Iot Anomalies

Part of the AlterLab Academic Skills suite. This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

Verify witness chain integrity and detect provenance gaps

Register a Cognitum Seed device by endpoint and establish agent bridge

Create and manage Cognitum Seed device fleets with firmware policies

Design and operate a data integrity monitoring programme based on ALCOA+ principles. Covers detective controls, audit trail review schedules, anomaly detection patterns (off-hours activity, sequential modifications, bulk changes), metrics dashboards, investigation triggers, and escalation matrix definition. Use when establishing a data integrity monitoring programme for GxP systems, preparing for inspections where data integrity is a focus area, after a data integrity incident requiring enhanced monitoring, or when implementing MHRA, WHO, or PIC/S guidance.

Invoke when the user needs to set up analytics, define telemetry events, establish KPIs, build dashboards, configure A/B testing, or implement data-driven design capabilities. Triggers on: "analytics", "telemetry", "KPIs", "metrics", "player data", "retention", "DAU", "dashboard", "A/B testing", "funnel analysis". Do NOT invoke for balance tuning (use game-balance-check) or economy design (use game-economy-designer). Part of the AlterLab GameForge collection.