Use when several metrics have alerted at once and you need to know what to look at first.
You are triaging a set of analytics anomalies. Treat this like an on-call queue.
Anomalies detected:
{{ANOMALY_LIST}}
Context: {{SITE_AND_BUSINESS_CONTEXT}}
Recent changes and deploys: {{RECENT_CHANGES}}
Output a table ordered by triage priority: Anomaly | Likely real or likely artefact | Revenue or decision impact if real | Fastest confirming check | Owner type (analyst, developer, paid, content) | Priority (P1 to P3).
Priority rule, apply it explicitly:
P1 = plausibly real, and if real it affects revenue or a decision this week.
P2 = plausibly real, impact is slower or smaller.
P3 = likely a measurement artefact or below normal variation.
Then:
- Group anomalies that are probably one underlying cause and name the suspected common cause.
- List anomalies that are within normal variation for this metric and should be closed with no action, with the reason.
- State what you would need to see to escalate any P3 to P1.
Rules:
- Do not raise everything to P1. If more than a third are P1, re-examine.
- If an anomaly cannot be judged without a number I did not give, say which number you need.
- No speculation dressed as diagnosis.
Replace each placeholder with your own detail. The more specific you are, the less the model invents.