RuView is a WiFi sensing platform that uses disturbances in radio signals, captured by low-cost ESP32 sensors, to detect presence, movement, breathing, and heart rate without cameras or wearables. It is intended for spatial monitoring and smart-home integrations. The catalogue add-ons support workflows for operating and integrating RuView.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ruvnet/RuView --skill train-posegit clone --depth 1 https://github.com/ruvnet/RuViewWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ruvnet/ruview/train-pose)<a href="https://agentmods.dev/skills/ruvnet/ruview/train-pose"><img src="https://agentmods.dev/badge/skills/ruvnet/ruview/train-pose/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ruvnet/ruview/train-pose"><img src="https://agentmods.dev/badge/skills/ruvnet/ruview/train-pose.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.00546 |
| Opus 5.5 | $0.00018 | $0.00218 |
| Sonnet 5.5 | $0.00009 | $0.00109 |
| Haiku 4.5 | $0.00004 | $0.00055 |
Grade A, and why
train-pose scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 18d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
train-pose
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.
The non-negotiable: mean-pose baseline first
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.
Paths
- camera-supervised (ADR-079) — MediaPipe Pose labels the camera frame; paired CSI trains the net. Train/infer in one camera frame so the skeleton aligns.
- camera-free (WiFlow, ADR-152) — no camera at inference; geometry-conditioned.
- in-browser (ADR-181) — WebGPU/WASM trainer; the active backend is shown as a badge (honest about what's executing).
Run it through the harness (ADR-371)
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.
Before you publish a number
- Run the mean-pose baseline on the same split.
- Report
(model − baseline)in pp, with the split definition (chronological / blocked-gap / grouped-bucket; no leakage). ruview_claim_checkthe writeup — it flags any untagged or 100%/perfect claim.- If it's a benchmark vs SOTA, tag MEASURED-EQUIVALENT only with the reproducer.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 18d ago Changed · +11 lines 8cb132e46839
- 1mo ago First seen · 34 lines · 44 tokens per session scan A 92aebd442347
train-pose is a skill published in the GitHub repository ruvnet/RuView (98,920 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 546 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
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langsmith-observability
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reproduce
Reproduces a paper's result, a claim, an artifact or a previous run with the target and success criterion frozen first, the canonical code path run before any substitute, exact inputs, environment, seeds and commands captured, and a verdict from a fixed set, reproduced, reproduced within tolerance, partially…