* feat(calibration): NodeGeometry transceiver-geometry recording (ADR-152 §2.1.1) PerceptAlign-motivated geometry capture at enrollment: per-node optional records (position, antenna orientation, inter-node distances, acquisition method) — recorded when known, never required. Event-sourced via EnrollmentEvent::GeometryRecorded (latest recording wins); persisted on SpecialistBank with serde defaults so pre-ADR-152 bank JSON loads cleanly (fixture-proven, and geometry-free banks serialize byte-shape-identical to the old schema); threaded through MultiNodeMixture as data only — the learned geometry embeddings and algorithmic fusion use are §2.1.2, deliberately deferred until the ADR-151 P6 LoRA heads exist. Geometry recorded from now on means banks captured today remain usable for layout-conditioned training later — you can't retroactively add geometry to data you didn't record. 8 new tests (3 geometry, 2 anchor, 2 bank, 1 multistatic) + full-loop extension (2-node geometry, one tape-measured + one unknown, surviving the bank JSON round-trip the runtime loads from). 50/50 calibration (both feature configs) + 23 CLI tests green. Co-Authored-By: RuFlo <ruv@ruv.net> * feat(training): two-checkerboard camera↔room calibration for ADR-079 labels (ADR-152 §2.1.3) Defends the camera-supervised pipeline against PerceptAlign's "coordinate overfitting": MediaPipe keypoints were emitted in raw camera coordinates with no shared frame and no transceiver-geometry metadata — the exact label shape that memorizes deployment layout and collapses cross-layout. - scripts/calibrate-camera-room.py + calibration_lib.py: OpenCV two-checkerboard calibration → versioned bundle JSON (intrinsics, camera→room extrinsics, checkerboard spec, transceiver geometry, sha256 calibration_id). Intrinsics resolve from file > cache > multi-view computation > loud-warning 2-view fallback. - collect-ground-truth.py --calibration <bundle>: every sample gains keypoints_room (unit bearing rays from the camera center in the room frame — documented projective alignment; raw image coords preserved so training chooses), camera_origin_room, calibration_id, and the transceiver geometry stamp. Without the flag, output is byte-identical to before (tested) + a one-line ADR-152 warning. Design finding (recorded for ADR-152): a single planar checkerboard's corner grid is centrosymmetric — the reversed corner ordering fits a ghost camera pose with IDENTICAL reprojection error, so per-board flip disambiguation is mathematically ill-posed. solve_two_board_extrinsics solves the joint wall+floor set over all 4 flip combinations, where the minimum is unique — an independent reason the TWO-checkerboard method is required, beyond what PerceptAlign states. 15 headless pytest tests green (synthetic corners: extrinsics recovery incl. ghost resolution, bundle round-trip + hash stability, ray transforms w/ distortion + cross-resolution, no-calibration byte identity). Co-Authored-By: RuFlo <ruv@ruv.net> * feat(benchmarks): WiFlow-STD reproduction harness + measurement (a) results (ADR-152 §2.2) Shipped checkpoint REFUTED (0.08% PCK@20, wrong keypoint normalization); 6 reproducibility defects documented (broken imports, corrupted dataset tail with float32-max garbage that NaN-poisons fp16 BatchNorm, unreachable test phase). After repairs, retraining with upstream defaults reproduces 96.09% PCK@20 full-test / 96.61% corruption-free (published 97.25%) on RTX 5080. Claims graded MEASURED-EQUIVALENT; 2.23M params + ~0.055 GFLOPs verified. Third-party code/weights/data stay out of tree (gitignored). Co-Authored-By: claude-flow <ruv@ruv.net> * feat: ADR-152 Rust integrations + ADR-153 802.11bf protocol model - calibration: GeometryEmbedding — 32-slot permutation-invariant NodeGeometry featurization for future LoRA-head conditioning (ADR-152 §2.1.2); derived SpecialistBank::geometry_embedding() accessor; 59 tests - train: MaePretrainConfig + patchify/random-mask with UNSW measured recipe (80% masking, (30,3) patches; ADR-152 §2.3, arXiv 2511.18792); strict no-truncate/no-NaN policy; proptest properties - train: WiFlowStdModel — tch-gated port of the verified ~96%-PCK@20 WiFlow-STD architecture (ADR-152 §2.2 beyond-SOTA); ungated param formula pinned to 2,225,042; 15/17-keypoint support; 239 crate tests - hardware: ieee80211bf forward-compatibility protocol model (ADR-153): SpecProfile gates, SensingCapabilities negotiation, required ConsentMode, session FSM, SensingTransport + SimTransport + OpportunisticCsiBridge; full acceptance checklist covered; 156+4 tests - deps: ruvector bumps per ADR-152 §2.6 survey (mincut/solver 2.0.6, attention 2.1.0, gnn 2.2.0); vendor/ruvector synced to a083bd77f - docs: ADR-153 accepted; ADR-152 §2.2 status, §2.4 amendment, §2.6 added Workspace: 162 test suites green (--no-default-features); Python proof PASS. Known pre-existing flake: homecore-api env_empty_falls_back_to_defaults (unserialized env-var mutation) — untouched, follow-up. Co-Authored-By: claude-flow <ruv@ruv.net> * docs: CHANGELOG + CLAUDE.md entries for ADR-152 integrations and ADR-153 Co-Authored-By: claude-flow <ruv@ruv.net> * fix(train): repair tch-backend bit-rot — gated path compiles and tests run again Mechanical API refresh against current tch: Vec::from(Tensor) -> try_from (+ explicit flatten), numel() usize cast, Rem/div ops -> remainder() / divide_scalar_mode(floor) — the latter fixed a silent true-division bug in heatmap argmax decoding; clamp(1.0, f64::MAX) -> clamp_min (torch 2.x scalar overflow panic); petgraph EdgeRef import; missing EvalMetrics and verify_checkpoint_dir APIs that tests documented. wiflow_std roundtrip test uses safetensors (.pt _save_parameters roundtrip broken in torch 2.11 Windows). Gated: 349 passed (incl. all 20 wiflow_std); ungated: unchanged. Known pre-existing: gaussian-heatmap convention mismatch (2 tests), proof seed race under parallel threads — documented, deliberate follow-ups. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(train): WiFlow-STD PyTorch->tch weight import + numerical parity proof export_to_safetensors.py maps the retrained checkpoint (295 tensors -> 248 mapped, param sum exactly 2,225,042; num_batches_tracked dropped) into a tch-loadable safetensors plus a deterministic parity fixture. Gated #[ignore] integration test loads it strictly and asserts forward-pass agreement: max abs diff 1.192e-7 on the seed-42 fixture. dump_variable_names test makes the tch name layout authoritative. Zero architecture discrepancies found. Co-Authored-By: claude-flow <ruv@ruv.net> * fix: workflow-review findings — BN gamma init, ThresholdParams serde, init docs Concurrent validation workflow (2 review lanes + adversarial verification, 13 agents): 5 confirmed findings, 3 refuted. Fixes: - wiflow_std: pin BatchNorm gamma to 1.0 (tch default draws Uniform(0,1) — silently halves activations in from-scratch training; loaded checkpoints unaffected, parity re-verified after the change) - wiflow_std: document the conv-init divergences vs the reference's effective kaiming_normal(fan_out) re-init (from-scratch dynamics only) - ieee80211bf: ThresholdParams deserialization validates via try_from so the <=100 invariant holds for untrusted payloads (+ rejection test) Benchmarks (release, ruvzen): GeometryEmbedding 1.84us/call (542k/s), MAE tokenization 7.38us/window (135k/s), 802.11bf FSM 8.9M events/s — nothing suspicious. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(adr): ADR-152 §2.1.4 gate resolved — PerceptAlign repo MIT, dataset on HF Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): edge optimization measured + measurement (b) blocked + 92.9% retraction Edge optimization (ADR-152 optimize track): ONNX Runtime fp32 is the CPU latency win (3.2 ms/window, ~3.4x faster than torch, parity 2.4e-7); ORT dynamic int8 reaches 2.44 MB (paper's ~2.2 MB claim plausible only via conv-capable toolchains; -0.16pt PCK@20, +18% MPJPE, 2x slower); torch dynamic quant converts 0% of this conv-only model; fp16 halves storage free but is slower on CPU. Measurement (b) BLOCKED-ON-DATA: only 1,077 paired ESP32 windows exist (stop rule <2k). Forensic recheck of the surviving April holdout RETRACTS the ADR-079 '92.9% PCK@20' figure: constant-output model, absolute (not torso) threshold, 69 near-static frames — mean predictor scores 100% under that protocol; torso-PCK@20 is 19.1%. Corroborates PR #535. Stale citations removed from user-guide, readme-details, ADR-152 §2.1.3; no-citation rule extended to ADR-079 accuracy claims. Unblock: >=2k-window multi-pose paired session + torso-PCK re-baseline. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(user-guide): corrected camera-supervised collection tutorial Step 0 CSI-rate check + session-length math (window yield = frames/20 — the May session's 8x under-delivery was a ~12 Hz CSI rate, not an aligner bug); two-checkerboard calibration step (ADR-152 §2.1.3); pose-variety and confidence guidance; torso-normalized PCK + temporal-split + pred-variance eval protocol (lessons from the 92.9% retraction); scale presets re-keyed to realistic window counts. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): static PTQ int8 (calibrated) results + overnight capture script Conv-only static QDQ beats dynamic int8 on accuracy (PCK@20 96.61-96.63% vs 96.52%, MPJPE +10% vs +18% over fp32) at ~equal size/latency; all-ops QDQ strictly worse (int8 activations through attention glue). Entropy calibration verified bit-identical to MinMax on this data. Deployment: ONNX fp32 for speed (3.2ms), static conv-only QDQ for smallest (2.53MB). Also: scripts/overnight-empty-capture.py — segmented UDP CSI recorder for empty-room baselines (no glob collisions, detach-safe). Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): measurement (b) MEASURED — optimization transfer only, mean-pose baseline wins WiFlow-STD fine-tuned on 2,046 fresh single-room ESP32 paired windows (temporal 70/15/15, 70->540 adapter, K=17): pretrained-init 65% PCK@20 vs scratch 0% (optimization transfer) but frozen-trunk ~0% (no feature transfer), and NOTHING beats the mean-pose baseline (95.9% PCK@20 — single subject, near-static normalized coords). Honesty gates held: pred std 0.0113 (non-constant model) but mean-baseline dominance means no citable CSI->pose capability from this data. ADR-152 open question 1 answered partially; definitive answer needs multi-subject/position data. Two new aligner findings: heterogeneous csi_shape with silent zero-padding (~20%), and extractCsiMatrix's transposed shape label (frame-major data, [nSc, nFrames] label) — fixes pending. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): efficiency sweep MEASURED — half model dominates full reference Compact WiFlow-STD variants on the same data/split/protocol: half (843,834 params, 0.38x) strictly dominates the 2.23M reference (PCK@20 96.62 vs 96.61, PCK@50 99.47 vs 99.11, MPJPE 0.00898 vs 0.0094) — the published architecture is over-parameterized for its own benchmark. quarter (338k) 96.05%; tiny (56,290 params, 1/39.5) holds 94.11% — a ~220KB fp32 edge candidate. In-domain caveats recorded; cross-domain untested. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(train): compact WiFlow-STD presets in Rust + tiny edge artifact (ADR-152) WiFlowStdConfig gains half()/quarter()/tiny() mirroring the overnight sweep exactly: TcnGroupsMode (Fixed/Gcd/Depthwise), input_pw_groups, derived stride schedule and decoder-mid (all default to upstream behavior; legacy serde JSON unaffected). Param formulas pin to trained ground truth first try: 843,834 / 338,600 / 56,290; default 2,225,042 pin and 1.192e-7 parity unchanged. 248 tests green. Tiny edge artifact (tiny_edge_bench.py): ONNX fp32 = 295 KB, 0.66 ms/win (~1,500/s CPU), 94.11% PCK@20 (matches sweep clean-test exactly; parity 1.49e-7). Static int8 is a bad trade at this scale (-1.43pt, +19% MPJPE, -16% size, slower) — recorded as negative result. Export note: width-16 breaks AdaptiveAvgPool((15,1)) TorchScript export; replaced by exact mean+matmul equivalent, proven by parity. Co-Authored-By: claude-flow <ruv@ruv.net> * fix: resolve all 10 confirmed code-review findings (7-angle review, 20/20 verified) wiflow_std: min_feature_width (default 15) replaces the keypoints->stride coupling — for_keypoints(17) now provably builds the trained [2,2,2,2] graph and pools 15->17, matching the validated Python protocol (pinned by tests); param_count() total on invalid configs; random_mask returns Result and rejects non-finite/out-of-range ratios; trainer checkpoints switched to safetensors (.pt VarStore roundtrip broken on Windows torch 2.11). ieee80211bf: SBP proxy now re-triggers instances and relays reports via Action::RelaySbpReport -> SensingFrame::SbpReport (clients consume via their existing path); missed_instances reset on success = consecutive semantics; SessionTable gains a guarded SBP entry point + unknown-id drop counter; initiator-role sessions reject inbound setup/SBP requests (RejectedNotSupported) closing the idle hijack; StartSetup/StartSbp outside Idle return InvalidStateForCommand; SBP validation unified through evaluate_setup with a 1:1 SetupStatus->SbpStatus mapping. events.rs split out to honor the 500-line cap. calibration/cli: enrollment geometry now actually reaches trained banks — both production call sites attach .with_geometry; --geometry flag on train-room and POST /enroll/geometry + train-body geometry on calibrate-serve give production a recording surface; geometry-free banks log the ADR-152 §2.1.2 note. benchmarks: corruption masks committed as ground truth (unregenerable after in-place cleaning; verified bit-identical regeneration from the pristine copy) + generate_corruption_masks.py producer; _bench_common.py dedups the 5x-copied shim/evaluate/seed/remap (post-refactor PCK@20 re-verified equal to the last digit); remote scripts get the mmap patch; tiny_edge --calib validated multiple-of-64; onnx_bench --help no longer executes (and overwrote) the export — artifact restored byte-exact. Workspace: 2,963 tests passed, 0 failed; Python proof PASS. Co-Authored-By: claude-flow <ruv@ruv.net> * ci: build workspace tests without debuginfo — runner disk exhaustion The combined 38-crate debug target exceeds the GitHub runner's disk ('final link failed: No space left on device'); the same tree measured 151GB locally with full debuginfo. CARGO_PROFILE_{DEV,TEST}_DEBUG=0 shrinks the target ~5-10x; debuginfo serves no purpose in CI test runs. Co-Authored-By: claude-flow <ruv@ruv.net>
19 KiB
Claude Code Configuration — WiFi-DensePose + Claude Flow V3
Project: wifi-densepose
WiFi-based human pose estimation using Channel State Information (CSI).
Dual codebase: Python v1 (v1/) and Rust port (v2/).
Key Rust Crates
| Crate | Description |
|---|---|
wifi-densepose-core |
Core types, traits, error types, CSI frame primitives |
wifi-densepose-signal |
SOTA signal processing + RuvSense multistatic sensing (16 modules) |
wifi-densepose-nn |
Neural network inference (ONNX, PyTorch, Candle backends) |
wifi-densepose-train |
Training pipeline with ruvector integration + ruview_metrics; MAE pretraining recipe (mae.rs, ADR-152 §2.3) + WiFlow-STD port (wiflow_std/, tch-gated) |
wifi-densepose-mat |
Mass Casualty Assessment Tool — disaster survivor detection |
wifi-densepose-hardware |
ESP32 aggregator, TDM protocol, channel hopping firmware; ieee80211bf/ 802.11bf forward-compat protocol model (ADR-153) |
wifi-densepose-ruvector |
RuVector v2.0.4 integration + cross-viewpoint fusion (5 modules) |
wifi-densepose-wasm |
WebAssembly bindings for browser deployment |
wifi-densepose-cli |
CLI tool (wifi-densepose binary) — calibrate/calibrate-serve/enroll/train-room/room-watch + MAT (MAT gated behind the mat feature; build --no-default-features for the aarch64/appliance calibration binary) |
wifi-densepose-calibration |
ADR-151 per-room calibration & specialist training — baseline → enroll → extract → train → bank of small specialists (presence/posture/breathing/heartbeat/restlessness/anomaly) + multistatic fusion; pure Rust, edge-deployable |
wifi-densepose-sensing-server |
Lightweight Axum server for WiFi sensing UI |
wifi-densepose-wifiscan |
Multi-BSSID WiFi scanning (ADR-022) |
wifi-densepose-vitals |
ESP32 CSI-grade vital sign extraction (ADR-021) |
nvsim |
Deterministic NV-diamond magnetometer pipeline simulator (ADR-089) — standalone leaf, WASM-ready |
vendor/rvcsi (submodule) |
rvCSI — edge RF sensing runtime (ADR-095/096): 9 crates (rvcsi-core/-dsp/-events/-adapter-file/-adapter-nexmon/-ruvector/-runtime/-node/-cli). Lives in its own repo (github.com/ruvnet/rvcsi), vendored here under vendor/rvcsi, published to crates.io as rvcsi-* 0.3.x and to npm as @ruv/rvcsi. Not a v2/ workspace member — depend on the published crates (or the submodule's crates/rvcsi-* paths). Normalized CsiFrame/CsiWindow/CsiEvent schema, validate-before-FFI, reusable DSP, typed confidence-scored events, the napi-c Nexmon shim (real nexmon_csi .pcap from a Raspberry Pi 5 / 4 / 3B+ — BCM43455c0), the napi-rs SDK, the rvcsi CLI, a Claude Code plugin. |
ruview-swarm |
Drone swarm control system (ADR-148) — hierarchical-mesh topology, Raft consensus, MARL, CSI sensing payload, MAVLink/PX4 compat, Ruflo AI-agent integration |
RuvSense Modules (signal/src/ruvsense/)
| Module | Purpose |
|---|---|
multiband.rs |
Multi-band CSI frame fusion, cross-channel coherence |
phase_align.rs |
Iterative LO phase offset estimation, circular mean |
multistatic.rs |
Attention-weighted fusion, geometric diversity |
coherence.rs |
Z-score coherence scoring, DriftProfile |
coherence_gate.rs |
Accept/PredictOnly/Reject/Recalibrate gate decisions |
pose_tracker.rs |
17-keypoint Kalman tracker with AETHER re-ID embeddings |
field_model.rs |
SVD room eigenstructure, perturbation extraction |
tomography.rs |
RF tomography, ISTA L1 solver, voxel grid |
longitudinal.rs |
Welford stats, biomechanics drift detection |
intention.rs |
Pre-movement lead signals (200-500ms) |
cross_room.rs |
Environment fingerprinting, transition graph |
gesture.rs |
DTW template matching gesture classifier |
adversarial.rs |
Physically impossible signal detection, multi-link consistency |
cir.rs |
ADR-134 CSI→CIR via ISTA L1 sparse recovery (NeumannSolver warm-start) |
calibration.rs |
ADR-135 empty-room baseline (Welford amplitude + von Mises phase, drift trigger) |
Cross-Viewpoint Fusion (ruvector/src/viewpoint/)
| Module | Purpose |
|---|---|
attention.rs |
CrossViewpointAttention, GeometricBias, softmax with G_bias |
geometry.rs |
GeometricDiversityIndex, Cramer-Rao bounds, Fisher Information |
coherence.rs |
Phase phasor coherence, hysteresis gate |
fusion.rs |
MultistaticArray aggregate root, domain events |
RuVector v2.0.4 Integration (ADR-016 complete, ADR-017 proposed)
All 5 ruvector crates integrated in workspace:
ruvector-mincut→metrics.rs(DynamicPersonMatcher) +subcarrier_selection.rsruvector-attn-mincut→model.rs(apply_antenna_attention) +spectrogram.rsruvector-temporal-tensor→dataset.rs(CompressedCsiBuffer) +breathing.rsruvector-solver→subcarrier.rs(sparse interpolation 114→56) +triangulation.rsruvector-attention→model.rs(apply_spatial_attention) +bvp.rs
Architecture Decisions
43 ADRs in docs/adr/ (ADR-001 through ADR-043). Key ones:
- ADR-014: SOTA signal processing (Accepted)
- ADR-015: MM-Fi + Wi-Pose training datasets (Accepted)
- ADR-016: RuVector training pipeline integration (Accepted — complete)
- ADR-017: RuVector signal + MAT integration (Proposed — next target)
- ADR-024: Contrastive CSI embedding / AETHER (Accepted)
- ADR-027: Cross-environment domain generalization / MERIDIAN (Accepted)
- ADR-028: ESP32 capability audit + witness verification (Accepted)
- ADR-029: RuvSense multistatic sensing mode (Proposed)
- ADR-030: RuvSense persistent field model (Proposed)
- ADR-031: RuView sensing-first RF mode (Proposed)
- ADR-032: Multistatic mesh security hardening (Proposed)
- ADR-148: Drone swarm control system /
ruview-swarm(In Progress) - ADR-152: WiFi-Pose SOTA 2026 intake — geometry conditioning, WiFlow-STD benchmark (measurement (a) complete: claims MEASURED-EQUIVALENT at ~96% PCK@20), MAE recipe (Proposed; §2.1–2.3, 2.6 implemented)
- ADR-153: IEEE 802.11bf-2025 forward-compatibility protocol model (Accepted — amends ADR-152 §2.4)
Supported Hardware
| Device | Port | Chip | Role | Cost |
|---|---|---|---|---|
| ESP32-S3 (8MB flash) | COM9 (ruvzen, was COM7) | Xtensa dual-core | WiFi CSI sensing node | ~$9 |
| ESP32-S3 SuperMini (4MB) | — | Xtensa dual-core | WiFi CSI (compact) | ~$6 |
| ESP32-C6 + Seeed MR60BHA2 | COM12 (ruvzen, was COM4) | RISC-V + 60 GHz FMCW | mmWave HR/BR/presence + WiFi CSI | ~$15 |
| HLK-LD2410 | — | 24 GHz FMCW | Presence + distance | ~$3 |
Not supported: ESP32 (original), ESP32-C3 — single-core, can't run CSI DSP pipeline.
Build & Test Commands (this repo)
# Rust — full workspace tests (1,031+ tests, ~2 min)
cd v2
cargo test --workspace --no-default-features
# Rust — single crate check (no GPU needed)
cargo check -p wifi-densepose-train --no-default-features
# Python — deterministic proof verification (SHA-256)
python archive/v1/data/proof/verify.py
# Python — test suite
cd archive/v1 && python -m pytest tests/ -x -q
ESP32 Firmware Build (Windows — Python subprocess required)
# Build 8MB firmware (real WiFi CSI mode, no mocks)
# See CLAUDE.local.md for the full Python subprocess command
# Key: must strip MSYSTEM env vars for ESP-IDF v5.4 on Git Bash
# Build 4MB firmware
cp sdkconfig.defaults.4mb sdkconfig.defaults
# then same build process
# Flash to COM7
# [python, idf_py, '-p', 'COM7', 'flash']
# Provision WiFi
python firmware/esp32-csi-node/provision.py --port COM7 \
--ssid "YourWiFi" --password "secret" --target-ip 192.168.1.20
# Monitor serial
python -m serial.tools.miniterm COM7 115200
Firmware Release Process
- Build 8MB from
sdkconfig.defaults.template(no mock) - Build 4MB from
sdkconfig.defaults.4mb(no mock) - Save 6 binaries:
esp32-csi-node.bin,bootloader.bin,partition-table.bin,ota_data_initial.bin,esp32-csi-node-4mb.bin,partition-table-4mb.bin - Tag:
git tag v0.X.Y-esp32 && git push origin v0.X.Y-esp32 - Release:
gh release create v0.X.Y-esp32 <binaries> --title "..." --notes-file ... - Verify on real hardware (COM7) before publishing
- CRITICAL: Always test with real WiFi CSI, not mock mode — mock missed the Kconfig threshold bug
Crate Publishing Order
Crates must be published in dependency order:
wifi-densepose-core(no internal deps)wifi-densepose-vitals(no internal deps)wifi-densepose-wifiscan(no internal deps)wifi-densepose-hardware(no internal deps)wifi-densepose-signal(depends on core)wifi-densepose-nn(no internal deps, workspace only)wifi-densepose-ruvector(no internal deps, workspace only)wifi-densepose-train(depends on signal, nn)wifi-densepose-mat(depends on core, signal, nn)wifi-densepose-wasm(depends on mat)wifi-densepose-sensing-server(depends on wifiscan)wifi-densepose-cli(depends on mat)
Validation & Witness Verification (ADR-028)
After any significant code change, run the full validation:
# 1. Rust tests — must be 1,031+ passed, 0 failed
cd v2
cargo test --workspace --no-default-features
# 2. Python proof — must print VERDICT: PASS
cd ..
python archive/v1/data/proof/verify.py
# 3. Generate witness bundle (includes both above + firmware hashes)
bash scripts/generate-witness-bundle.sh
# 4. Self-verify the bundle — must be 7/7 PASS
cd dist/witness-bundle-ADR028-*/
bash VERIFY.sh
If the Python proof hash changes (e.g., numpy/scipy version update):
# Regenerate the expected hash, then verify it passes
python archive/v1/data/proof/verify.py --generate-hash
python archive/v1/data/proof/verify.py
Witness bundle contents (dist/witness-bundle-ADR028-<sha>.tar.gz):
WITNESS-LOG-028.md— 33-row attestation matrix with evidence per capabilityADR-028-esp32-capability-audit.md— Full audit findingsproof/verify.py+expected_features.sha256— Deterministic pipeline prooftest-results/rust-workspace-tests.log— Full cargo test outputfirmware-manifest/source-hashes.txt— SHA-256 of all 7 ESP32 firmware filescrate-manifest/versions.txt— All 15 crates with versionsVERIFY.sh— One-command self-verification for recipients
Key proof artifacts:
archive/v1/data/proof/verify.py— Trust Kill Switch: feeds reference signal through production pipeline, hashes outputarchive/v1/data/proof/expected_features.sha256— Published expected hasharchive/v1/data/proof/sample_csi_data.json— 1,000 synthetic CSI frames (seed=42)docs/WITNESS-LOG-028.md— 11-step reproducible verification proceduredocs/adr/ADR-028-esp32-capability-audit.md— Complete audit record
Branch
Default branch: main
Active feature branch: ruvsense-full-implementation (PR #77)
Behavioral Rules (Always Enforced)
- Do what has been asked; nothing more, nothing less
- NEVER create files unless they're absolutely necessary for achieving your goal
- ALWAYS prefer editing an existing file to creating a new one
- NEVER proactively create documentation files (*.md) or README files unless explicitly requested
- NEVER save working files, text/mds, or tests to the root folder
- Never continuously check status after spawning a swarm — wait for results
- ALWAYS read a file before editing it
- NEVER commit secrets, credentials, or .env files
File Organization
- NEVER save to root folder — use the directories below
docs/adr/— Architecture Decision Records (43 ADRs)docs/ddd/— Domain-Driven Design modelsv2/crates/— Rust workspace crates (15 crates)v2/crates/wifi-densepose-signal/src/ruvsense/— RuvSense multistatic modules (14 files)v2/crates/wifi-densepose-ruvector/src/viewpoint/— Cross-viewpoint fusion (5 files)v2/crates/wifi-densepose-hardware/src/esp32/— ESP32 TDM protocolfirmware/esp32-csi-node/main/— ESP32 C firmware (channel hopping, NVS config, TDM)archive/v1/src/— Python source (core, hardware, services, api)archive/v1/data/proof/— Deterministic CSI proof bundles.claude-flow/— Claude Flow coordination state (committed for team sharing).claude/— Claude Code settings, agents, memory (committed for team sharing)
Project Architecture
- Follow Domain-Driven Design with bounded contexts
- Keep files under 500 lines
- Use typed interfaces for all public APIs
- Prefer TDD London School (mock-first) for new code
- Use event sourcing for state changes
- Ensure input validation at system boundaries
Project Config
- Topology: hierarchical-mesh
- Max Agents: 15
- Memory: hybrid
- HNSW: Enabled
- Neural: Enabled
Pre-Merge Checklist
Before merging any PR, verify each item applies and is addressed:
- Rust tests pass —
cargo test --workspace --no-default-features(1,031+ passed, 0 failed) - Python proof passes —
python archive/v1/data/proof/verify.py(VERDICT: PASS) - README.md — Update platform tables, crate descriptions, hardware tables, feature summaries if scope changed
- CLAUDE.md — Update crate table, ADR list, module tables, version if scope changed
- CHANGELOG.md — Add entry under
[Unreleased]with what was added/fixed/changed - User guide (
docs/user-guide.md) — Update if new data sources, CLI flags, or setup steps were added - ADR index — Update ADR count in README docs table if a new ADR was created
- Witness bundle — Regenerate if tests or proof hash changed:
bash scripts/generate-witness-bundle.sh - Docker Hub image — Only rebuild if Dockerfile, dependencies, or runtime behavior changed
- Crate publishing — Only needed if a crate is published to crates.io and its public API changed
.gitignore— Add any new build artifacts or binaries- Security audit — Run security review for new modules touching hardware/network boundaries
Build & Test
# Build
npm run build
# Test
npm test
# Lint
npm run lint
- ALWAYS run tests after making code changes
- ALWAYS verify build succeeds before committing
Security Rules
- NEVER hardcode API keys, secrets, or credentials in source files
- NEVER commit .env files or any file containing secrets
- Always validate user input at system boundaries
- Always sanitize file paths to prevent directory traversal
- Run
npx @claude-flow/cli@latest security scanafter security-related changes
Concurrency: 1 MESSAGE = ALL RELATED OPERATIONS
- All operations MUST be concurrent/parallel in a single message
- Use Claude Code's Task tool for spawning agents, not just MCP
- ALWAYS batch ALL todos in ONE TodoWrite call (5-10+ minimum)
- ALWAYS spawn ALL agents in ONE message with full instructions via Task tool
- ALWAYS batch ALL file reads/writes/edits in ONE message
- ALWAYS batch ALL Bash commands in ONE message
Swarm Orchestration
- MUST initialize the swarm using CLI tools when starting complex tasks
- MUST spawn concurrent agents using Claude Code's Task tool
- Never use CLI tools alone for execution — Task tool agents do the actual work
- MUST call CLI tools AND Task tool in ONE message for complex work
3-Tier Model Routing (ADR-026)
| Tier | Handler | Latency | Cost | Use Cases |
|---|---|---|---|---|
| 1 | Agent Booster (WASM) | <1ms | $0 | Simple transforms (var→const, add types) — Skip LLM |
| 2 | Haiku | ~500ms | $0.0002 | Simple tasks, low complexity (<30%) |
| 3 | Sonnet/Opus | 2-5s | $0.003-0.015 | Complex reasoning, architecture, security (>30%) |
- Always check for
[AGENT_BOOSTER_AVAILABLE]or[TASK_MODEL_RECOMMENDATION]before spawning agents - Use Edit tool directly when
[AGENT_BOOSTER_AVAILABLE]
Swarm Configuration & Anti-Drift
- ALWAYS use hierarchical topology for coding swarms
- Keep maxAgents at 6-8 for tight coordination
- Use specialized strategy for clear role boundaries
- Use
raftconsensus for hive-mind (leader maintains authoritative state) - Run frequent checkpoints via
post-taskhooks - Keep shared memory namespace for all agents
npx @claude-flow/cli@latest swarm init --topology hierarchical --max-agents 8 --strategy specialized
Swarm Execution Rules
- ALWAYS use
run_in_background: truefor all agent Task calls - ALWAYS put ALL agent Task calls in ONE message for parallel execution
- After spawning, STOP — do NOT add more tool calls or check status
- Never poll TaskOutput or check swarm status — trust agents to return
- When agent results arrive, review ALL results before proceeding
V3 CLI Commands
Core Commands
| Command | Subcommands | Description |
|---|---|---|
init |
4 | Project initialization |
agent |
8 | Agent lifecycle management |
swarm |
6 | Multi-agent swarm coordination |
memory |
11 | AgentDB memory with HNSW search |
task |
6 | Task creation and lifecycle |
session |
7 | Session state management |
hooks |
17 | Self-learning hooks + 12 workers |
hive-mind |
6 | Byzantine fault-tolerant consensus |
Quick CLI Examples
npx @claude-flow/cli@latest init --wizard
npx @claude-flow/cli@latest agent spawn -t coder --name my-coder
npx @claude-flow/cli@latest swarm init --v3-mode
npx @claude-flow/cli@latest memory search --query "authentication patterns"
npx @claude-flow/cli@latest doctor --fix
Available Agents (60+ Types)
Core Development
coder, reviewer, tester, planner, researcher
Specialized
security-architect, security-auditor, memory-specialist, performance-engineer
Swarm Coordination
hierarchical-coordinator, mesh-coordinator, adaptive-coordinator
GitHub & Repository
pr-manager, code-review-swarm, issue-tracker, release-manager
SPARC Methodology
sparc-coord, sparc-coder, specification, pseudocode, architecture
Memory Commands Reference
# Store (REQUIRED: --key, --value; OPTIONAL: --namespace, --ttl, --tags)
npx @claude-flow/cli@latest memory store --key "pattern-auth" --value "JWT with refresh" --namespace patterns
# Search (REQUIRED: --query; OPTIONAL: --namespace, --limit, --threshold)
npx @claude-flow/cli@latest memory search --query "authentication patterns"
# List (OPTIONAL: --namespace, --limit)
npx @claude-flow/cli@latest memory list --namespace patterns --limit 10
# Retrieve (REQUIRED: --key; OPTIONAL: --namespace)
npx @claude-flow/cli@latest memory retrieve --key "pattern-auth" --namespace patterns
Quick Setup
claude mcp add claude-flow -- npx -y @claude-flow/cli@latest
npx @claude-flow/cli@latest daemon start
npx @claude-flow/cli@latest doctor --fix
Claude Code vs CLI Tools
- Claude Code's Task tool handles ALL execution: agents, file ops, code generation, git
- CLI tools handle coordination via Bash: swarm init, memory, hooks, routing
- NEVER use CLI tools as a substitute for Task tool agents
Support
- Documentation: https://github.com/ruvnet/claude-flow
- Issues: https://github.com/ruvnet/claude-flow/issues