failure.
PHP
The replayer is \Temporal\Testing\Replay\WorkflowReplayer. Replay from a running server with replayFromServer(...), from an exported JSON file with replayFromJSON(...), or from an in-memory history with replayHistory($history). A non-deterministic replay throws \Temporal\Testing\Replay\Exception\ReplayerException.
TEMPORAL_DEBUG: suppress the deadlock detector while stepping
Two SDKs explicitly document a debug-mode env var. Without it, pausing on a breakpoint for more than a second can cause the Worker's deadlock detector to fail the Workflow Task during your debugging session:
Go. "The Temporal Go SDK includes deadlock detection which fails a Workflow Task in case the code blocks over a second without relinquishing execution control. Because of this you can often encounter a
PanicError: Potential deadlock detectedwhile stepping through Workflow Definitions during debugging. To alleviate this issue, you can set theTEMPORAL_DEBUGenvironment variable totruebefore debugging your Workflow Definition."Java. "The Temporal Java SDK includes deadlock detection which fails a Workflow Task in case the code blocks over a second without relinquishing execution control. Because of this you can often encounter the
PotentialDeadlockExceptionException while stepping through Workflow code during debugging. To alleviate this issue, you can set theTEMPORAL_DEBUGenvironment variable to true before debugging your Workflow code."
Both pages add the same warning: "Make sure to set TEMPORAL_DEBUG to true only during debugging."
Python and TypeScript debugging pages do not document a TEMPORAL_DEBUG env var in the sources consulted.
Interpreting a replay that diverges
The documented behavior when replay detects non-determinism:
TypeScript: throws
DeterminismViolationError; any other replay failure throwsReplayError.Go: the replayer returns an error from
ReplayWorkflowHistory; the docs describe the condition as "cause the Workflow to fail with a nondeterminism error" without pinning a public type name.Java:
WorkflowReplayer.replayWorkflowExecutionthrows; the versioning doc describes the condition as "This would cause the Workflow to fail with a nondeterminism error."Python:
Replayer.replay_workflowraises; if any replay fails, the code raises an exception.
The error is raised where the SDK detected the mismatch — typically inside the Worker machinery, not on the Workflow line that emitted the bad Command. A debugger halts at the same place. To locate the offending Workflow line, compare:
The last Command the code was about to emit (the frame just below the SDK entry in the stack), and
The next non-bookkeeping Event in the recorded history (see non-determinism.md §Identifying ND from the Event History).
The divergence is the mismatch between those two. The encyclopedia frames this as: "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."
For remediation paths (Worker Versioning, per-SDK patching, reset past the divergence), return to non-determinism.md §Remediation.
Interpreting a replay that succeeds
If replay succeeds locally against the checked-out source, but production Workers keep failing with the same Workflow Execution, the deployed Worker code almost certainly differs from the local checkout. Find the deployed build (via Worker Versioning metadata if used, or your deploy system) and reproduce from that commit. See non-determinism.md §Reproducing ND locally via replay.
A local-replay success on the current source with production still failing is also the classic case Worker Versioning is meant to prevent. See non-determinism.md §Remediation: Worker Versioning (preferred).
The VS Code extension (TypeScript only, interactive)
Temporal publishes a VS Code extension that wraps the TypeScript SDK's replayer in the VS Code debugger UI — one-click "open history → set breakpoints → step through." For Workflows authored in any other SDK, skip it and use the SDK replayer above under your IDE's native debugger; the observability is the same (a stack trace at the point the SDK detected divergence) and the replayer APIs are first-party and doc-backed.
Claims in this section come from the extension's marketplace listing; the Temporal docs themselves do not document the extension.
Install: search the VS Code Marketplace for "Temporal" and install the extension published by Temporal Technologies Inc. (
temporal-technologies.temporalio). As of the listing consulted, it debugs TypeScript workflows only.Configure: the replayer is driven by a TypeScript entrypoint that calls
startDebugReplayerwith aworkflowsPath; the extension reads the path from thetemporal.replayerEntrypointsetting (defaultsrc/debug-replayer.ts).Run: Temporal: Open Panel from the Command Palette → enter a Workflow ID (fetched from the configured server, default
localhost:7233) or pick a history JSON file → Start → set breakpoints in Workflow source or on history events, then step through. For Cloud, supply the Cloud gRPC endpoint plus the credentials in authentication.md and certificates.md.
For anything the marketplace page does not spell out (a supported-SDK matrix beyond TypeScript, the full command list, launch.json recipes, OS requirements, mTLS-vs-API-key support), consult the extension's README for the version you installed — the citations here are a point-in-time snapshot.
Requirements and compatibility
Written for: OpenAI Codex, Claude Code
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: Claude Code frontmatter, Needs an API key
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Attribution and licence
- Created by
- Temporal
- Publisher
- OpenAI (Official)
- Distributed by
- OpenAI
- Source
- openai/plugins · plugins/temporal/skills/temporal-ops · 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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