Define Guardian visual language

.codex/prompts/ruview-advanced.md

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+# /ruview-advanced — advanced RuView capabilities
+
+Drive RuView's research-grade / multi-node features. Topic: `$ARGUMENTS` (one of `multistatic`, `cross-viewpoint`, `tomography`, `field-model`, `intention`, `adversarial`, `security`; if empty, ask).
+
+- **multistatic** (ADR-029) — treat every WiFi link in range (incl. neighbours' APs) as a bistatic radar pair, then fuse. `v2/crates/wifi-densepose-signal/src/ruvsense/multistatic.rs` (attention-weighted fusion, geometric diversity), `phase_align.rs` (iterative LO phase-offset, circular mean), `multiband.rs`, `coherence.rs` / `coherence_gate.rs` (Z-score scoring; Accept / PredictOnly / Reject / Recalibrate).
+- **cross-viewpoint** (ADR-016 viewpoint module) — combine 2+ nodes geometrically. `v2/crates/wifi-densepose-ruvector/src/viewpoint/`: `attention.rs` (CrossViewpointAttention, GeometricBias, softmax with `G_bias`), `geometry.rs` (GeometricDiversityIndex, Cramér–Rao bounds, Fisher Information), `coherence.rs` (phase-phasor coherence, hysteresis gate), `fusion.rs` (MultistaticArray aggregate root). Explore geometry first: `node scripts/mesh-graph-transformer.js`, `node scripts/deep-scan.js`.
+- **tomography** — `ruvsense/tomography.rs` reconstructs a voxel occupancy grid via an ISTA L1 solver (sparse — most voxels empty); pair with cross-viewpoint geometry for through-wall volumetric imaging. RuVector solver crates back the 114→56 subcarrier sparse interpolation.
+- **field-model** (ADR-030) — `ruvsense/field_model.rs` builds an SVD eigenstructure of the room, persists it (RVF, ideally on a Cognitum Seed); new frames are projected against it and the residual is the perturbation. Survives restarts; answers "what's different from the empty-room baseline?"
+- **intention** — `ruvsense/intention.rs`, pre-movement lead signals 200–500 ms ahead.
+- **adversarial** — `ruvsense/adversarial.rs`, rejects physically impossible signals + cross-checks multi-link consistency.
+- **security** (ADR-032, multistatic mesh hardening) — using neighbour APs and pooling links across a mesh expands the attack surface. Mitigations: `adversarial.rs` + `coherence_gate.rs` quarantine (Reject / Recalibrate) + Ed25519 witness chain (ADR-028). Run a security review (`docs/security-audit-wasm-edge-vendor.md`); see `/ruview-verify`.
+
+Also relevant: ADR-031 (sensing-first RF mode), ADR-081 (adaptive CSI mesh firmware kernel), ADR-083 (per-cluster π compute hop), ADR-095/096 (on-ESP32 temporal modeling, sparse GQA).
+
+Validate: `cd v2 && cargo test -p wifi-densepose-signal --no-default-features && cargo test -p wifi-densepose-ruvector --no-default-features`, then `cd .. && python archive/v1/data/proof/verify.py`.

.codex/prompts/ruview-app.md

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+# /ruview-app — run a RuView sensing application
+
+Run a RuView application. Which one: `$ARGUMENTS` (one of `presence`, `vitals`, `pose`, `sleep`, `environment`, `mat`, `pointcloud`, or a novel-RF app name; if empty, show the catalogue and ask).
+
+- **presence / vitals / pose / environment** → `cd v2 && cargo run -p wifi-densepose-sensing-server` against a live ESP32 sink, or the Docker demo (`docker run -p 3000:3000 ruvnet/wifi-densepose:latest`) for simulated CSI. For environment also `-- --model model.rvf --build-index env`. Vitals: breathing 6–30 BPM (bandpass 0.1–0.5 Hz), heart rate 40–120 BPM (bandpass 0.8–2.0 Hz), `wifi-densepose-vitals` crate (ADR-021). Pose: 17 COCO keypoints via WiFlow (ADR-059 live pipeline) — train for accuracy (`/ruview-train`).
+- **sleep** → `examples/sleep/` + `node scripts/apnea-detector.js` (sleep-stage classification, apnea screening).
+- **mat** (Mass Casualty Assessment — disaster survivor detection) → `wifi-densepose-mat` crate, `docs/wifi-mat-user-guide.md`.
+- **pointcloud** → `python scripts/mmwave_fusion_bridge.py` (camera depth via MiDaS + WiFi CSI + mmWave radar → unified spatial model, ~22 ms, 19K+ pts/frame; ADR-094).
+- **novel RF** → `scripts/passive-radar.js`, `material-classifier.js`, `device-fingerprint.js`, `mincut-person-counter.js`, `gait-analyzer.js` (ADR-077/078).
+
+No hardware? Fall back to the Docker demo or `python examples/ruview_live.py`. Visualisers: `node scripts/csi-spectrogram.js`, `node scripts/csi-graph-visualizer.js`.
+
+Help me pick: through-wall → presence/activity (≤5 m depth); stationary subject → vitals/sleep; need skeletons → pose (train it); search & rescue → MAT; best spatial accuracy → 2+ ESP32 nodes + cross-viewpoint fusion (`v2/crates/wifi-densepose-ruvector/src/viewpoint/`), optionally + Cognitum Seed. Examples: `examples/{environment,medical,sleep,stress,happiness-vector}/`.

.codex/prompts/ruview-flash.md

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+# /ruview-flash — build + flash ESP32 firmware
+
+Build and flash RuView ESP32 firmware. Variant + port: `$ARGUMENTS` (default `8mb`, port `COM8`).
+
+1. **Variant.** `8mb` → ensure it builds from `firmware/esp32-csi-node/sdkconfig.defaults.template` (no mock — real WiFi CSI). `4mb` → `cp firmware/esp32-csi-node/sdkconfig.defaults.4mb firmware/esp32-csi-node/sdkconfig.defaults` first (display disabled, dual OTA via `partitions_4mb.csv`). `heltec` → `sdkconfig.defaults.heltec_n16r2`.
+2. **Build (Windows).** ESP-IDF v5.4 does NOT work under Git Bash; `cmd.exe /C` hangs. Use the Espressif Python venv as a subprocess with `MSYSTEM*` env vars stripped — the exact command is in `CLAUDE.local.md` (`[python, idf_py, 'build']`, cwd = `firmware/esp32-csi-node`). Outputs in `firmware/esp32-csi-node/build/{bootloader/bootloader.bin, partition_table/partition-table.bin, esp32-csi-node.bin, ota_data_initial.bin}`.
+3. **Flash.** Same subprocess with `[python, idf_py, '-p', 'COM8', 'flash']`, or:
+   ```
+   python -m esptool --chip esp32s3 --port COM8 --baud 460800 write_flash \
+     0x0 firmware/esp32-csi-node/build/bootloader/bootloader.bin \
+     0x8000 firmware/esp32-csi-node/build/partition_table/partition-table.bin \
+     0xf000 firmware/esp32-csi-node/build/ota_data_initial.bin \
+     0x20000 firmware/esp32-csi-node/build/esp32-csi-node.bin
+   ```
+4. **Confirm.** Serial monitor via pyserial on `COM8` @ 115200 (NOT `idf.py monitor` — it hangs in a subprocess). Then `cd v2 && cargo run -p wifi-densepose-sensing-server` — frames should arrive. If not: re-run `/ruview-provision`, match the AP channel, drop any `--filter-mac`.
+
+Never test in mock mode — the Kconfig fall-threshold bug only showed up with real CSI.

.codex/prompts/ruview-provision.md

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+# /ruview-provision — provision an ESP32 sensing node
+
+Write NVS config to a RuView ESP32 node. Args: `$ARGUMENTS` (expect `--port`, `--ssid`, `--password`, `--target-ip`, optional `--channel`, `--filter-mac`). Default port `COM8`.
+
+First get the authoritative flag list: `python firmware/esp32-csi-node/provision.py --help` (on Windows prefix `PYTHONUTF8=1 PYTHONIOENCODING=utf-8` — the help text has non-ASCII and crashes under cp1252). Then run:
+
+```
+python firmware/esp32-csi-node/provision.py --port COM8 \
+  --ssid "<SSID>" --password "<PW>" --target-ip <SINK_IP> --target-port 5005 --node-id <0-255> \
+  [--channel <N>] [--filter-mac <AA:BB:CC:DD:EE:FF>] [--hop-channels 1,6,11 --hop-dwell 200] \
+  [--tdm-slot <i> --tdm-total <n>] [--edge-tier 0|1|2] [--pres-thresh 50] [--fall-thresh 15000] \
+  [--vital-win 300] [--vital-int 1000] [--subk-count 32] \
+  [--seed-url http://10.1.10.236 --seed-token <bearer> --zone lobby] [--swarm-hb 30] [--swarm-ingest 5] [--dry-run]
+```
+
+Trade-offs:
+- `--channel <N>` pins the node to one WiFi channel (set it to the AP's channel). Omit it and pass `--hop-channels 1,6,11` for the firmware's multi-band hopping schedule (more sensing bandwidth, uses neighbour APs as illuminators; `--hop-dwell` ms per channel).
+- `--filter-mac <MAC>` restricts CSI capture to one transmitter (cleaner signal); omit for all transmitters (more data, more noise).
+- `--edge-tier` 0/1/2 = off / stats / vitals (ADR-041). `--tdm-slot`/`--tdm-total` slot a multi-node mesh. `--fall-thresh 15000` ≈ 15.0 rad/s² (raise to cut false falls).
+
+⚠️ **Issue #391:** flashing rewrites the *entire* `csi_cfg` NVS namespace — every key not on the CLI is erased. Pass the full set you want; warn before re-provisioning a working node. `--dry-run` builds the NVS binary without flashing; `--force-partial` allows config without WiFi creds (knowingly).
+
+Fleet provisioning: `python scripts/generate_nvs_matrix.py` (subprocess-first — the `esp_idf_nvs_partition_gen` API changed across versions).
+
+Verify: serial monitor (pyserial on `COM8`, 115200) should show `adaptive_ctrl` ticks + `csi_collector: CSI cb #… len=128 rssi=… ch=…` lines; the sink `cd v2 && cargo run -p wifi-densepose-sensing-server` should report incoming UDP frames if `--target-ip` points at this host. If no frames: wrong channel, MAC filter too tight, target-ip not this host, or WiFi creds wrong — re-run with corrected args.

.codex/prompts/ruview-rvagent.md

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+# ruview-rvagent — explore rvAgent + RVF agentic flows for RuView
+
+You are helping the operator explore or prototype the integration of `vendor/ruvector/crates/rvAgent/` (a production Rust AI-agent framework) with RuView's existing sensing pipeline (`v2/crates/wifi-densepose-*`) and the RVF cognitive container format (`v2/crates/wifi-densepose-sensing-server/src/rvf_container.rs`).
+
+## Live MCP server: `@ruvnet/rvagent` v0.1.0
+
+The TypeScript MCP server (`tools/ruview-mcp/`, published as `@ruvnet/rvagent`) is live on npm and exposes `bfld_last_scan`, `bfld_subscribe`, `presence_now`, `vitals_get_breathing`, `vitals_get_heart_rate`, `vitals_get_all`, `vitals_fetch`. Add to a Codex MCP config:
+
+```json
+{
+  "mcpServers": {
+    "rvagent": {
+      "command": "npx",
+      "args": ["-y", "@ruvnet/rvagent"],
+      "env": { "RVAGENT_SENSING_URL": "http://localhost:3000" }
+    }
+  }
+}
+```
+
+This is the operator-facing tool surface; the Rust crate below remains the substrate for deeper RVF-aware agentic flows.
+
+## Trigger phrasing
+
+- "wire rvAgent into RuView"
+- "I want a queen agent that fans out to cog-pose-estimation and cog-bfld"
+- "persist agent decisions in the same witness bundle as sensing events"
+- "how do I keep agent outputs class-3 compliant?"
+
+## What to read first
+
+1. `docs/research/rvagent-rvf-integration/README.md` — full integration thesis, open questions, next steps.
+2. `vendor/ruvector/crates/rvAgent/README.md` — what rvAgent ships (8 crates, 14 middlewares).
+3. `vendor/ruvector/crates/rvAgent/.ruv/agents/rvagent-queen.md` — queen-agent persona that coordinates cog subagents.
+4. `v2/crates/wifi-densepose-bfld/src/{event.rs,pipeline_handle.rs}` — the BFLD event surface and the operator-facing handle that an agent would call.
+5. `v2/crates/wifi-densepose-sensing-server/src/rvf_container.rs` — segment types; `SEG_AGENT_STATE = 0x08` and `SEG_DECISION = 0x09` are the proposed additions.
+
+## Three shippable touchpoints (each independent)
+
+1. **RVF wire** — add `SEG_AGENT_STATE` + `SEG_DECISION` segments so rvAgent and RuView sessions can interleave in one blob (witness-bundle covers both halves).
+2. **Tool shim** — `BfldEvent::to_json()` already exists; wrap as `rvagent_tools::ToolOutput`.
+3. **Cog subagents** — register `cog-pose-estimation`, `cog-person-count`, `cog-ha-matter`, (proposed) `cog-bfld` under the queen via the `Subagent` trait.
+
+## Open questions to surface
+
+- Is `vendor/ruvector/crates/rvAgent/` on the v2 workspace path?
+- Sync ↔ async adapter location (BFLD `Publish` is sync; rvAgent backends are tokio).
+- Privacy-class composition — does `rvagent-middleware::sanitizer` consume `BfldEvent::privacy_class`?
+- Soul Signature ↔ `SoulMatchOracle` bridge (ADR-121 §2.6).
+- Should `BfldPipelineHandle::send` land as a public MCP tool via `rvagent-mcp`?
+
+## Suggested next action
+
+Draft ADR-124 — "rvAgent + RVF integration for RuView agentic flows" — capturing segment assignments, cog-subagent contract, and privacy-class composition. Land **before** scaffolding `v2/crates/wifi-densepose-agent`.

.codex/prompts/ruview-start.md

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+# /ruview-start — onboard onto RuView
+
+Help me get started with RuView (WiFi-DensePose). Path: `$ARGUMENTS` (one of `docker`, `build`, `hardware`; if empty, ask which hardware I have).
+
+- **docker** (no hardware): `docker pull ruvnet/wifi-densepose:latest && docker run -p 3000:3000 ruvnet/wifi-densepose:latest`, then open http://localhost:3000 (simulated CSI, full UI).
+- **build** (from source): `cd v2 && cargo test --workspace --no-default-features`, then `cd .. && python archive/v1/data/proof/verify.py` (expect `VERDICT: PASS`). Single-crate sanity: `cargo check -p wifi-densepose-train --no-default-features`.
+- **hardware** (ESP32-S3/C6): use `/ruview-flash` then `/ruview-provision`, then `cd v2 && cargo run -p wifi-densepose-sensing-server` to consume the UDP CSI stream. Also: `node scripts/rf-scan.js --port 5006`, `node scripts/snn-csi-processor.js --port 5006`.
+
+Warn me about: ESP32-C3 / original ESP32 are unsupported (single-core); one node = limited spatial resolution (use 2+ or add a Cognitum Seed); camera-free pose is modest — camera-supervised training reaches 92.9% PCK@20 (ADR-079); no cloud/cameras/internet needed.
+
+Then point me at next steps: `/ruview-app`, `/ruview-train`, `/ruview-verify`, and the configuration workflow (sdkconfig variants, NVS provisioning, edge modules, mesh, Cognitum Seed). Reference `README.md`, `docs/user-guide.md`, `docs/build-guide.md`, `docs/TROUBLESHOOTING.md`, `examples/`.

.codex/prompts/ruview-train.md

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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`.

.codex/prompts/ruview-verify.md

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+# /ruview-verify — run the RuView trust pipeline
+
+Verify a RuView build. Scope: `$ARGUMENTS` (one of `tests`, `proof`, `bundle`, `all`; default `all`).
+
+1. **tests** — `cd v2 && cargo test --workspace --no-default-features` → must be 1,400+ passed, 0 failed (~2 min). Single-crate: `cargo test -p wifi-densepose-signal --no-default-features`, etc.
+2. **proof** — `cd .. && python archive/v1/data/proof/verify.py` → must print `VERDICT: PASS`. If a hash mismatch from a legitimate numpy/scipy bump: `python archive/v1/data/proof/verify.py --generate-hash`, then re-run. Optional: `cd archive/v1 && python -m pytest tests/ -x -q`.
+3. **bundle** — `bash scripts/generate-witness-bundle.sh` produces `dist/witness-bundle-ADR028-<sha>.tar.gz` (WITNESS-LOG-028.md, ADR-028 audit, proof, rust test log, firmware hash manifest, crate versions, VERIFY.sh). Then `cd dist/witness-bundle-ADR028-*/ && bash VERIFY.sh` → must be 7/7 PASS.
+4. **all** — do 1→3 in order.
+
+If this follows a code change, walk the pre-merge checklist from `CLAUDE.md`: Rust tests pass; Python proof passes; README updated if scope changed; CLAUDE.md updated if scope changed; CHANGELOG `[Unreleased]` entry; `docs/user-guide.md` updated if new data sources/CLI flags/setup; ADR count bumped in README if a new ADR added; witness bundle regenerated if tests/proof hash changed; Docker image rebuilt only if Dockerfile/deps/runtime changed; crate publishing only if a published crate's public API changed (publish in dependency order — see CLAUDE.md); `.gitignore` updated for new artifacts; security review for new hardware/network-boundary modules.
+
+For security-related changes also run `npx @claude-flow/cli@latest security scan`. QEMU firmware CI (ADR-061): local helpers `scripts/qemu-esp32s3-test.sh`, `qemu-mesh-test.sh`, `qemu-chaos-test.sh`, `install-qemu.sh`.