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Alert Rules Specification
Define monitoring thresholds, trigger conditions, and escalation rules to catch data anomalies without false positives.
Updated June 2026
The prompt
Metric: {{metric_name}}. Baseline: {{baseline}}. SLA: {{sla_target}}. False positive tolerance: {{fp_tolerance}}%. Current false positive rate: {{current_fp_rate}}%. Design alert rules: 1. Rule 1 (Static): {{metric_name}} drops below {{static_threshold}} for {{static_window}} 2. Rule 2 (Dynamic): {{metric_name}} deviates > {{std_threshold}} std devs for {{dynamic_window}} 3. Rule 3 (Rate of change): {{metric_name}} changes >{{pct_change}}% hour-over-hour 4. Rule 4 (Interaction): when {{metric_name}} AND {{related_metric}} both trigger 5. False positive suppression: skip alerts during {{exception_windows}} 6. Escalation: Slack if {{low_severity}}, PagerDuty if {{high_severity}}, daily digest if {{medium_severity}}
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Variables
Metric to monitorCurrent baseline valueSLA targetAcceptable false positive %Current false positive rate %Absolute threshold valueDuration (e.g., 10 minutes)Std dev multiplier (e.g., 2.5)Window for dynamic check% change thresholdCorrelated metric for compound ruleTimes to suppress alerts (maintenance, deploy, etc.)Low severity conditionHigh severity conditionMedium severity conditionDetails
Author
AI Khazna
License
—
Security
—
Type
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