Build a room-specific sensing model by recording an empty room and occupants performing activities, then training small specialized models. The workflow continues into live monitoring of presence, posture, and breathing in the calibrated room.
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Run the ADR-151 per-room calibration pipeline — baseline → enroll → extract → train → a bank of small specialists (presence/posture/breathing/heartbeat/restlessness/anomaly).
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Turn a provisioned node + sensing-server into a working room model. Pure-Rust,
edge-deployable (ADR-151). Use the ruview_calibrate tool (installed
wifi-densepose binary, else cargo run -p wifi-densepose-cli).
ruview_calibrate {step: "baseline"}ruview_calibrate {step: "enroll"}ruview_calibrate {step: "train-room"}ruview_calibrate {step: "room-watch"} (or the room-watch skill)The specialists are calibrated to this room; cross-room transfer is a separate
problem (LoRA recalibration, ADR-079 P9). Report which room a number came from, and
tag presence/vitals accuracy MEASURED only with a held-out check — run
ruview_claim_check on the writeup.
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Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.
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Create AgentDB learning plugins that use recorded experience to improve agent decisions. Choose a learning method, store states, actions, and rewards, train models, and evaluate suggested actions through CLI and API workflows.
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Build AI agents that retain conversation history, user preferences, and useful interaction patterns with AgentDB. The skill covers persistent storage, meaning memories survive between sessions, plus retrieval, context management, and ReasoningBank integration.
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Tune AgentDB memory use and search speed through quantization, which stores vectors more compactly, search indexes, caching, and batch operations. Includes configuration examples and benchmarks to help balance accuracy, speed, and storage as datasets grow.
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Build document search with AgentDB that finds related content by meaning. Store documents as numerical representations, combine similarity search with metadata filters, and retrieve relevant passages for systems that generate answers using source material.
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Automate browser tasks with agent-browser, using snapshots that label page elements for the agent. Handle login flows, fill and submit forms, extract page information, and capture screenshots through a repeatable navigation and interaction workflow.
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