mirror of https://github.com/ruvnet/RuView synced 2026-06-09 10:13:17 +00:00

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ruv 8ff7c2c35a feat(plugins): RuView Claude Code + Codex marketplace plugin

Add `plugins/ruview` — an end-to-end toolkit for working with RuView
(WiFi-DensePose) from Claude Code, mirrored as Codex prompts.

Marketplace: `plugins/.claude-plugin/marketplace.json` (one plugin, `ruview`).

Skills (9): ruview-quickstart, ruview-hardware-setup, ruview-configure,
ruview-applications, ruview-model-training, ruview-advanced-sensing,
ruview-cli-api, ruview-mmwave, ruview-verify — shell-first (cargo / python /
idf.py / docker / node), no claude-flow MCP dependency.

Commands (7): /ruview-start, /ruview-flash, /ruview-provision, /ruview-app,
/ruview-train, /ruview-advanced, /ruview-verify.

Agents (3): ruview-onboarding-guide, ruview-config-engineer,
ruview-training-engineer.

Codex mirror: codex/AGENTS.md + codex/README.md + codex/prompts/*.md (full
command parity, enforced by scripts/smoke.sh).

Docs: docs/adrs/0001-ruview-plugin-contract.md (Proposed). Verification:
scripts/smoke.sh (13 structural checks). Provisioning docs reflect the full
`provision.py` flag set (TDM mesh, edge tiers, vitals, hop channels, Cognitum
Seed, swarm intervals) and the issue #391 NVS-namespace-replace gotcha.

Verified: `claude plugin validate` (plugin + marketplace), loads via
`claude --plugin-dir`, smoke 13/13, and confirmed against an attached ESP32-S3
on COM8 running the RuView CSI firmware (live adaptive_ctrl + csi_collector
serial output).

Co-Authored-By: claude-flow <ruv@ruv.net>

2026-05-11 17:39:16 -04:00

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/ruview-train — train a RuView model

Train / evaluate / publish a RuView model. Track: $ARGUMENTS (one of camera-free, camera-supervised, embeddings, domain-gen, snn, gpu; if empty, ask).

camera-free (WiFlow pose, no labels): cd v2 && cargo run -p wifi-densepose-sensing-server -- --pretrain --dataset data/csi/ --pretrain-epochs 50, then -- --train --dataset data/mmfi/ --epochs 100 --save-rvf model.rvf. ~84 s on M4 Pro, modest accuracy. Bench node scripts/benchmark-wiflow.js, eval node scripts/eval-wiflow.js.
camera-supervised (ADR-079, 92.9% PCK@20, ~19 min): python scripts/collect-ground-truth.py (MediaPipe landmarks; needs data/pose_landmarker_lite.task), python scripts/collect-training-data.py (CSI capture), node scripts/align-ground-truth.js (timestamp align), then cd v2 && cargo run -p wifi-densepose-sensing-server -- --train --dataset data/paired/ --epochs <N> --save-rvf model.rvf, eval node scripts/eval-wiflow.js (reports PCK@20).
embeddings (AETHER ADR-024 / spectrogram ADR-076): wifi-densepose-train + wifi-densepose-ruvector; -- --model model.rvf --embed, -- --model model.rvf --build-index env. 171K emb/s on M4 Pro.
domain-gen (MERIDIAN ADR-027): domain-generalization options in the training pipeline + ruview_metrics.
snn (local env adaptation, <30 s): node scripts/snn-csi-processor.js --port 5006; docs/tutorials/cognitum-seed-pretraining.md; ADR-084/085 (RaBitQ), ADR-086 (novelty gate).
gpu: gcloud auth login && gcloud config set project cognitum-20260110, then bash scripts/gcloud-train.sh --dry-run (smoke), bash scripts/gcloud-train.sh --gpu l4 --hours 2 (proto, ~~$0.80/hr), bash scripts/gcloud-train.sh --gpu a100 --config scripts/training-config-sweep.json (~~$3.60/hr), bash scripts/gcloud-train.sh --sweep (full sweep). VM auto-deletes unless --keep-vm. Local Mac: bash scripts/mac-mini-train.sh. Bench: python scripts/benchmark-model.py.

Data: data/recordings/ raw CSI · data/csi/ pretrain · data/mmfi/ MM-Fi · data/paired/ camera↔CSI · data/ground-truth/ MediaPipe · models/ artifacts. Record more: python scripts/record-csi-udp.py.

After training: cd v2 && cargo test --workspace --no-default-features, cd .. && python archive/v1/data/proof/verify.py (VERDICT: PASS). Publish: python scripts/publish-huggingface.py (or .sh; docs/huggingface/). Then run /ruview-verify.

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/ruview-train — train a RuView model

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