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Home Assistant Anomaly-Detection Suite

A modular, micro-service-oriented toolkit that ingests raw Home Assistant telemetry, detects anomalies, reasons about context with LLMs, and proposes (or executes) remediations.

Component Directory Map

# Folder Purpose Status
01 components/01_ingestion Integrate Home Assistant event stream, MQTT topics, and REST pulls into a unified message bus. ✅ Done
02 components/02_time_series_store Optimised time-series DB & feature store. ✅ Done
03 components/03_feature_engineering Data cleansing, resampling, and feature derivation. Planned
04 components/04_anomaly_detection Rule-based & ML models run hourly to flag anomalies. Planned
05 components/05_media_analysis Optional worker for image/video understanding. Planned
06 components/06_llm_reasoner LLM-powered explanation & action planning. Planned
07 components/07_notification_ux Push notifications and feedback capture. Planned
08 components/08_automation_executor Executes approved actions via Home Assistant services. Planned
09 components/09_model_training Offline model training & evaluation pipelines. Planned
10 components/10_observability Metrics, logs, and health dashboards. Planned
11 components/11_devops CI/CD pipelines and deployment manifests. Planned
12 components/12_docs_config Centralised docs, schemas, and config templates. Planned

📚 High-level architecture lives in architecture/project_overview.md.

🔖 Current sprint focus lives in .llm_context/global_status.yml.

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