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>
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# AGENTS.md — RuView (WiFi-DensePose)
Project rules for Codex (and any agent) working in the `ruvnet/RuView` / `wifi-densepose` repo. Mirrors the Claude Code `ruview` plugin.
## What this repo is
WiFi-based human sensing from Channel State Information (CSI). Dual codebase: Rust port in `v2/` (15 crates), Python v1 in `archive/v1/`. ESP32-S3 / ESP32-C6 firmware in `firmware/esp32-csi-node/`. 96 ADRs in `docs/adr/`.
## Hard rules
- Do exactly what's asked — nothing more, nothing less.
- Never create files (especially `*.md`/README) unless required for the task. Prefer editing an existing file.
- Never save working files/tests/notes to the repo root — use `v2/crates/`, `tests/`, `docs/`, `scripts/`, `examples/`.
- Read a file before editing it.
- Never commit secrets, credentials, or `.env`.
- Validate user input at system boundaries; sanitize file paths.
- ESP32-C3 and the original ESP32 are **not supported** (single-core). Use ESP32-S3 (8MB/4MB) or ESP32-C6.
## Build & test
```bash
# Rust workspace (1,400+ tests, ~2 min)
cd v2 && cargo test --workspace --no-default-features
# Single crate, no GPU
cargo check -p wifi-densepose-train --no-default-features
# Deterministic Python pipeline proof (SHA-256 Trust Kill Switch)
python archive/v1/data/proof/verify.py # must print VERDICT: PASS
# Python v1 tests
cd archive/v1 && python -m pytest tests/ -x -q
```
## ESP32 firmware (Windows)
ESP-IDF v5.4 does **not** work under Git Bash/MSYS2 and `cmd.exe /C` hangs when called from bash. Build/flash via the **Espressif Python venv as a subprocess with `MSYSTEM*` env vars stripped** — the exact command is in `CLAUDE.local.md`. Default ESP32 serial port: **COM8** (confirm with `mode` / Device Manager — older docs say COM7 or COM9). Provision WiFi: `python firmware/esp32-csi-node/provision.py --port COM8 --ssid ... --password ... --target-ip ... [--channel N] [--filter-mac MAC]`. Serial monitor via pyserial, not `idf.py monitor`. Always test with real WiFi CSI, never mock mode.
## Witness verification (ADR-028)
After significant changes: run the Rust tests + Python proof, then `bash scripts/generate-witness-bundle.sh`, then `cd dist/witness-bundle-ADR028-*/ && bash VERIFY.sh` (7/7 PASS). Pre-merge checklist lives in `CLAUDE.md`.
## Prompt files in `codex/prompts/`
| Prompt | Purpose |
|--------|---------|
| `ruview-start` | Onboarding — Docker demo / repo build / live ESP32 |
| `ruview-flash` | Build + flash ESP32 firmware (8MB / 4MB) |
| `ruview-provision` | Provision WiFi creds + sink IP + channel/MAC overrides |
| `ruview-app` | Run a sensing application (presence / vitals / pose / sleep / MAT / point cloud) |
| `ruview-train` | Train / evaluate / publish a model (incl. GPU on GCloud) |
| `ruview-verify` | Run the trust pipeline + pre-merge checklist |
Install: copy `codex/prompts/*.md` into `~/.codex/prompts/`, or run Codex with this directory on its prompt path.
## Reference
`README.md`, `docs/user-guide.md`, `docs/wifi-mat-user-guide.md`, `docs/build-guide.md`, `docs/TROUBLESHOOTING.md`, `docs/adr/`, `docs/tutorials/`, `examples/`, `CLAUDE.md`, `CLAUDE.local.md`.
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# RuView prompts for Codex (OpenAI CLI)
This directory mirrors the Claude Code `ruview` plugin's operator commands as Codex prompts, plus an `AGENTS.md` carrying the RuView project rules.
## Contents
| File | Purpose |
|------|---------|
| `AGENTS.md` | Project rules — repo layout, hard rules, build/test, ESP32 firmware on Windows, witness verification |
| `prompts/ruview-start.md` | Onboarding — Docker demo / repo build / live ESP32 |
| `prompts/ruview-flash.md` | Build + flash ESP32 firmware (8MB / 4MB) |
| `prompts/ruview-provision.md` | Provision WiFi creds + sink IP + channel/MAC overrides |
| `prompts/ruview-app.md` | Run a sensing application (presence / vitals / pose / sleep / MAT / point cloud) |
| `prompts/ruview-train.md` | Train / evaluate / publish a model (incl. GPU on GCloud) |
| `prompts/ruview-advanced.md` | Multistatic / tomography / cross-viewpoint / field-model / mesh-security |
| `prompts/ruview-verify.md` | Run the trust pipeline + pre-merge checklist |
Prompt parity with the Claude Code plugin is enforced by `plugins/ruview/scripts/smoke.sh` (every `commands/<name>.md` must have a matching `codex/prompts/<name>.md`).
## Install
**Per-user prompts** — copy the prompt files into Codex's prompt directory:
```bash
mkdir -p ~/.codex/prompts
cp plugins/ruview/codex/prompts/*.md ~/.codex/prompts/
# now in the codex TUI: /ruview-start /ruview-flash /ruview-app /ruview-train /ruview-verify /ruview-advanced
```
**Project rules** — point Codex at the `AGENTS.md`. Codex auto-discovers an `AGENTS.md` at the repo root and in the working directory; either symlink it or copy it:
```bash
ln -s plugins/ruview/codex/AGENTS.md AGENTS.md # repo root (if you don't already have one)
# — or, if a root AGENTS.md exists, append the relevant sections from plugins/ruview/codex/AGENTS.md
```
**Config (optional)** — to keep prompts in-repo instead of `~/.codex/prompts`, add to `~/.codex/config.toml`:
```toml
# Codex reads prompts from ~/.codex/prompts by default; symlinking keeps them versioned with the repo:
# ln -s "$PWD/plugins/ruview/codex/prompts" ~/.codex/prompts/ruview (then prompts appear as /ruview/ruview-start, etc.)
```
## Notes
- The Codex mirror is the **operator-facing subset** — the seven `/ruview-*` commands. The Claude Code plugin additionally ships skills (`ruview-quickstart`, `ruview-hardware-setup`, `ruview-configure`, `ruview-applications`, `ruview-model-training`, `ruview-advanced-sensing`, `ruview-cli-api`, `ruview-mmwave`, `ruview-verify`) and agents (`ruview-onboarding-guide`, `ruview-config-engineer`, `ruview-training-engineer`) that have no Codex equivalent — their content is folded into `AGENTS.md` and the prompt files.
- On Windows, ESP-IDF firmware builds go through the Python-subprocess pattern documented in `CLAUDE.local.md` (Git Bash / MSYS2 is not supported by ESP-IDF v5.4). Default ESP32 serial port: **COM8**.
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# /ruview-advanced — advanced RuView capabilities
Drive RuView's research-grade / multi-node features. Topic: `$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, CramérRao 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 200500 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`.
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# /ruview-app — run a RuView sensing application
Run a RuView application. Which one: `$ARGUMENTS` (one of `presence`, `vitals`, `pose`, `sleep`, `environment`, `mat`, `pointcloud`, or a novel-RF app name; if empty, show the catalogue and ask).
- **presence / vitals / pose / environment** → `cd v2 && cargo run -p wifi-densepose-sensing-server` against a live ESP32 sink, or the Docker demo (`docker run -p 3000:3000 ruvnet/wifi-densepose:latest`) for simulated CSI. For environment also `-- --model model.rvf --build-index env`. Vitals: breathing 630 BPM (bandpass 0.10.5 Hz), heart rate 40120 BPM (bandpass 0.82.0 Hz), `wifi-densepose-vitals` crate (ADR-021). Pose: 17 COCO keypoints via WiFlow (ADR-059 live pipeline) — train for accuracy (`/ruview-train`).
- **sleep** → `examples/sleep/` + `node scripts/apnea-detector.js` (sleep-stage classification, apnea screening).
- **mat** (Mass Casualty Assessment — disaster survivor detection) → `wifi-densepose-mat` crate, `docs/wifi-mat-user-guide.md`.
- **pointcloud** → `python scripts/mmwave_fusion_bridge.py` (camera depth via MiDaS + WiFi CSI + mmWave radar → unified spatial model, ~22 ms, 19K+ pts/frame; ADR-094).
- **novel RF** → `scripts/passive-radar.js`, `material-classifier.js`, `device-fingerprint.js`, `mincut-person-counter.js`, `gait-analyzer.js` (ADR-077/078).
No hardware? Fall back to the Docker demo or `python examples/ruview_live.py`. Visualisers: `node scripts/csi-spectrogram.js`, `node scripts/csi-graph-visualizer.js`.
Help me pick: through-wall → presence/activity (≤5 m depth); stationary subject → vitals/sleep; need skeletons → pose (train it); search & rescue → MAT; best spatial accuracy → 2+ ESP32 nodes + cross-viewpoint fusion (`v2/crates/wifi-densepose-ruvector/src/viewpoint/`), optionally + Cognitum Seed. Examples: `examples/{environment,medical,sleep,stress,happiness-vector}/`.
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# /ruview-flash — build + flash ESP32 firmware
Build and flash RuView ESP32 firmware. Variant + port: `$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.
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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.
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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/`.
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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` (~$3.60/hr), `bash scripts/gcloud-train.sh --sweep` (full sweep). VM auto-deletes unless `--keep-vm`. Local Mac: `bash scripts/mac-mini-train.sh`. Bench: `python scripts/benchmark-model.py`.
Data: `data/recordings/` raw CSI · `data/csi/` pretrain · `data/mmfi/` MM-Fi · `data/paired/` camera↔CSI · `data/ground-truth/` MediaPipe · `models/` artifacts. Record more: `python scripts/record-csi-udp.py`.
After training: `cd v2 && cargo test --workspace --no-default-features`, `cd .. && python archive/v1/data/proof/verify.py` (VERDICT: PASS). Publish: `python scripts/publish-huggingface.py` (or `.sh`; `docs/huggingface/`). Then run `/ruview-verify`.
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# /ruview-verify — run the RuView trust pipeline
Verify a RuView build. Scope: `$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`.