Operate a Guidewire Cloud API integration in production — define SLIs/SLOs for token availability, bind success rate, FNOL p99 latency; route alerts so the on-call gets paged for real outages and never for transient noise; triage 401 spikes, 409 storms, 429 saturation, scope drift, and Gosu OOM cascades from signal to recovery in 15 minutes or less. Use when designing a dashboard for a new integration, writing the on-call runbook, or running a post-incident review. Trigger with "guidewire observability", "guidewire slo", "guidewire on-call", "guidewire 401 spike", "guidewire 409 storm", "guidewire incident".
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Operate a Guidewire Cloud API integration in production — define SLIs/SLOs for token availability, bind success rate, FNOL p99 latency; route alerts so the on-call gets paged for real outages and never for transient noise; triage 401 spikes, 409 storms, 429 saturation, scope drift, and Gosu OOM cascades from signal to recovery in 15 minutes or less. Use when designing a dashboard for a new integration, writing the on-call runbook, or running a post-incident review. Trigger with "guidewire observability", "guidewire slo", "guidewire on-call", "guidewire 401 spike", "guidewire 409 storm", "guidewire incident".
Run a production Guidewire Cloud API integration with the dashboards, alerts, and runbooks an on-call engineer can act on at 3am. This skill consolidates the operational layer: what to measure, what to alert on, how to triage the top five incident classes, and how to close the loop with a post-incident review that prevents recurrence rather than performing root-cause theater.
Five operational failures this skill prevents:
Vanity dashboards — graphs of total request count, no per-endpoint p99, no SLI burn rate; on-call sees green during a real outage because the right thing was never measured.
Alert fatigue — every transient 5xx pages someone; in three weeks the team mutes the channel; a real incident two weeks later goes unnoticed for an hour.
No triage tree — on-call wakes up to "401 spike", does not know whether to rotate a secret, restart the integration, or call GCC; loses 20 minutes Googling.
Skipped post-incident review — the same root cause produces three incidents in a quarter because no one wrote down the action items from the first one.
Common-errors table living in nine Slack threads — operators cannot find the recovery for an error they have seen before, ask the same question in #ops, the answer takes 45 minutes.
Prerequisites
A working integration emitting structured logs and metrics to a backend (Datadog, Grafana+Prometheus, New Relic, Splunk, or equivalent)
Access to a paging system (PagerDuty, Opsgenie, VictorOps) with on-call rotations defined
The integration_audit table from guidewire-security-and-rbac populated — incident triage depends on knowing what the integration tried to do
correlation_id propagated end-to-end through every Cloud API call
Instructions
Build the operational layer in this order. Each step targets one of the five operational failures listed in Overview.
1. Define SLIs that measure user-visible behavior
Track what users care about, not what is easy to measure. For a Guidewire integration, the SLIs that matter:
SLI
Measurement
Target
Token-endpoint availability
success rate of /oauth/token over a rolling 5min window
≥99.9%
Cloud API write success
rate on POST/PATCH calls, excluding 4xx caller errors
2xx
≥99.5%
Bind success rate
bound / quoted ratio over rolling 1h, excluding referrals
≥98%
FNOL intake p99 latency
end-to-end time from inbound event to claim.created log
≤2s
Quote-to-bind median latency
median time from quote-call to bind-success
≤30s
5xx from upstream Cloud API counts against availability; 4xx from caller bugs (validation failures, scope mismatches) does not — those are caller errors, not integration outages. Distinguishing the two is the single biggest cause of either alert fatigue or missed incidents, depending on which way the bias goes.
2. Burn-rate-based alerting, not threshold alerting
Alerting on "error rate > 1%" pages the on-call every time a transient 502 happens. Alert on SLO burn rate instead — the rate at which the error budget is being consumed.
Fast burn: 2% error budget consumed in 1 hour → page immediately
Slow burn: 5% error budget consumed in 6 hours → page during business hours
Trickle: 10% error budget consumed in 3 days → ticket, not page
A 5-minute outage that consumes 1% of the monthly budget should not page; a 20-minute outage that consumes 4% should. Burn-rate alerts encode this naturally.
3. Top-five triage trees
Each tree is the ~5-step decision sequence on-call follows from signal to recovery. Memorize the entry signal; the body is in the runbook.
T1: 401 spike on Cloud API calls
401s > 1% for 5min
├─ Check token age in cache: is the integration refreshing? → if no, restart token-cache process
├─ Decode a recent token, verify exp > now + 60s → if no, clock skew or aggressive proxy caching
├─ Check GCC: is the Service Application enabled? → if no, talk to tenant admin
└─ Check secret-rotation history: was a secret rotated in the last 24h? → if yes, run dual-secret swap or restart
T2: 409 storm on PATCH calls
409s > 5% for 5min
├─ Are concurrent writers expected? → if yes, scale checksum round-trip retry budget
├─ Is one resource-id producing all 409s? → if yes, it's a hot key; coordinate writes via queue
└─ Is the client retrying the bare PATCH instead of the GET-PATCH cycle? → fix the retry layer
T3: 429 saturation on Cloud API or Hub
429s > 1% for 10min
├─ Is the Hub /oauth/token endpoint 429ing? → token cache missing single-flight gate; deploy fix immediately
├─ Is the data-plane API 429ing? → check tenant quota in GCC, request increase if legitimate growth
└─ Is one customer driving the saturation? → tenant-side rate limit on the integration's intake
T4: Scope drift detected
Scope-drift alert from auth refresh
├─ What scope is missing? → check GCC > Identity & Access > Applications > [app] > Permissions
├─ Was a permission removed by a tenant admin? → coordinate; restore or accept loss of capability
└─ Was a scope renamed in a tenant config push? → update GW_SCOPES env, redeploy
T5: GUnit / runServer OOM (dev or staging)
OOMKilled or heap dump generated
├─ Is sample data set abnormally large? → reset dev DB, reload fixtures
├─ Is a recent change loading too many entities (no pagination, no filter)? → revert; add limit
└─ Is the JVM under-provisioned? → bump -Xmx in gradle.properties; restart
4. Recovery playbooks (top 5 incident classes)
Each playbook is one page in the on-call runbook. Concrete commands, not prose.
## Playbook: 401 spike — secret rotation suspected1. Rotate in GCC > Identity & Access > Applications > [app] > Generate Secret
2. sops secrets.prod.<tenant>.sops.yaml # set new GW_CLIENT_SECRET (and SECONDARY = old)
3. git commit -m "rotate(secrets): incident-driven rotation $(date -Iseconds)"
4. Trigger deploy of token service
5. Confirm token claims show iat > rotation timestamp: kubectl exec ... -- /token-debug
6. After 24h zero failures from primary: remove SECONDARY
7. Open audit row with reason="incident-401-spike"
Each playbook ends with an audit-row insert so the next post-incident review has the full timeline.
5. Post-incident review template (PIR)
# PIR: <date> — <one-linesummary>## Timeline- HH:MM — first signal (alert link, dashboard screenshot)
- HH:MM — on-call paged
- HH:MM — root cause identified
- HH:MM — mitigation applied
- HH:MM — confirmation of recovery
## SLO impact- Error budget consumed: X%
- Customer-impacting requests: N
- Compliance impact: <yes / no — ifyes, NAICnotificationwindow>## Root cause<theactualrootcause, notasymptom; use5-whysbutdon'tperform; oneparagraphmax>## What worked<whattheteamdidright; preservethese>## What didn't<whatslowedtheresponse; thisdrivesactionitems>## Action items (each with owner + target date)- [ ] OWNER, BY: <date> — <concretechange>- [ ] OWNER, BY: <date> — <concretechange>## Recurrence prevention<theonechangethatmakesthisexactincidentimpossiblenexttime, not "bemorecareful">
The discipline is the action items having owners and dates. PIRs without those are theater.
Output
A production-grade observability layer ships with all of the following:
A dashboard showing the five SLIs with their targets and current burn rate; one panel per SLI, each linked to the underlying logs/metrics query.
Burn-rate-based alerts wired to the paging system; fast-burn pages immediately, slow-burn opens a ticket, trickle goes to a weekly review.
Five triage trees memorized by every on-call rotation member; the entry signal is the alert title, the body is the runbook.
Five recovery playbooks one-page each, kept in the integration repo (so they version with the code), linked from each alert.
A PIR template applied to every incident with SLO impact > 0; action items tracked in the work-management system with owners and dates.
Examples
Example 1 — Datadog burn-rate monitor (SLO target 99.5%)
-- "What was the integration trying to do during the 401 spike?"SELECT correlation_id, actor, api_method, api_path, status_code, reason, atFROM integration_audit
WHEREatBETWEEN'2026-04-15T03:00:00Z'AND'2026-04-15T03:30:00Z'AND status_code =401ORDERBYat;
Example 3 — Runbook link in alert
# Alert configtitle:"GW Cloud API 401 spike"runbook:https://github.com/acme/guidewire-integration/blob/main/runbooks/401-spike.mdpriority:P1escalation:"platform-oncall"
The on-call clicks the runbook link from the page; the runbook is in the repo so it versions with the code; updates land in PRs reviewed like any other change.
Error Handling
Symptom
First check
Likely cause
401 spike on Cloud API
token cache age
reactive refresh, clock skew, secret rotation in flight
409 storm on PATCH
concurrent writers + retry pattern
client retrying bare PATCH instead of GET-then-PATCH
429 on /oauth/token
single-flight gate active?
thundering herd refresh from a stampede on token expiry
429 on data-plane API
per-tenant quota
another integration sharing the tenant quota; growth past quota
403 Forbidden despite valid token
scope-drift gate
tenant admin removed a permission
PKIX path building failed
cert chain in JVM trust store
private-CA cert renewal not propagated
Gosu OOM in production
recent deploy + heap dump
unbounded entity load (missing filter or pagination)
FNOL p99 latency spike
upstream lookup latency
external policy-resolution service degraded
Bind success rate drops below SLO
UW-issue volume + product mix
new product or new broker producing high-referral volume; not an outage
Audit table empty for an outage window
audit insert path failing
check the audit-write retry budget; never let audit failures block the request
For deeper coverage (RED method vs USE method tradeoffs, multi-tenant dashboard partitioning, synthetic FNOL probes, chaos-engineering tests for token-cache resilience), see implementation guide and API reference.
See Also
guidewire-install-auth — produces the auth-side signals this skill alerts on (token cache, scope drift)
guidewire-sdk-patterns — the structured GwError and 409 retry semantics this skill's triage trees assume
guidewire-security-and-rbac — the integration_audit table this skill queries during incidents
guidewire-ci-cd-pipeline — the deployment surface incident response interacts with (rollback, canary)
