Train and evaluate models that estimate human poses from WiFi signals. Compare results against a model that always predicts the average pose, check for data leakage, and report measured improvements with clear evaluation conditions.
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Train/evaluate WiFi pose models honestly — camera-supervised (MediaPipe + CSI) and camera-free (WiFlow), always checked against the mean-pose baseline before any PCK is quoted.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Build a CSI→pose model without overstating it. The project has a retracted 92.9%/100% history — the discipline below exists so it never recurs.
A pose model that always predicts the dataset's mean pose already scores ~50% PCK. Quote PCK only as a delta over that baseline, on a held-out split with no subject or temporal leakage. Example honest result (ADR-181):
Held-out PCK@20 59.5% vs a 50% mean-pose baseline = +9.4 pp real signal — MEASURED.
npx @ruvnet/ruview train-plan --mode pose-smoke # command, cwd, outputs; runs nothing
npx @ruvnet/ruview train --mode pose-smoke --confirm # SYNTHETIC pipeline smoke (libtorch 2.11 for tch 0.24)
npx @ruvnet/ruview train --mode pose --data-dir <in-repo MM-Fi dir> --confirm
npx @ruvnet/ruview train-gate --file eval-report.json # mean-pose baseline + leakage gate
The gate returns the only acceptable claim sentence. Quote nothing it fails.
(model − baseline) in pp, with the split definition (chronological /
blocked-gap / grouped-bucket; no leakage).ruview_claim_check the writeup — it flags any untagged or 100%/perfect claim.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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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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