Design Decisions

Anomsmith makes strong choices.

Anomsmith separates scoring from detection. Scores measure deviation. Detection applies rules. This separation prevents silent threshold assumptions.

Anomsmith treats thresholds as first class objects. Quantiles, fixed values, and adaptive rules all share the same interface.

Anomsmith avoids alert semantics. It reports evidence. Alerting belongs to downstream systems.

Anomsmith does not optimize for one anomaly definition. Point anomalies, level shifts, and gradual drift coexist. Each detector states its assumptions through tags.

Anomsmith does not embed visualization. Plotsmith handles that role. This keeps Anomsmith dependency light and focused.

These decisions reduce convenience in the short term. They increase correctness and reuse over time.