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.
git 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/commands/ruvnet/ruview/ruview-train)<a href="https://agentmods.dev/commands/ruvnet/ruview/ruview-train"><img src="https://agentmods.dev/badge/commands/ruvnet/ruview/ruview-train/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/commands/ruvnet/ruview/ruview-train"><img src="https://agentmods.dev/badge/commands/ruvnet/ruview/ruview-train.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00045 | $0.00528 |
| Opus 5.5 | $0.00018 | $0.00211 |
| Sonnet 5.5 | $0.00009 | $0.00106 |
| Haiku 4.5 | $0.00005 | $0.00053 |
Grade A, and why
ruview-train 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 1mo 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
/ruview-train
Train, fine-tune, evaluate, or publish a RuView model.
- Invoke the
ruview-model-trainingskill. - Pick the track from
$ARGUMENTS; if empty, ask which:- camera-free (Track A) —
cargo run -p wifi-densepose-sensing-server -- --pretrain --dataset data/csi/ --pretrain-epochs 50then-- --train --dataset data/mmfi/ --epochs 100 --save-rvf model.rvf. ~84 s on M4 Pro, modest accuracy. - camera-supervised (Track B, ADR-079) —
python scripts/collect-ground-truth.py,python scripts/collect-training-data.py,node scripts/align-ground-truth.js, then train ondata/paired/, eval withnode scripts/eval-wiflow.js. ~19 min, 92.9% PCK@20. Needsdata/pose_landmarker_lite.task. - embeddings (Track C, AETHER ADR-024) —
wifi-densepose-train+wifi-densepose-ruvector;-- --model model.rvf --embed,-- --build-index env. - domain-gen (Track D, MERIDIAN ADR-027) / snn (Track E) —
node scripts/snn-csi-processor.js --port 5006; cognitum-seed-pretraining tutorial. - gpu —
gcloud config set project cognitum-20260110;bash scripts/gcloud-train.sh --gpu l4 --hours 2(or--gpu a100 --sweep,--dry-runto smoke-test). VM auto-deletes unless--keep-vm.
- camera-free (Track A) —
- After training:
cd v2 && cargo test --workspace --no-default-features,python archive/v1/data/proof/verify.py. To publish:python scripts/publish-huggingface.py. - Hand off to
/ruview-verifyfor the witness bundle.
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.
- 1mo ago First seen · 19 lines · 45 tokens per session scan A c8022ff4d393
ruview-train is a command published in the GitHub repository ruvnet/RuView (100,355 stars, last pushed today), licensed MIT. It adds 45 tokens to every session and 528 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 commands, from other repositories
ml-run-review
Review a machine learning training run or experiment configuration for soundness, reproducibility, and evaluation rigor.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.