Merge pull request #1044 from ruvnet/feat/edge-skills-synthetic-validation

feat(wasm-edge): unified EdgePipeline (all ~64 skills) + honest synthetic validation harness

benchmarks/edge-skills/RESULTS.md

@@ -0,0 +1,132 @@
+# Edge-Skill Synthetic-Ground-Truth Validation — RESULTS
+
+**Crate:** `v2/crates/wifi-densepose-wasm-edge` (workspace-EXCLUDED — build from its own dir)
+**Branch:** `feat/edge-skills-synthetic-validation`
+**ADR:** [ADR-160](../../docs/adr/ADR-160-edge-skill-library-honest-labeling.md)
+**Date:** 2026-06-13
+**Harness:** `tests/synthetic_validation.rs`
+
+> **HONESTY BOUNDARY — read first.** Everything below is **synthetic-ground-truth
+> validation**: a signal is *planted* with a known answer, the **real** detector
+> is run, and detection accuracy / precision / recall / rate-error is **measured**.
+> This is **NOT field accuracy.** A skill that recovers a planted sinusoid here is
+> proven to do the math it claims on a *constructed* signal; it is **NOT** proven
+> to work on real CSI in a real room. Skills whose detection target cannot be
+> honestly planted (clinical, weapon, affect, sleep-stage, sign-language) are
+> **NOT** given a number — they are listed under **DATA-GATED** with the real
+> data each would require.
+
+## Reproduce
+
+```bash
+cd v2/crates/wifi-densepose-wasm-edge   # workspace-excluded; build here
+cargo test --features std --test synthetic_validation -- --nocapture
+# also runs under the medical tier (med_* skills stay DATA-GATED, not validated):
+cargo test --features std,medical-experimental --test synthetic_validation -- --nocapture
+```
+
+Each `MEASURED-on-synthetic | …` line printed by the harness is the source of the
+table below. Numbers are deterministic (no RNG; pseudo-noise uses a fixed LCG seed).
+
+---
+
+## MEASURED-on-synthetic (constructible skills)
+
+| Skill | What was planted (ground truth) | Result | Grade |
+|-------|----------------------------------|--------|-------|
+| **vital_trend** | BPM held N≥6 calls at each threshold band (brady/tachy-pnea <12 / >25, brady/tachy-cardia <50 / >120, apnea breathing<1.0 for ≥20) vs normal | **acc 1.000, prec 1.000, recall 1.000** (TP5 FP0 TN5 FN0) | MEASURED |
+| **exo_time_crystal** | period-2 coordinated motion vs pseudo-noise + flat | **acc 1.000** (TP1 FP0 TN2 FN0) | MEASURED † |
+| **exo_ghost_hunter** (hidden breathing) | phase sinusoid at lag-8 (breathing band 5–15) in an empty room vs flat phase | **acc 1.000**; planted score **1.000**, flat **0.000** | MEASURED |
+| **occupancy** | 220-frame flat-amplitude calibration, then strong per-zone amplitude variance vs flat | **acc 1.000** (TP1 FP0 TN1 FN0) | MEASURED |
+| **intrusion** | calibrate→arm (330 quiet frames), then per-subcarrier Δphase>1.5 + Δamp≫3σ vs quiet | **acc 1.000** (TP1 FP0 TN1 FN0) | MEASURED |
+| **exo_rain_detect** | empty room, 60-frame baseline, then broadband variance (8/8 groups, ratio≫2.5) for ≥10 frames vs stable-low | **acc 1.000** (TP1 FP0 TN1 FN0) | MEASURED |
+| **sig_flash_attention** | sustained high phase+amplitude in each of the 8 subcarrier groups; assert reported attention peak == planted group | **peak-localization 8/8 = 1.000** | MEASURED |
+| **spt_spiking_tracker** | sparse (2-subcarrier) large phase-delta in each of the 4 zones; assert tracked zone == planted zone | **zone-localization 4/4 = 1.000** | MEASURED ‡ |
+| **sig_optimal_transport** | sustained large frame-to-frame amplitude-distribution change vs stationary | **acc 1.000** (TP1 FP0 TN1 FN0) | MEASURED |
+| **sig_mincut_person_match** | 2 persons with distinct stable per-region variance signatures over 40 frames | **person ids assigned, 0 id-swaps / 40 frames** | MEASURED |
+| **lrn_dtw_gesture_learn** | stillness → 3 identical gesture rehearsals → enrollment | **template enrolled (templates=1)** | MEASURED (enroll) §|
+| **sig_sparse_recovery** | 30 clean frames to init, then 8/32 (25%) nulled subcarriers | **dropout-detect + recovery-trigger = PASS** | MEASURED (trigger) ¶|
+
+### Caveats on individual results
+
+† **exo_time_crystal — honest discriminative limit.** A *pure* periodic signal
+already has autocorrelation peaks at lag L **and** 2L (natural harmonics), so this
+"period-doubling" detector cannot separate a true period-2 sub-harmonic from a
+plain periodic signal — an earlier plant using a clean sine produced a *false
+positive* (recorded during development). The construct it **can** discriminate
+with known ground truth is **periodic-coordination vs aperiodic** (noise/flat),
+which is what is measured (1.000). The original "sub-harmonic vs clean period"
+claim is **NOT** validatable with this algorithm.
+
+‡ **spt_spiking_tracker — plant must be sparse.** With weights init'd home=1.0 /
+cross=0.25, firing all 8 inputs in a zone (8×0.25=2.0 > threshold 1.0) overdrives
+*every* output neuron and the tracker collapses to zone 0 (measured 1/4 during
+development). Firing only 2 inputs (home 2.0 fires, cross 0.5 silent) yields clean
+4/4 zone localization. The validatable claim is *single-zone* localization.
+
+§ **lrn_dtw_gesture_learn — enrollment validated; replay-match NOT.** The
+deterministic, constructible part (stillness → 3 identical rehearsals → a template
+is enrolled) is MEASURED. The DTW *replay match* (731) did **not** fire on the
+identical replay in this run (`match_same=false`) — replay-recognition accuracy is
+**reported, not asserted**, and is not claimed as validated.
+
+¶ **sig_sparse_recovery — trigger validated; recovery accuracy is NEGATIVE.**
+The dropout-detection + ISTA-recovery *trigger* pipeline fires correctly on >10%
+planted nulls (asserted). But the **measured recovery accuracy is NOT a win**:
+recovered RMSE **1.0045** vs unrecovered-null RMSE **0.9830** (**−2.2%**, i.e.
+slightly *worse* than leaving the nulls at zero) on a neighbor-correlated signal.
+The tridiagonal correlation model's fixed point does not equal the planted truth.
+**The recovery's reconstruction quality is therefore NOT validated as effective on
+synthetic data** — only its detection/trigger path is. Reported honestly; no
+positive number claimed.
+
+---
+
+## DATA-GATED — NOT validatable on synthetic data
+
+Planting a "seizure-like" / "weapon-like" / "happy-like" synthetic signal and
+claiming the detector "works" validates **nothing real** and is exactly the
+AI-slop this project fights. These skills run real DSP (per ADR-160, 0 stubs) and
+keep their ADR-160 disclaimers, but get **no accuracy number** here. Each needs
+the specific real, labelled data listed:
+
+| Skill | Why not constructible on synthetic | Real data required |
+|-------|------------------------------------|--------------------|
+| `med_seizure_detect` | "seizure-like" motion is not a seizure; no ground-truth signature exists synthetically | Clinical EEG-/video-labelled tonic-clonic seizure CSI from instrumented patients |
+| `med_sleep_apnea` | a planted breathing-pause is not clinical apnea (AHI scoring, hypopnea, desaturation) | Polysomnography-labelled (PSG) overnight CSI with scored apnea/hypopnea events |
+| `med_cardiac_arrhythmia` | a synthetic HR sequence cannot encode true arrhythmia morphology | ECG-labelled CSI (AFib/PVC/etc.) from clinical monitoring |
+| `med_respiratory_distress` | distress is a clinical gestalt, not a plantable rate | Clinician-labelled respiratory-distress CSI episodes |
+| `med_gait_analysis` | clinical gait metrics need a reference motion-capture standard | Mocap-/force-plate-labelled gait CSI |
+| `sec_weapon_detect` | a high variance ratio is RF reflectivity, **not** weapon discrimination (ADR-160 §A3 already renamed the event to `HIGH_METAL_REFLECTIVITY`) | Labelled metal-object-vs-no-object CSI with controlled object classes |
+| `exo_emotion_detect` | affect is not recoverable from a planted heuristic; outputs are proxies (ADR-160 §A2) | Validated affect-labelled CSI (self-report / physiological ground truth) |
+| `exo_happiness_score` | "happiness" is a gait-energy proxy, not a measured affect (ADR-160 §A2) | Validated affect/valence-labelled CSI |
+| `exo_dream_stage` | sleep staging needs PSG reference (EEG/EOG/EMG) | PSG-staged overnight CSI |
+| `exo_gesture_language` | coarse gesture clusters ≠ true sign language (ADR-160 §A4) | Labelled ASL letter/word CSI dataset |
+
+> The above are **not failures** — they are the honest boundary. A smaller set of
+> genuinely-measured skills plus this explicit gated list is the deliverable, per
+> the prove-everything directive.
+
+---
+
+## Skills not in either list
+
+The remaining edge skills (smart-building / retail / industrial occupancy-style,
+the other `sig_*`/`lrn_*`/`spt_*`/`tmp_*`/`qnt_*`/`aut_*`/`ais_*` algorithm-named
+modules) are **wired and exercised live** in the unified pipeline integration test
+(`tests/pipeline_all.rs`, all 59 default / 64 medical skills run without panic over
+300 synthetic frames) but were **not** given an individual planted-ground-truth
+accuracy number here. They are honest REAL-DSP modules (ADR-160) whose physical
+observable could be planted with more harness work; that is deferred, not claimed.
+
+## Test counts (full crate suite)
+
+```
+DEFAULT  (--features std):                     631 passed, 0 failed
+  (lib 504; budget 25; honest_labeling 10; pipeline_all 4; synthetic_validation 12; bench 1; vendor 75)
+MEDICAL  (--features std,medical-experimental): 669 passed, 0 failed
+  (lib 542; +16 same new tests; med_* stay DATA-GATED, not validated)
+```
+
+(M6 baseline was 615 / 653; the new pipeline_all (4) + synthetic_validation (12)
+tests add 16 to each tier.)

docs/adr/ADR-160-edge-skill-library-honest-labeling.md

@@ -178,10 +178,33 @@ label or behavior change, consistent with leaving their claim surface intact.)
 
 ## Deferred Backlog (Nothing Dropped)
 
-- **Per-skill accuracy validation** — **DATA-GATED**. Validating any med_*/affect/
-  sign-language claim requires labelled clinical/affective/ASL data and reference
-  standards that do not exist in this repo. The disclaimers + feature gate are the
-  honest stand-in. Nothing is claimed that is not measured.
+- **Per-skill accuracy validation** — **PARTIALLY MEASURED-on-synthetic**
+  (2026-06-13). For the subset of skills whose detection target is *constructible*
+  with known ground truth, a synthetic-ground-truth harness
+  (`tests/synthetic_validation.rs`, 12 tests) plants signals with known answers,
+  runs the real detector, and **measures** detection accuracy / rate-error:
+  `vital_trend`, `exo_time_crystal` (periodic-vs-aperiodic — its sub-harmonic-vs-
+  clean-period claim is NOT separable, recorded honestly), `exo_ghost_hunter`
+  (hidden breathing), `occupancy`, `intrusion`, `exo_rain_detect`,
+  `sig_flash_attention` (8/8 peak localization), `spt_spiking_tracker` (4/4 zone
+  localization, sparse plant), `sig_optimal_transport`, `sig_mincut_person_match`
+  (0 id-swaps), `lrn_dtw_gesture_learn` (enrollment) — all 1.000 where claimed;
+  `sig_sparse_recovery`'s recovery accuracy is reported **negative** (−2.2% vs
+  unrecovered baseline) — only its trigger path is validated. Full numbers +
+  reproduce commands in `benchmarks/edge-skills/RESULTS.md`.
+  The **med_*/affect/sign-language/weapon** claims remain **DATA-GATED**:
+  validating them requires labelled clinical/affective/ASL/metal-object data and
+  reference standards that do not exist in this repo. Planting a "seizure-/weapon-/
+  happy-like" synthetic signal validates nothing real and is explicitly refused;
+  RESULTS.md lists each with the real data it needs. The disclaimers + feature gate
+  are the honest stand-in. Nothing is claimed that is not measured.
+- **Unified edge pipeline** — **MEASURED** (2026-06-13). `src/pipeline_all.rs`
+  (`EdgePipeline`) + `src/skill_registry.rs` register **every** runtime skill
+  behind one uniform `EdgeSkill` trait and run them all per CSI frame; `med_*` are
+  registered only under `--features medical-experimental` (preserves the §A1 gate).
+  `tests/pipeline_all.rs` (4 tests) proves all 59 default / 64 medical skills run
+  without panic over 300 synthetic frames with a well-formed aggregated event
+  stream. `examples/run_all_skills.rs` is a runnable demo. No skill DSP changed.
 - **Criterion benches for `process_frame` budget claims** — **DONE (host)**
   (ADR-163, 2026-06-12). `benches/process_frame_bench.rs` benches the heaviest
   hot paths (`exo_time_crystal` 256×128 autocorrelation, `exo_ghost_hunter`

v2/crates/wifi-densepose-wasm-edge/examples/run_all_skills.rs

@@ -0,0 +1,108 @@
+//! Runnable demo of the unified [`EdgePipeline`]: constructs every registered
+//! skill, feeds a short deterministic synthetic CSI frame sequence, and prints
+//! the per-skill events plus a registration summary.
+//!
+//! ```bash
+//! cd v2/crates/wifi-densepose-wasm-edge
+//! cargo run --example run_all_skills --features std
+//! cargo run --example run_all_skills --features std,medical-experimental
+//! ```
+//!
+//! [`EdgePipeline`]: wifi_densepose_wasm_edge::pipeline_all::EdgePipeline
+
+#[cfg(not(feature = "std"))]
+fn main() {
+    eprintln!("run_all_skills requires --features std");
+}
+
+#[cfg(feature = "std")]
+fn main() {
+    use std::collections::BTreeMap;
+    use wifi_densepose_wasm_edge::pipeline_all::{CsiFrameView, EdgePipeline};
+
+    const N_SC: usize = 32;
+    let mut pipeline = EdgePipeline::new();
+
+    println!("=== EdgePipeline registration ===");
+    println!("registered skills: {}", pipeline.skill_count());
+    let med = pipeline
+        .skills()
+        .iter()
+        .filter(|s| s.medical_experimental)
+        .count();
+    println!(
+        "  default tier: {}   medical-experimental tier: {}",
+        pipeline.skill_count() - med,
+        med
+    );
+    println!();
+
+    let mut phases = [0.0f32; N_SC];
+    let mut amps = [0.0f32; N_SC];
+    let mut vars = [0.0f32; N_SC];
+    let mut prev = [0.0f32; N_SC];
+
+    // Per-skill event counters over the run.
+    let mut counts: BTreeMap<&'static str, usize> = BTreeMap::new();
+    for s in pipeline.skills() {
+        counts.insert(s.name, 0);
+    }
+
+    let frames = 300usize;
+    for t in 0..frames {
+        let tf = t as f32;
+        let breath = (tf * 2.0 * std::f32::consts::PI * 0.3 / 20.0).sin();
+        let heart = (tf * 2.0 * std::f32::consts::PI * 1.2 / 20.0).sin();
+        let mut vmean = 0.0f32;
+        for i in 0..N_SC {
+            let sc = i as f32;
+            phases[i] = (sc * 0.21 + tf * 0.05).sin() + 0.15 * breath;
+            amps[i] = 1.0 + 0.3 * (sc * 0.11 + tf * 0.03).cos() + 0.1 * heart;
+            vars[i] = 0.02 + 0.01 * (sc * 0.3).sin().abs()
+                + if (t / 40) % 2 == 0 { 0.05 } else { 0.0 };
+            vmean += vars[i];
+        }
+        vmean /= N_SC as f32;
+
+        let v = CsiFrameView {
+            phases: &phases,
+            amplitudes: &amps,
+            variances: &vars,
+            prev_phases: &prev,
+            presence: if (t / 30) % 3 == 0 { 0 } else { 1 },
+            n_persons: ((t / 50) % 3) as i32,
+            motion_energy: 0.3 + 0.2 * (tf * 0.07).sin().abs(),
+            breathing_bpm: 18.0 + 2.0 * (tf * 0.01).sin(),
+            heartrate_bpm: 72.0 + 5.0 * (tf * 0.02).sin(),
+            coherence: 0.5 + 0.4 * (tf * 0.03).cos(),
+            variance_mean: vmean,
+        };
+
+        for e in pipeline.on_frame(&v) {
+            *counts.entry(e.skill).or_insert(0) += 1;
+            // Print the first few events from the last frame to show liveness.
+            if t == frames - 1 {
+                println!(
+                    "  frame {} | {:<26} event {:>3} = {:.4}",
+                    t, e.skill, e.event_id, e.value
+                );
+            }
+        }
+        prev.copy_from_slice(&phases);
+    }
+
+    println!();
+    println!("=== per-skill event totals over {} synthetic frames ===", frames);
+    let total: usize = counts.values().sum();
+    let active = counts.values().filter(|&&c| c > 0).count();
+    for (name, c) in &counts {
+        println!("  {:<28} {}", name, c);
+    }
+    println!();
+    println!(
+        "TOTAL events: {}   skills that emitted at least once: {}/{}",
+        total,
+        active,
+        pipeline.skill_count()
+    );
+}

v2/crates/wifi-densepose-wasm-edge/src/lib.rs

@@ -94,6 +94,18 @@ pub mod ind_structural_vibration;
 
 pub mod vendor_common;
 
+// ── Unified edge pipeline (ADR-160 deliverable) ──────────────────────────────
+//
+// `EdgePipeline` registers EVERY runtime skill module behind one uniform
+// `EdgeSkill` trait and runs them all per CSI frame. Host-only (`std`): it uses
+// Box/Vec for dynamic dispatch; the wasm `no_std` build keeps the small flagship
+// pipeline in this file. The `med_*` tier is registered only under
+// `medical-experimental` (preserves the ADR-160 safety gate).
+#[cfg(feature = "std")]
+pub mod pipeline_all;
+#[cfg(feature = "std")]
+pub mod skill_registry;
+
 // ── Vendor-integrated modules (ADR-041 Category 7) ──────────────────────────
 //
 // 24 modules organised into 7 sub-categories.  Each module file lives in

v2/crates/wifi-densepose-wasm-edge/src/pipeline_all.rs

@@ -0,0 +1,217 @@
+//! Unified edge pipeline — registers **every** runtime skill module in the crate
+//! behind one uniform [`EdgeSkill`] trait and runs them all per CSI frame.
+//!
+//! # Why this module exists
+//!
+//! Each skill in `src/*.rs` is an independently-loadable DSP module with its own
+//! bespoke `process_frame` / `on_timer` signature (some take `&[f32]` phases,
+//! some scalars like `motion_energy`, some `breathing_bpm`/`heartrate_bpm`, etc.).
+//! On the wasm target only the flagship `gesture + coherence + adversarial`
+//! pipeline (in `lib.rs`) is on the default `on_frame` path. This module wires
+//! **all** of them into a single [`EdgePipeline`] so a host can run the whole
+//! skill library over one CSI frame stream and collect every emitted event,
+//! tagged by its source skill.
+//!
+//! # Design
+//!
+//! - [`CsiFrameView`] — a borrowed, host-supplied view of one CSI frame carrying
+//!   every input any skill needs (phase/amplitude/variance slices + the scalar
+//!   features the host derives: presence, n_persons, motion_energy, breathing &
+//!   heart rate, coherence, plus the previous frame's phases for delta skills).
+//! - [`EdgeSkill`] — the uniform adapter trait. Each skill gets a small adapter
+//!   (see `skill_registry`) that pulls the fields it needs out of the view, calls
+//!   the underlying detector **unchanged**, and returns an aggregated
+//!   `&[(i32, f32)]` event buffer. **No skill DSP is modified.**
+//! - [`EdgePipeline`] — owns one boxed adapter per skill, dispatches `on_frame`
+//!   to all of them, and aggregates `(skill_name, event_id, value)` triples.
+//!
+//! # Feature gating (preserves the ADR-160 safety gate)
+//!
+//! The five `med_*` skills are registered **only** under
+//! `--features medical-experimental`. They are NOT pulled into the default
+//! pipeline, so they cannot be silently built into a shipping artifact. The
+//! medical tier is opt-in; see `EdgePipeline::new` and `skills()`.
+//!
+//! Requires `std` (uses `Box`/`Vec`); the wasm `no_std` build keeps the small
+//! flagship `lib.rs` pipeline instead.
+
+#![cfg(feature = "std")]
+
+extern crate std;
+use std::boxed::Box;
+use std::vec::Vec;
+
+/// Borrowed view of one CSI frame: every input any registered skill can consume.
+///
+/// The host derives these from the Tier-2 DSP output. Slices are
+/// per-subcarrier; scalars are frame-level aggregates. A skill adapter reads
+/// only the fields it needs and ignores the rest — heterogeneity is absorbed
+/// here, not in the skills.
+#[derive(Clone, Copy)]
+pub struct CsiFrameView<'a> {
+    /// Per-subcarrier unwrapped phase (radians).
+    pub phases: &'a [f32],
+    /// Per-subcarrier amplitude (linear).
+    pub amplitudes: &'a [f32],
+    /// Per-subcarrier short-window variance.
+    pub variances: &'a [f32],
+    /// Previous frame's phases (for delta/velocity skills like the spiking tracker).
+    pub prev_phases: &'a [f32],
+    /// Presence flag from host (0 = empty, 1 = occupied).
+    pub presence: i32,
+    /// Estimated person count from host.
+    pub n_persons: i32,
+    /// Frame-level motion energy.
+    pub motion_energy: f32,
+    /// Breathing rate estimate (breaths/min); 0 if unavailable.
+    pub breathing_bpm: f32,
+    /// Heart rate estimate (beats/min); 0 if unavailable.
+    pub heartrate_bpm: f32,
+    /// Coherence score [0,1] from the coherence monitor (for gate-style skills).
+    pub coherence: f32,
+    /// Mean variance across `variances` (convenience scalar for skills wanting one).
+    pub variance_mean: f32,
+}
+
+impl<'a> CsiFrameView<'a> {
+    /// Mean amplitude across the frame (convenience for scalar-input skills).
+    #[inline]
+    pub fn amplitude_mean(&self) -> f32 {
+        if self.amplitudes.is_empty() {
+            return 0.0;
+        }
+        let mut s = 0.0f32;
+        for &a in self.amplitudes {
+            s += a;
+        }
+        s / self.amplitudes.len() as f32
+    }
+
+    /// Mean phase across the frame.
+    #[inline]
+    pub fn phase_mean(&self) -> f32 {
+        if self.phases.is_empty() {
+            return 0.0;
+        }
+        let mut s = 0.0f32;
+        for &p in self.phases {
+            s += p;
+        }
+        s / self.phases.len() as f32
+    }
+}
+
+/// One emitted event, tagged by its source skill.
+#[derive(Clone, Copy, Debug, PartialEq)]
+pub struct SkillEvent {
+    /// Stable name of the skill that produced this event (e.g. `"occupancy"`).
+    pub skill: &'static str,
+    /// Event type id (the registry id from `event_types`).
+    pub event_id: i32,
+    /// Event payload value.
+    pub value: f32,
+}
+
+/// Uniform adapter trait over a heterogeneous skill detector.
+///
+/// Implementors live in `skill_registry`; each wraps exactly one underlying
+/// detector and forwards `on_frame` to its real `process_frame`/`on_timer`
+/// without changing the DSP. `event_ids()` is introspection only.
+pub trait EdgeSkill {
+    /// Stable skill name (matches the `src/<name>.rs` module).
+    fn name(&self) -> &'static str;
+    /// The event ids this skill can emit (for introspection / docs).
+    fn event_ids(&self) -> &'static [i32];
+    /// Run this skill over one frame, returning its emitted `(event_id, value)`
+    /// pairs. Returns an empty slice if the skill emitted nothing this frame.
+    fn on_frame(&mut self, frame: &CsiFrameView) -> &[(i32, f32)];
+}
+
+/// Introspection record for one registered skill.
+#[derive(Clone, Copy, Debug)]
+pub struct SkillInfo {
+    /// Skill name.
+    pub name: &'static str,
+    /// Event ids the skill can emit.
+    pub event_ids: &'static [i32],
+    /// Whether the skill is part of the gated `medical-experimental` tier.
+    pub medical_experimental: bool,
+}
+
+/// The unified pipeline: holds one adapter per registered skill and runs them
+/// all per frame.
+pub struct EdgePipeline {
+    skills: Vec<Box<dyn EdgeSkill>>,
+    /// Parallel flag marking which entries are the gated medical tier.
+    medical_flags: Vec<bool>,
+    frame_count: u64,
+}
+
+impl EdgePipeline {
+    /// Construct the pipeline with **every** registered skill.
+    ///
+    /// The five `med_*` skills are included **only** when the crate is built
+    /// with `--features medical-experimental`; otherwise the default
+    /// (non-medical) tier is registered. This preserves the ADR-160 safety gate.
+    pub fn new() -> Self {
+        let mut skills: Vec<Box<dyn EdgeSkill>> = Vec::new();
+        let mut medical_flags: Vec<bool> = Vec::new();
+
+        crate::skill_registry::register_default(&mut skills, &mut medical_flags);
+        #[cfg(feature = "medical-experimental")]
+        crate::skill_registry::register_medical(&mut skills, &mut medical_flags);
+
+        Self {
+            skills,
+            medical_flags,
+            frame_count: 0,
+        }
+    }
+
+    /// Number of registered skills (default tier, or +medical if that feature is on).
+    pub fn skill_count(&self) -> usize {
+        self.skills.len()
+    }
+
+    /// Run every registered skill over one frame, aggregating all emitted events
+    /// tagged by source skill. Order matches registration order.
+    pub fn on_frame(&mut self, frame: &CsiFrameView) -> Vec<SkillEvent> {
+        self.frame_count += 1;
+        let mut out: Vec<SkillEvent> = Vec::new();
+        for skill in self.skills.iter_mut() {
+            let name = skill.name();
+            for &(event_id, value) in skill.on_frame(frame) {
+                out.push(SkillEvent {
+                    skill: name,
+                    event_id,
+                    value,
+                });
+            }
+        }
+        out
+    }
+
+    /// Total frames processed so far.
+    pub fn frame_count(&self) -> u64 {
+        self.frame_count
+    }
+
+    /// Introspection: list every registered skill with its event ids and tier.
+    pub fn skills(&self) -> Vec<SkillInfo> {
+        let mut out = Vec::with_capacity(self.skills.len());
+        for (i, skill) in self.skills.iter().enumerate() {
+            out.push(SkillInfo {
+                name: skill.name(),
+                event_ids: skill.event_ids(),
+                medical_experimental: self.medical_flags.get(i).copied().unwrap_or(false),
+            });
+        }
+        out
+    }
+}
+
+impl Default for EdgePipeline {
+    fn default() -> Self {
+        Self::new()
+    }
+}

v2/crates/wifi-densepose-wasm-edge/src/skill_registry.rs

@@ -0,0 +1,630 @@
+//! Adapters wiring every runtime skill detector to the uniform [`EdgeSkill`]
+//! trait, plus the registration functions consumed by [`EdgePipeline::new`].
+//!
+//! [`EdgePipeline::new`]: crate::pipeline_all::EdgePipeline::new
+//! [`EdgeSkill`]: crate::pipeline_all::EdgeSkill
+//!
+//! # How adapters work
+//!
+//! Each underlying detector keeps its own bespoke `process_frame`/`on_timer`
+//! signature and its owned `events: [(i32,f32); N]` buffer (the ADR-160 M6
+//! soundness fix). An adapter holds the detector, implements [`EdgeSkill`], and
+//! in `on_frame` simply pulls the needed fields out of [`CsiFrameView`] and
+//! forwards the call **unchanged**. The detector returns `&self.events[..n]`;
+//! the adapter forwards that borrow directly, so no extra buffer or copy is
+//! needed for the common case.
+//!
+//! Three families need a small owned scratch buffer in the adapter instead of a
+//! direct forward, because the underlying entry point does not itself return a
+//! `&[(i32,f32)]`:
+//! - `gesture` (`-> Option<u8>`), `coherence` (`-> f32`), `adversarial`
+//!   (`-> bool`): the adapter synthesizes a single tagged event.
+//! - `sig_sparse_recovery` (`process_frame(&mut [f32])`): the adapter copies the
+//!   frame amplitudes into an owned scratch slice so the in-place ISTA recovery
+//!   never mutates the shared frame, then forwards the borrow.
+//! - timer-driven skills (`vital_trend`, `lrn_meta_adapt`, `sig_temporal_compress`,
+//!   `tmp_goap_autonomy`, `tmp_pattern_sequence`): their `on_timer()` is driven
+//!   once per frame here (a frame *is* the tick at the edge), forwarding the
+//!   borrow. `tmp_pattern_sequence` additionally calls its `on_frame(...)`
+//!   accumulator first.
+//!
+//! **No skill's DSP is changed.** Only the call wiring lives here.
+
+#![cfg(feature = "std")]
+
+extern crate std;
+use std::boxed::Box;
+use std::vec::Vec;
+
+use crate::pipeline_all::{CsiFrameView, EdgeSkill};
+
+// ── Direct-forward adapter macro ─────────────────────────────────────────────
+//
+// Generates an adapter whose `on_frame` forwards directly to a detector method
+// that already returns `&[(i32, f32)]`. `$call` is an expression over `self.0`
+// (the detector) and `f` (the `&CsiFrameView`).
+macro_rules! fwd_skill {
+    ($adapter:ident, $detector:path, $name:literal, $ids:expr, |$d:ident, $f:ident| $call:expr) => {
+        pub struct $adapter($detector);
+        impl $adapter {
+            pub fn new() -> Self {
+                Self(<$detector>::new())
+            }
+        }
+        impl EdgeSkill for $adapter {
+            fn name(&self) -> &'static str {
+                $name
+            }
+            fn event_ids(&self) -> &'static [i32] {
+                &$ids
+            }
+            fn on_frame(&mut self, $f: &CsiFrameView) -> &[(i32, f32)] {
+                let $d = &mut self.0;
+                $call
+            }
+        }
+    };
+}
+
+// ── Synthesized-event adapter macro ──────────────────────────────────────────
+//
+// For detectors whose entry point does NOT return `&[(i32, f32)]`. The adapter
+// owns a tiny scratch buffer; `$body` (over `self`, `f`, and `self.buf`/`self.n`)
+// fills it and the trait returns the filled prefix.
+macro_rules! synth_skill {
+    ($adapter:ident, $detector:path, $name:literal, $ids:expr, $buf:literal,
+     |$s:ident, $f:ident| $body:block) => {
+        pub struct $adapter {
+            det: $detector,
+            buf: [(i32, f32); $buf],
+            n: usize,
+        }
+        impl $adapter {
+            pub fn new() -> Self {
+                Self {
+                    det: <$detector>::new(),
+                    buf: [(0, 0.0); $buf],
+                    n: 0,
+                }
+            }
+        }
+        impl EdgeSkill for $adapter {
+            fn name(&self) -> &'static str {
+                $name
+            }
+            fn event_ids(&self) -> &'static [i32] {
+                &$ids
+            }
+            fn on_frame(&mut self, $f: &CsiFrameView) -> &[(i32, f32)] {
+                let $s = self;
+                $s.n = 0;
+                $body
+                &$s.buf[..$s.n]
+            }
+        }
+    };
+}
+
+use crate::event_types as ev;
+
+// ── Flagship (synthesized) ───────────────────────────────────────────────────
+
+synth_skill!(GestureAdapter, crate::gesture::GestureDetector, "gesture",
+    [ev::GESTURE_DETECTED], 1, |s, f| {
+        if let Some(id) = s.det.process_frame(f.phases) {
+            s.buf[0] = (ev::GESTURE_DETECTED, id as f32);
+            s.n = 1;
+        }
+    });
+
+synth_skill!(CoherenceAdapter, crate::coherence::CoherenceMonitor, "coherence",
+    [ev::COHERENCE_SCORE], 1, |s, f| {
+        let score = s.det.process_frame(f.phases);
+        s.buf[0] = (ev::COHERENCE_SCORE, score);
+        s.n = 1;
+    });
+
+synth_skill!(AdversarialAdapter, crate::adversarial::AnomalyDetector, "adversarial",
+    [ev::ANOMALY_DETECTED], 1, |s, f| {
+        if s.det.process_frame(f.phases, f.amplitudes) {
+            s.buf[0] = (ev::ANOMALY_DETECTED, 1.0);
+            s.n = 1;
+        }
+    });
+
+// ── sig_sparse_recovery (needs owned mutable amplitude scratch) ───────────────
+
+const SPARSE_SC: usize = 64;
+pub struct SparseRecoveryAdapter {
+    det: crate::sig_sparse_recovery::SparseRecovery,
+    scratch: [f32; SPARSE_SC],
+}
+impl SparseRecoveryAdapter {
+    pub fn new() -> Self {
+        Self {
+            det: crate::sig_sparse_recovery::SparseRecovery::new(),
+            scratch: [0.0; SPARSE_SC],
+        }
+    }
+}
+impl EdgeSkill for SparseRecoveryAdapter {
+    fn name(&self) -> &'static str {
+        "sig_sparse_recovery"
+    }
+    fn event_ids(&self) -> &'static [i32] {
+        &[ev::RECOVERY_COMPLETE, ev::RECOVERY_ERROR, ev::DROPOUT_RATE]
+    }
+    fn on_frame(&mut self, f: &CsiFrameView) -> &[(i32, f32)] {
+        let n = f.amplitudes.len().min(SPARSE_SC);
+        self.scratch[..n].copy_from_slice(&f.amplitudes[..n]);
+        self.det.process_frame(&mut self.scratch[..n])
+    }
+}
+
+// ── Standard direct-forward skills (return &[(i32,f32)]) ─────────────────────
+
+fwd_skill!(AisBehavioralAdapter, crate::ais_behavioral_profiler::BehavioralProfiler,
+    "ais_behavioral_profiler",
+    [ev::BEHAVIOR_ANOMALY, ev::PROFILE_DEVIATION, ev::NOVEL_PATTERN, ev::PROFILE_MATURITY],
+    |d, f| d.process_frame(f.presence != 0, f.motion_energy, f.n_persons.max(0) as u8));
+
+fwd_skill!(AisPromptShieldAdapter, crate::ais_prompt_shield::PromptShield,
+    "ais_prompt_shield",
+    [ev::REPLAY_ATTACK, ev::INJECTION_DETECTED, ev::JAMMING_DETECTED, ev::SIGNAL_INTEGRITY],
+    |d, f| d.process_frame(f.phases, f.amplitudes));
+
+fwd_skill!(AutPsychoAdapter, crate::aut_psycho_symbolic::PsychoSymbolicEngine,
+    "aut_psycho_symbolic",
+    [ev::INFERENCE_RESULT, ev::INFERENCE_CONFIDENCE, ev::RULE_FIRED, ev::CONTRADICTION],
+    |d, f| d.process_frame(f.presence as f32, f.motion_energy, f.breathing_bpm,
+        f.heartrate_bpm, f.n_persons as f32, 0.0));
+
+fwd_skill!(AutMeshAdapter, crate::aut_self_healing_mesh::SelfHealingMesh,
+    "aut_self_healing_mesh",
+    [ev::NODE_DEGRADED, ev::MESH_RECONFIGURE, ev::COVERAGE_SCORE, ev::HEALING_COMPLETE],
+    |d, f| d.process_frame(f.variances));
+
+fwd_skill!(BldElevatorAdapter, crate::bld_elevator_count::ElevatorCounter,
+    "bld_elevator_count",
+    [ev::ELEVATOR_COUNT, ev::DOOR_OPEN, ev::DOOR_CLOSE, ev::OVERLOAD_WARNING],
+    |d, f| d.process_frame(f.amplitudes, f.phases, f.motion_energy, f.n_persons));
+
+fwd_skill!(BldEnergyAdapter, crate::bld_energy_audit::EnergyAuditor,
+    "bld_energy_audit",
+    [ev::SCHEDULE_SUMMARY, ev::AFTER_HOURS_ALERT, ev::UTILIZATION_RATE],
+    |d, f| d.process_frame(f.presence, f.n_persons));
+
+fwd_skill!(BldHvacAdapter, crate::bld_hvac_presence::HvacPresenceDetector,
+    "bld_hvac_presence",
+    [ev::HVAC_OCCUPIED, ev::ACTIVITY_LEVEL, ev::DEPARTURE_COUNTDOWN],
+    |d, f| d.process_frame(f.presence as f32, f.motion_energy));
+
+fwd_skill!(BldLightingAdapter, crate::bld_lighting_zones::LightingZoneController,
+    "bld_lighting_zones",
+    [ev::LIGHT_ON, ev::LIGHT_DIM, ev::LIGHT_OFF],
+    |d, f| d.process_frame(f.amplitudes, f.motion_energy));
+
+fwd_skill!(BldMeetingAdapter, crate::bld_meeting_room::MeetingRoomTracker,
+    "bld_meeting_room",
+    [ev::MEETING_START, ev::MEETING_END, ev::PEAK_HEADCOUNT, ev::ROOM_AVAILABLE],
+    |d, f| d.process_frame(f.presence, f.n_persons, f.motion_energy));
+
+fwd_skill!(ExoBreathingSyncAdapter, crate::exo_breathing_sync::BreathingSyncDetector,
+    "exo_breathing_sync",
+    [ev::SYNC_DETECTED, ev::SYNC_PAIR_COUNT, ev::GROUP_COHERENCE, ev::SYNC_LOST],
+    |d, f| d.process_frame(f.phases, f.variances, f.breathing_bpm, f.n_persons));
+
+fwd_skill!(ExoEmotionAdapter, crate::exo_emotion_detect::EmotionDetector,
+    "exo_emotion_detect",
+    [ev::AROUSAL_LEVEL, ev::STRESS_INDEX, ev::CALM_DETECTED, ev::AGITATION_DETECTED],
+    |d, f| d.process_frame(f.breathing_bpm, f.heartrate_bpm, f.motion_energy,
+        f.phase_mean(), f.variance_mean));
+
+fwd_skill!(ExoDreamAdapter, crate::exo_dream_stage::DreamStageDetector,
+    "exo_dream_stage",
+    [ev::SLEEP_STAGE, ev::SLEEP_QUALITY, ev::REM_EPISODE, ev::DEEP_SLEEP_RATIO],
+    |d, f| d.process_frame(f.breathing_bpm, f.heartrate_bpm, f.motion_energy,
+        f.phase_mean(), f.variance_mean, f.presence));
+
+fwd_skill!(ExoGestureLangAdapter, crate::exo_gesture_language::GestureLanguageDetector,
+    "exo_gesture_language",
+    [ev::LETTER_RECOGNIZED, ev::LETTER_CONFIDENCE, ev::WORD_BOUNDARY, ev::GESTURE_REJECTED],
+    |d, f| d.process_frame(f.phases, f.amplitudes, f.variance_mean, f.motion_energy, f.presence));
+
+fwd_skill!(ExoGhostAdapter, crate::exo_ghost_hunter::GhostHunterDetector,
+    "exo_ghost_hunter",
+    [ev::EXO_ANOMALY_DETECTED, ev::EXO_ANOMALY_CLASS, ev::HIDDEN_PRESENCE, ev::ENVIRONMENTAL_DRIFT],
+    |d, f| d.process_frame(f.phases, f.amplitudes, f.variances, f.presence, f.motion_energy));
+
+fwd_skill!(ExoHappinessAdapter, crate::exo_happiness_score::HappinessScoreDetector,
+    "exo_happiness_score",
+    [ev::HAPPINESS_SCORE, ev::GAIT_ENERGY, ev::AFFECT_VALENCE, ev::SOCIAL_ENERGY, ev::TRANSIT_DIRECTION],
+    |d, f| d.process_frame(f.phases, f.amplitudes, f.variances, f.presence,
+        f.motion_energy, f.breathing_bpm, f.heartrate_bpm));
+
+fwd_skill!(ExoHyperbolicAdapter, crate::exo_hyperbolic_space::HyperbolicEmbedder,
+    "exo_hyperbolic_space",
+    [ev::HIERARCHY_LEVEL, ev::HYPERBOLIC_RADIUS, ev::LOCATION_LABEL],
+    |d, f| d.process_frame(f.amplitudes));
+
+fwd_skill!(ExoMusicAdapter, crate::exo_music_conductor::MusicConductorDetector,
+    "exo_music_conductor",
+    [ev::CONDUCTOR_BPM, ev::BEAT_POSITION, ev::DYNAMIC_LEVEL, ev::GESTURE_CUTOFF, ev::GESTURE_FERMATA],
+    |d, f| d.process_frame(f.phase_mean(), f.amplitude_mean(), f.motion_energy, f.variance_mean));
+
+fwd_skill!(ExoPlantAdapter, crate::exo_plant_growth::PlantGrowthDetector,
+    "exo_plant_growth",
+    [ev::GROWTH_RATE, ev::CIRCADIAN_PHASE, ev::WILT_DETECTED, ev::WATERING_EVENT],
+    |d, f| d.process_frame(f.amplitudes, f.phases, f.variances, f.presence));
+
+fwd_skill!(ExoRainAdapter, crate::exo_rain_detect::RainDetector,
+    "exo_rain_detect",
+    [ev::RAIN_ONSET, ev::RAIN_INTENSITY, ev::RAIN_CESSATION],
+    |d, f| d.process_frame(f.phases, f.variances, f.amplitudes, f.presence));
+
+fwd_skill!(ExoTimeCrystalAdapter, crate::exo_time_crystal::TimeCrystalDetector,
+    "exo_time_crystal",
+    [ev::CRYSTAL_DETECTED, ev::CRYSTAL_STABILITY, ev::COORDINATION_INDEX],
+    |d, f| d.process_frame(f.motion_energy));
+
+fwd_skill!(IndCleanRoomAdapter, crate::ind_clean_room::CleanRoomMonitor,
+    "ind_clean_room",
+    [ev::OCCUPANCY_COUNT, ev::OCCUPANCY_VIOLATION, ev::TURBULENT_MOTION, ev::COMPLIANCE_REPORT],
+    |d, f| d.process_frame(f.n_persons, f.presence, f.motion_energy));
+
+fwd_skill!(IndConfinedAdapter, crate::ind_confined_space::ConfinedSpaceMonitor,
+    "ind_confined_space",
+    [ev::WORKER_ENTRY, ev::WORKER_EXIT, ev::BREATHING_OK, ev::EXTRACTION_ALERT, ev::IMMOBILE_ALERT],
+    |d, f| d.process_frame(f.presence, f.breathing_bpm, f.motion_energy, f.variance_mean));
+
+fwd_skill!(IndForkliftAdapter, crate::ind_forklift_proximity::ForkliftProximityDetector,
+    "ind_forklift_proximity",
+    [ev::PROXIMITY_WARNING, ev::VEHICLE_DETECTED, ev::HUMAN_NEAR_VEHICLE],
+    |d, f| d.process_frame(f.phases, f.amplitudes, f.variances, f.motion_energy, f.presence, f.n_persons));
+
+fwd_skill!(IndLivestockAdapter, crate::ind_livestock_monitor::LivestockMonitor,
+    "ind_livestock_monitor",
+    [ev::ANIMAL_PRESENT, ev::ABNORMAL_STILLNESS, ev::LABORED_BREATHING, ev::ESCAPE_ALERT],
+    |d, f| d.process_frame(f.presence, f.breathing_bpm, f.motion_energy, f.variance_mean));
+
+fwd_skill!(IndVibrationAdapter, crate::ind_structural_vibration::StructuralVibrationMonitor,
+    "ind_structural_vibration",
+    [ev::SEISMIC_DETECTED, ev::MECHANICAL_RESONANCE, ev::STRUCTURAL_DRIFT, ev::VIBRATION_SPECTRUM],
+    |d, f| d.process_frame(f.phases, f.amplitudes, f.variances, f.presence));
+
+fwd_skill!(IntrusionAdapter, crate::intrusion::IntrusionDetector,
+    "intrusion",
+    [ev::INTRUSION_ALERT, ev::INTRUSION_ZONE, 202],
+    |d, f| d.process_frame(f.phases, f.amplitudes));
+
+fwd_skill!(LrnAttractorAdapter, crate::lrn_anomaly_attractor::AttractorDetector,
+    "lrn_anomaly_attractor",
+    [ev::ATTRACTOR_TYPE, ev::LYAPUNOV_EXPONENT, ev::BASIN_DEPARTURE, ev::LEARNING_COMPLETE],
+    |d, f| d.process_frame(f.phases, f.amplitudes, f.motion_energy));
+
+fwd_skill!(LrnDtwAdapter, crate::lrn_dtw_gesture_learn::GestureLearner,
+    "lrn_dtw_gesture_learn",
+    [ev::GESTURE_LEARNED, ev::GESTURE_MATCHED, ev::LRN_MATCH_DISTANCE, ev::TEMPLATE_COUNT],
+    |d, f| d.process_frame(f.phases, f.motion_energy));
+
+fwd_skill!(LrnEwcAdapter, crate::lrn_ewc_lifelong::EwcLifelong,
+    "lrn_ewc_lifelong",
+    [ev::KNOWLEDGE_RETAINED, ev::NEW_TASK_LEARNED, ev::FISHER_UPDATE, ev::FORGETTING_RISK],
+    |d, f| d.process_frame(f.variances, f.presence));
+
+fwd_skill!(OccupancyAdapter, crate::occupancy::OccupancyDetector,
+    "occupancy",
+    [ev::ZONE_OCCUPIED, ev::ZONE_COUNT, ev::ZONE_TRANSITION],
+    |d, f| d.process_frame(f.phases, f.amplitudes));
+
+fwd_skill!(QntInterferenceAdapter, crate::qnt_interference_search::InterferenceSearch,
+    "qnt_interference_search",
+    [ev::HYPOTHESIS_WINNER, ev::HYPOTHESIS_AMPLITUDE, ev::SEARCH_ITERATIONS],
+    |d, f| d.process_frame(f.presence, f.motion_energy, f.n_persons));
+
+fwd_skill!(QntCoherenceAdapter, crate::qnt_quantum_coherence::QuantumCoherenceMonitor,
+    "qnt_quantum_coherence",
+    [ev::ENTANGLEMENT_ENTROPY, ev::DECOHERENCE_EVENT, ev::BLOCH_DRIFT],
+    |d, f| d.process_frame(f.phases));
+
+fwd_skill!(RetFlowAdapter, crate::ret_customer_flow::CustomerFlowTracker,
+    "ret_customer_flow",
+    [ev::INGRESS, ev::EGRESS, ev::NET_OCCUPANCY, ev::HOURLY_TRAFFIC],
+    |d, f| d.process_frame(f.phases, f.amplitudes, f.variance_mean, f.motion_energy));
+
+fwd_skill!(RetDwellAdapter, crate::ret_dwell_heatmap::DwellHeatmapTracker,
+    "ret_dwell_heatmap",
+    [ev::DWELL_ZONE_UPDATE, ev::HOT_ZONE, ev::COLD_ZONE, ev::SESSION_SUMMARY],
+    |d, f| d.process_frame(f.presence, f.variances, f.motion_energy, f.n_persons));
+
+fwd_skill!(RetQueueAdapter, crate::ret_queue_length::QueueLengthEstimator,
+    "ret_queue_length",
+    [ev::QUEUE_LENGTH, ev::WAIT_TIME_ESTIMATE, ev::SERVICE_RATE, ev::QUEUE_ALERT],
+    |d, f| d.process_frame(f.presence, f.n_persons, f.variance_mean, f.motion_energy));
+
+fwd_skill!(RetShelfAdapter, crate::ret_shelf_engagement::ShelfEngagementDetector,
+    "ret_shelf_engagement",
+    [ev::SHELF_BROWSE, ev::SHELF_CONSIDER, ev::SHELF_ENGAGE, ev::REACH_DETECTED],
+    |d, f| d.process_frame(f.presence, f.motion_energy, f.variance_mean, f.phases));
+
+fwd_skill!(RetTableAdapter, crate::ret_table_turnover::TableTurnoverTracker,
+    "ret_table_turnover",
+    [ev::TABLE_SEATED, ev::TABLE_VACATED, ev::TABLE_AVAILABLE, ev::TURNOVER_RATE],
+    |d, f| d.process_frame(f.presence, f.motion_energy, f.n_persons));
+
+fwd_skill!(SecLoiteringAdapter, crate::sec_loitering::LoiteringDetector,
+    "sec_loitering",
+    [ev::LOITERING_START, ev::LOITERING_ONGOING, ev::LOITERING_END],
+    |d, f| d.process_frame(f.presence, f.motion_energy));
+
+fwd_skill!(SecPanicAdapter, crate::sec_panic_motion::PanicMotionDetector,
+    "sec_panic_motion",
+    [ev::PANIC_DETECTED, ev::STRUGGLE_PATTERN, ev::FLEEING_DETECTED],
+    |d, f| d.process_frame(f.motion_energy, f.variance_mean, f.phase_mean(), f.presence));
+
+fwd_skill!(SecPerimeterAdapter, crate::sec_perimeter_breach::PerimeterBreachDetector,
+    "sec_perimeter_breach",
+    [ev::PERIMETER_BREACH, ev::APPROACH_DETECTED, ev::DEPARTURE_DETECTED, ev::SEC_ZONE_TRANSITION],
+    |d, f| d.process_frame(f.phases, f.amplitudes, f.variances, f.motion_energy));
+
+fwd_skill!(SecTailgateAdapter, crate::sec_tailgating::TailgateDetector,
+    "sec_tailgating",
+    [ev::TAILGATE_DETECTED, ev::SINGLE_PASSAGE, ev::MULTI_PASSAGE],
+    |d, f| d.process_frame(f.motion_energy, f.presence, f.n_persons, f.variance_mean));
+
+fwd_skill!(SecWeaponAdapter, crate::sec_weapon_detect::WeaponDetector,
+    "sec_weapon_detect",
+    [ev::METAL_ANOMALY, ev::HIGH_METAL_REFLECTIVITY, ev::CALIBRATION_NEEDED],
+    |d, f| d.process_frame(f.phases, f.amplitudes, f.variances, f.motion_energy, f.presence));
+
+fwd_skill!(SigCoherenceGateAdapter, crate::sig_coherence_gate::CoherenceGate,
+    "sig_coherence_gate",
+    [ev::GATE_DECISION, ev::SIG_COHERENCE_SCORE, ev::RECALIBRATE_NEEDED],
+    |d, f| d.process_frame(f.phases));
+
+fwd_skill!(SigFlashAttnAdapter, crate::sig_flash_attention::FlashAttention,
+    "sig_flash_attention",
+    [ev::ATTENTION_PEAK_SC, ev::ATTENTION_SPREAD, ev::SPATIAL_FOCUS_ZONE],
+    |d, f| d.process_frame(f.phases, f.amplitudes));
+
+fwd_skill!(SigMincutAdapter, crate::sig_mincut_person_match::PersonMatcher,
+    "sig_mincut_person_match",
+    [ev::PERSON_ID_ASSIGNED, ev::PERSON_ID_SWAP, ev::MATCH_CONFIDENCE],
+    |d, f| d.process_frame(f.amplitudes, f.variances, f.n_persons.max(0) as usize));
+
+fwd_skill!(SigTransportAdapter, crate::sig_optimal_transport::OptimalTransportDetector,
+    "sig_optimal_transport",
+    [ev::WASSERSTEIN_DISTANCE, ev::DISTRIBUTION_SHIFT, ev::SUBTLE_MOTION],
+    |d, f| d.process_frame(f.amplitudes));
+
+fwd_skill!(SptHnswAdapter, crate::spt_micro_hnsw::MicroHnsw,
+    "spt_micro_hnsw",
+    [ev::NEAREST_MATCH_ID, ev::HNSW_MATCH_DISTANCE, ev::CLASSIFICATION, ev::LIBRARY_SIZE],
+    |d, f| d.process_frame(f.variances));
+
+fwd_skill!(SptPagerankAdapter, crate::spt_pagerank_influence::PageRankInfluence,
+    "spt_pagerank_influence",
+    [ev::DOMINANT_PERSON, ev::INFLUENCE_SCORE, ev::INFLUENCE_CHANGE],
+    |d, f| d.process_frame(f.phases, f.n_persons.max(0) as usize));
+
+fwd_skill!(SptSpikingAdapter, crate::spt_spiking_tracker::SpikingTracker,
+    "spt_spiking_tracker",
+    [ev::TRACK_UPDATE, ev::TRACK_VELOCITY, ev::SPIKE_RATE, ev::TRACK_LOST],
+    |d, f| d.process_frame(f.phases, f.prev_phases));
+
+fwd_skill!(TmpLogicGuardAdapter, crate::tmp_temporal_logic_guard::TemporalLogicGuard,
+    "tmp_temporal_logic_guard",
+    [ev::LTL_VIOLATION, ev::LTL_SATISFACTION, ev::COUNTEREXAMPLE],
+    |d, f| {
+        let input = crate::tmp_temporal_logic_guard::FrameInput {
+            presence: f.presence,
+            n_persons: f.n_persons,
+            motion_energy: f.motion_energy,
+            coherence: f.coherence,
+            breathing_bpm: f.breathing_bpm,
+            heartrate_bpm: f.heartrate_bpm,
+            fall_alert: false,
+            intrusion_alert: false,
+            person_id_active: f.n_persons > 0,
+            vital_signs_active: f.breathing_bpm > 0.0,
+            seizure_detected: false,
+            normal_gait: true,
+        };
+        d.on_frame(&input)
+    });
+
+// ── Timer-driven skills (driven once per frame) ──────────────────────────────
+
+fwd_skill!(VitalTrendAdapter, crate::vital_trend::VitalTrendAnalyzer,
+    "vital_trend",
+    // 101-105 = brady/tachypnea, brady/tachycardia, apnea; 110/111 = breathing/heartrate
+    // moving averages (module-local EVENT_BREATHING_AVG / EVENT_HEARTRATE_AVG).
+    [ev::BRADYPNEA, ev::TACHYPNEA, ev::BRADYCARDIA, ev::TACHYCARDIA, ev::APNEA, 110, 111],
+    |d, f| d.on_timer(f.breathing_bpm, f.heartrate_bpm));
+
+fwd_skill!(LrnMetaAdapter, crate::lrn_meta_adapt::MetaAdapter,
+    "lrn_meta_adapt",
+    [ev::PARAM_ADJUSTED, ev::ADAPTATION_SCORE, ev::ROLLBACK_TRIGGERED, ev::META_LEVEL],
+    |d, _f| d.on_timer());
+
+fwd_skill!(SigTemporalCompressAdapter, crate::sig_temporal_compress::TemporalCompressor,
+    "sig_temporal_compress",
+    [ev::COMPRESSION_RATIO, ev::TIER_TRANSITION, ev::HISTORY_DEPTH_HOURS],
+    |d, _f| d.on_timer());
+
+fwd_skill!(TmpGoapAdapter, crate::tmp_goap_autonomy::GoapPlanner,
+    "tmp_goap_autonomy",
+    [ev::GOAL_SELECTED, ev::MODULE_ACTIVATED, ev::MODULE_DEACTIVATED, ev::PLAN_COST],
+    |d, _f| d.on_timer());
+
+// tmp_pattern_sequence: accumulate via on_frame, then drive on_timer per frame.
+pub struct TmpPatternAdapter(crate::tmp_pattern_sequence::PatternSequenceAnalyzer);
+impl TmpPatternAdapter {
+    pub fn new() -> Self {
+        Self(crate::tmp_pattern_sequence::PatternSequenceAnalyzer::new())
+    }
+}
+impl EdgeSkill for TmpPatternAdapter {
+    fn name(&self) -> &'static str {
+        "tmp_pattern_sequence"
+    }
+    fn event_ids(&self) -> &'static [i32] {
+        &[ev::PATTERN_DETECTED, ev::PATTERN_CONFIDENCE, ev::ROUTINE_DEVIATION, ev::PREDICTION_NEXT]
+    }
+    fn on_frame(&mut self, f: &CsiFrameView) -> &[(i32, f32)] {
+        self.0.on_frame(f.presence, f.motion_energy, f.n_persons);
+        self.0.on_timer()
+    }
+}
+
+// ── Medical tier (gated) ─────────────────────────────────────────────────────
+
+#[cfg(feature = "medical-experimental")]
+mod medical {
+    use super::*;
+
+    // Medical event ids verified against each module's local consts (100-199 block).
+    fwd_skill!(MedCardiacAdapter, crate::med_cardiac_arrhythmia::CardiacArrhythmiaDetector,
+        "med_cardiac_arrhythmia",
+        [110, 111, 112, 113],
+        |d, f| d.process_frame(f.heartrate_bpm, f.phase_mean()));
+
+    fwd_skill!(MedGaitAdapter, crate::med_gait_analysis::GaitAnalyzer,
+        "med_gait_analysis",
+        [130, 131, 132, 133, 134],
+        |d, f| d.process_frame(f.phase_mean(), f.amplitude_mean(), f.variance_mean, f.motion_energy));
+
+    fwd_skill!(MedRespiratoryAdapter, crate::med_respiratory_distress::RespiratoryDistressDetector,
+        "med_respiratory_distress",
+        [120, 121, 122, 123],
+        |d, f| d.process_frame(f.breathing_bpm, f.phase_mean(), f.variance_mean));
+
+    fwd_skill!(MedSeizureAdapter, crate::med_seizure_detect::SeizureDetector,
+        "med_seizure_detect",
+        [140, 141, 142, 143],
+        |d, f| d.process_frame(f.phase_mean(), f.amplitude_mean(), f.motion_energy, f.presence));
+
+    fwd_skill!(MedApneaAdapter, crate::med_sleep_apnea::SleepApneaDetector,
+        "med_sleep_apnea",
+        [100, 101, 102],
+        |d, f| d.process_frame(f.breathing_bpm, f.presence, f.variance_mean));
+
+    pub fn register(skills: &mut Vec<Box<dyn EdgeSkill>>, med: &mut Vec<bool>) {
+        macro_rules! push {
+            ($a:ty) => {{
+                skills.push(Box::new(<$a>::new()));
+                med.push(true);
+            }};
+        }
+        push!(MedSeizureAdapter);
+        push!(MedCardiacAdapter);
+        push!(MedRespiratoryAdapter);
+        push!(MedApneaAdapter);
+        push!(MedGaitAdapter);
+    }
+}
+
+// ── Registration ─────────────────────────────────────────────────────────────
+
+/// Register every default-tier (non-medical) skill.
+pub fn register_default(skills: &mut Vec<Box<dyn EdgeSkill>>, med: &mut Vec<bool>) {
+    macro_rules! push {
+        ($a:ty) => {{
+            skills.push(Box::new(<$a>::new()));
+            med.push(false);
+        }};
+    }
+
+    // Flagship + synthesized
+    push!(GestureAdapter);
+    push!(CoherenceAdapter);
+    push!(AdversarialAdapter);
+    push!(OccupancyAdapter);
+    push!(IntrusionAdapter);
+    push!(VitalTrendAdapter);
+
+    // Security
+    push!(SecPerimeterAdapter);
+    push!(SecWeaponAdapter);
+    push!(SecTailgateAdapter);
+    push!(SecLoiteringAdapter);
+    push!(SecPanicAdapter);
+
+    // Smart building
+    push!(BldHvacAdapter);
+    push!(BldLightingAdapter);
+    push!(BldElevatorAdapter);
+    push!(BldMeetingAdapter);
+    push!(BldEnergyAdapter);
+
+    // Retail
+    push!(RetQueueAdapter);
+    push!(RetDwellAdapter);
+    push!(RetFlowAdapter);
+    push!(RetTableAdapter);
+    push!(RetShelfAdapter);
+
+    // Industrial
+    push!(IndForkliftAdapter);
+    push!(IndConfinedAdapter);
+    push!(IndCleanRoomAdapter);
+    push!(IndLivestockAdapter);
+    push!(IndVibrationAdapter);
+
+    // Exotic / research
+    push!(ExoTimeCrystalAdapter);
+    push!(ExoHyperbolicAdapter);
+    push!(ExoDreamAdapter);
+    push!(ExoEmotionAdapter);
+    push!(ExoGestureLangAdapter);
+    push!(ExoMusicAdapter);
+    push!(ExoPlantAdapter);
+    push!(ExoGhostAdapter);
+    push!(ExoRainAdapter);
+    push!(ExoBreathingSyncAdapter);
+    push!(ExoHappinessAdapter);
+
+    // Signal intelligence
+    push!(SigCoherenceGateAdapter);
+    push!(SigFlashAttnAdapter);
+    push!(SigTemporalCompressAdapter);
+    push!(SparseRecoveryAdapter);
+    push!(SigMincutAdapter);
+    push!(SigTransportAdapter);
+
+    // Adaptive learning
+    push!(LrnDtwAdapter);
+    push!(LrnAttractorAdapter);
+    push!(LrnMetaAdapter);
+    push!(LrnEwcAdapter);
+
+    // Spatial reasoning
+    push!(SptPagerankAdapter);
+    push!(SptHnswAdapter);
+    push!(SptSpikingAdapter);
+
+    // Temporal analysis
+    push!(TmpPatternAdapter);
+    push!(TmpLogicGuardAdapter);
+    push!(TmpGoapAdapter);
+
+    // AI security
+    push!(AisPromptShieldAdapter);
+    push!(AisBehavioralAdapter);
+
+    // Quantum-inspired
+    push!(QntCoherenceAdapter);
+    push!(QntInterferenceAdapter);
+
+    // Autonomous systems
+    push!(AutPsychoAdapter);
+    push!(AutMeshAdapter);
+
+    let _ = (skills.len(), med.len());
+}
+
+/// Register the gated `medical-experimental` tier (5 `med_*` skills).
+#[cfg(feature = "medical-experimental")]
+pub fn register_medical(skills: &mut Vec<Box<dyn EdgeSkill>>, med: &mut Vec<bool>) {
+    medical::register(skills, med);
+}

v2/crates/wifi-densepose-wasm-edge/tests/pipeline_all.rs

@@ -0,0 +1,208 @@
+//! Integration test for the unified [`EdgePipeline`] (ADR-160 deliverable 1).
+//!
+//! Proves that EVERY registered skill executes over a deterministic synthetic
+//! CSI frame sequence without panicking, that the aggregated event stream is
+//! well-formed (each event tagged with a known skill name + a declared event
+//! id), and pins the registered-skill count (default vs +medical-experimental).
+//!
+//! Run:
+//!   cargo test --features std --test pipeline_all
+//!   cargo test --features std,medical-experimental --test pipeline_all
+//!
+//! [`EdgePipeline`]: wifi_densepose_wasm_edge::pipeline_all::EdgePipeline
+
+#![cfg(feature = "std")]
+
+use wifi_densepose_wasm_edge::pipeline_all::{CsiFrameView, EdgePipeline};
+
+const N_SC: usize = 32;
+
+/// Deterministic synthetic frame: a moving breathing/heartbeat target plus
+/// structured per-subcarrier phase/amplitude. No randomness — fully reproducible.
+fn synth_frame(t: usize, phases: &mut [f32], amps: &mut [f32], vars: &mut [f32]) {
+    let tf = t as f32;
+    // 0.3 Hz breathing modulation @ 20 Hz frame rate -> period ~66 frames.
+    let breath = (tf * 2.0 * core::f32::consts::PI * 0.3 / 20.0).sin();
+    // 1.2 Hz heartbeat.
+    let heart = (tf * 2.0 * core::f32::consts::PI * 1.2 / 20.0).sin();
+    for i in 0..phases.len() {
+        let sc = i as f32;
+        phases[i] = (sc * 0.21 + tf * 0.05).sin() + 0.15 * breath;
+        amps[i] = 1.0 + 0.3 * (sc * 0.11 + tf * 0.03).cos() + 0.1 * heart;
+        // motion-correlated variance, with one occasionally-hot zone.
+        vars[i] = 0.02 + 0.01 * (sc * 0.3).sin().abs() + if (t / 40) % 2 == 0 { 0.05 } else { 0.0 };
+    }
+}
+
+/// Build a view over the supplied buffers for frame `t`.
+fn view<'a>(
+    t: usize,
+    phases: &'a [f32],
+    amps: &'a [f32],
+    vars: &'a [f32],
+    prev_phases: &'a [f32],
+) -> CsiFrameView<'a> {
+    let tf = t as f32;
+    let motion = 0.3 + 0.2 * (tf * 0.07).sin().abs();
+    let mut vmean = 0.0f32;
+    for &v in vars {
+        vmean += v;
+    }
+    vmean /= vars.len().max(1) as f32;
+    CsiFrameView {
+        phases,
+        amplitudes: amps,
+        variances: vars,
+        prev_phases,
+        presence: if (t / 30) % 3 == 0 { 0 } else { 1 },
+        n_persons: ((t / 50) % 3) as i32,
+        motion_energy: motion,
+        breathing_bpm: 18.0 + 2.0 * (tf * 0.01).sin(),
+        heartrate_bpm: 72.0 + 5.0 * (tf * 0.02).sin(),
+        coherence: 0.5 + 0.4 * (tf * 0.03).cos(),
+        variance_mean: vmean,
+    }
+}
+
+#[test]
+fn all_skills_execute_without_panic_over_synthetic_stream() {
+    let mut pipeline = EdgePipeline::new();
+    let n_skills = pipeline.skill_count();
+    assert!(n_skills > 0, "pipeline must register skills");
+
+    let mut phases = [0.0f32; N_SC];
+    let mut amps = [0.0f32; N_SC];
+    let mut vars = [0.0f32; N_SC];
+    let mut prev_phases = [0.0f32; N_SC];
+
+    let known: std::collections::HashSet<&'static str> =
+        pipeline.skills().iter().map(|s| s.name).collect();
+
+    // Feed 300 frames (15 s @ 20 Hz) — enough for calibration windows, DTW
+    // enrollment, periodicity buffers, and timer cadences to fire.
+    let mut total_events = 0usize;
+    for t in 0..300 {
+        synth_frame(t, &mut phases, &mut amps, &mut vars);
+        let v = view(t, &phases, &amps, &vars, &prev_phases);
+        let events = pipeline.on_frame(&v);
+        for e in &events {
+            // Every event must be tagged with a registered skill name.
+            assert!(known.contains(e.skill), "unknown skill tag: {}", e.skill);
+            // Value must be finite (no NaN/Inf leaking from the DSP).
+            assert!(e.value.is_finite(), "non-finite value from {}", e.skill);
+        }
+        total_events += events.len();
+        prev_phases.copy_from_slice(&phases);
+    }
+
+    assert_eq!(pipeline.frame_count(), 300);
+    // A real run over 300 frames must emit *some* events across 59+ skills.
+    assert!(
+        total_events > 0,
+        "expected the skill library to emit events over 300 frames, got 0"
+    );
+    println!(
+        "pipeline: {} skills, {} aggregated events over 300 synthetic frames",
+        n_skills, total_events
+    );
+}
+
+#[test]
+fn every_emitted_event_id_is_declared_by_its_skill() {
+    // Stronger well-formedness: each event's id must be one the producing skill
+    // declared in its `event_ids()` introspection list.
+    let mut pipeline = EdgePipeline::new();
+
+    // skill name -> its declared event id set
+    let mut declared: std::collections::HashMap<&'static str, std::collections::HashSet<i32>> =
+        std::collections::HashMap::new();
+    for s in pipeline.skills() {
+        declared.insert(s.name, s.event_ids.iter().copied().collect());
+    }
+
+    let mut phases = [0.0f32; N_SC];
+    let mut amps = [0.0f32; N_SC];
+    let mut vars = [0.0f32; N_SC];
+    let mut prev_phases = [0.0f32; N_SC];
+
+    for t in 0..300 {
+        synth_frame(t, &mut phases, &mut amps, &mut vars);
+        let v = view(t, &phases, &amps, &vars, &prev_phases);
+        for e in &pipeline.on_frame(&v) {
+            let set = declared.get(e.skill).expect("skill declared");
+            assert!(
+                set.contains(&e.event_id),
+                "{} emitted undeclared event id {}",
+                e.skill,
+                e.event_id
+            );
+        }
+        prev_phases.copy_from_slice(&phases);
+    }
+}
+
+#[test]
+fn introspection_lists_every_skill_with_event_ids() {
+    let pipeline = EdgePipeline::new();
+    let infos = pipeline.skills();
+    assert_eq!(infos.len(), pipeline.skill_count());
+    for info in &infos {
+        assert!(!info.name.is_empty());
+        assert!(
+            !info.event_ids.is_empty(),
+            "skill {} declares no event ids",
+            info.name
+        );
+    }
+    // No duplicate skill names.
+    let names: std::collections::HashSet<_> = infos.iter().map(|i| i.name).collect();
+    assert_eq!(names.len(), infos.len(), "duplicate skill registration");
+}
+
+#[cfg(not(feature = "medical-experimental"))]
+#[test]
+fn default_tier_count_excludes_medical() {
+    let pipeline = EdgePipeline::new();
+    assert_eq!(
+        pipeline.skill_count(),
+        59,
+        "default (non-medical) tier must register exactly 59 skills"
+    );
+    // The ADR-160 safety gate: no med_* skill is present in the default build.
+    for info in pipeline.skills() {
+        assert!(
+            !info.medical_experimental,
+            "medical skill {} leaked into default tier",
+            info.name
+        );
+        assert!(
+            !info.name.starts_with("med_"),
+            "med_* skill {} present without the medical-experimental feature",
+            info.name
+        );
+    }
+}
+
+#[cfg(feature = "medical-experimental")]
+#[test]
+fn medical_tier_adds_five_skills() {
+    let pipeline = EdgePipeline::new();
+    assert_eq!(
+        pipeline.skill_count(),
+        64,
+        "default 59 + 5 medical = 64 skills"
+    );
+    let med: Vec<_> = pipeline
+        .skills()
+        .into_iter()
+        .filter(|s| s.medical_experimental)
+        .collect();
+    assert_eq!(med.len(), 5, "exactly 5 medical-experimental skills");
+    for m in &med {
+        assert!(
+            m.name.starts_with("med_"),
+            "medical-flagged skill has non-med_ name: {}",
+            m.name
+        );
+    }
+}

v2/crates/wifi-densepose-wasm-edge/tests/synthetic_validation.rs

@@ -0,0 +1,762 @@
+//! Synthetic-ground-truth validation harness (ADR-160 deliverable 2).
+//!
+//! For the subset of edge skills whose detection target can be PLANTED with
+//! known ground truth, we generate N signals with known answers, run the real
+//! detector, and MEASURE detection rate / precision / recall / rate-error.
+//!
+//! # Honesty boundary
+//!
+//! This is **synthetic-ground-truth validation, NOT field accuracy.** A skill
+//! that recovers a planted sinusoid here is proven to do the math it claims on
+//! a constructed signal; it is NOT proven to work on real CSI in a real room.
+//!
+//! Skills whose detection target cannot be honestly planted on synthetic data
+//! (clinical seizure/apnea/arrhythmia/gait, weapon discrimination, affect/
+//! emotion/happiness, dream stage, sign language) are **NOT** validated here —
+//! see RESULTS.md "DATA-GATED" section. Planting a "seizure-like" wiggle and
+//! claiming the detector works validates nothing real.
+//!
+//! Run:
+//!   cargo test --features std --test synthetic_validation -- --nocapture
+//!
+//! The printed `MEASURED` lines are the source of `benchmarks/edge-skills/RESULTS.md`.
+
+#![cfg(feature = "std")]
+
+use std::f32::consts::PI;
+
+// ── Confusion-matrix accumulator ─────────────────────────────────────────────
+
+#[derive(Default, Clone, Copy)]
+struct Confusion {
+    tp: u32,
+    fp: u32,
+    tn: u32,
+    fn_: u32,
+}
+impl Confusion {
+    fn observe(&mut self, predicted_positive: bool, actual_positive: bool) {
+        match (predicted_positive, actual_positive) {
+            (true, true) => self.tp += 1,
+            (true, false) => self.fp += 1,
+            (false, false) => self.tn += 1,
+            (false, true) => self.fn_ += 1,
+        }
+    }
+    fn precision(&self) -> f32 {
+        let d = self.tp + self.fp;
+        if d == 0 {
+            1.0
+        } else {
+            self.tp as f32 / d as f32
+        }
+    }
+    fn recall(&self) -> f32 {
+        let d = self.tp + self.fn_;
+        if d == 0 {
+            1.0
+        } else {
+            self.tp as f32 / d as f32
+        }
+    }
+    fn accuracy(&self) -> f32 {
+        let d = self.tp + self.fp + self.tn + self.fn_;
+        if d == 0 {
+            0.0
+        } else {
+            (self.tp + self.tn) as f32 / d as f32
+        }
+    }
+    fn report(&self, name: &str) {
+        println!(
+            "MEASURED-on-synthetic | {:<34} | acc={:.3} prec={:.3} recall={:.3} | TP={} FP={} TN={} FN={}",
+            name,
+            self.accuracy(),
+            self.precision(),
+            self.recall(),
+            self.tp,
+            self.fp,
+            self.tn,
+            self.fn_
+        );
+    }
+}
+
+// ── 1. vital_trend — rate-threshold detection (directly verified thresholds) ─
+// Thresholds (from src/vital_trend.rs): BRADYPNEA<12, TACHYPNEA>25,
+// BRADYCARDIA<50, TACHYCARDIA>120, APNEA at breathing<1.0 for 20 calls;
+// ALERT_DEBOUNCE=5. Drive on_timer with known BPM, count event presence.
+
+#[test]
+fn vital_trend_rate_thresholds() {
+    use wifi_densepose_wasm_edge::vital_trend::VitalTrendAnalyzer;
+
+    // event ids: 101 brady-pnea, 102 tachy-pnea, 103 brady-cardia, 104 tachy-cardia, 105 apnea
+    fn drive_breathing(bpm: f32, n: u32) -> std::collections::HashSet<i32> {
+        let mut det = VitalTrendAnalyzer::new();
+        let mut seen = std::collections::HashSet::new();
+        for _ in 0..n {
+            for &(id, _) in det.on_timer(bpm, 72.0) {
+                seen.insert(id);
+            }
+        }
+        seen
+    }
+    fn drive_heart(bpm: f32, n: u32) -> std::collections::HashSet<i32> {
+        let mut det = VitalTrendAnalyzer::new();
+        let mut seen = std::collections::HashSet::new();
+        for _ in 0..n {
+            for &(id, _) in det.on_timer(16.0, bpm) {
+                seen.insert(id);
+            }
+        }
+        seen
+    }
+
+    // 6 calls > ALERT_DEBOUNCE(5) so a sustained abnormal value fires.
+    let mut c = Confusion::default();
+    // Bradypnea: <12 positive; normal 16 negative.
+    c.observe(drive_breathing(8.0, 6).contains(&101), true);
+    c.observe(drive_breathing(16.0, 6).contains(&101), false);
+    // Tachypnea: >25 positive; normal negative.
+    c.observe(drive_breathing(30.0, 6).contains(&102), true);
+    c.observe(drive_breathing(16.0, 6).contains(&102), false);
+    // Bradycardia: <50.
+    c.observe(drive_heart(40.0, 6).contains(&103), true);
+    c.observe(drive_heart(72.0, 6).contains(&103), false);
+    // Tachycardia: >120.
+    c.observe(drive_heart(140.0, 6).contains(&104), true);
+    c.observe(drive_heart(72.0, 6).contains(&104), false);
+    // Apnea: breathing < 1.0 for >= 20 calls.
+    c.observe(drive_breathing(0.0, 20).contains(&105), true);
+    c.observe(drive_breathing(0.0, 10).contains(&105), false); // only 10 calls -> below APNEA_SECONDS
+
+    c.report("vital_trend (brady/tachy-pnea/cardia, apnea)");
+    // All 5 thresholds + their negatives must classify correctly.
+    assert_eq!(c.accuracy(), 1.0, "vital_trend rate thresholds must be exact");
+}
+
+// ── 2. exo_time_crystal — period-doubling (sub-harmonic) detection ───────────
+// Detects a peak at lag L AND a peak at lag 2L in motion-energy autocorrelation.
+// PLANT positive: period-2 modulation (alternating amplitude on a base period)
+//   so autocorr has peaks at both L and 2L.
+// PLANT negative: a single clean period (peak at L only) or noise.
+
+fn run_time_crystal(motion: &[f32]) -> bool {
+    use wifi_densepose_wasm_edge::exo_time_crystal::TimeCrystalDetector;
+    let mut det = TimeCrystalDetector::new();
+    let mut detected = false;
+    for &m in motion {
+        for &(id, v) in det.process_frame(m) {
+            if id == 680 && v >= 2.0 {
+                detected = true; // CRYSTAL_DETECTED with multiplier 2
+            }
+        }
+    }
+    detected
+}
+
+#[test]
+fn exo_time_crystal_period_doubling() {
+    let n = 256usize;
+    // Positive: period-2 subharmonic. Base period P=16; alternate full periods
+    // are scaled differently so the waveform only repeats every 2P=32 (peak at
+    // lag 32) while still correlating at P=16. Plain sine (no abs, which would
+    // itself fold frequency and fake a sub-harmonic).
+    let base_p = 16.0f32;
+    let mut pos = Vec::with_capacity(n);
+    for t in 0..n {
+        let phase = (t as f32) * 2.0 * PI / base_p;
+        let sub = if ((t as f32 / base_p) as i32) % 2 == 0 { 1.0 } else { 0.45 };
+        pos.push(0.6 + 0.35 * phase.sin() * sub);
+    }
+    // HONEST LIMIT (measured below): a *pure* periodic signal already has
+    // autocorrelation peaks at L AND 2L (natural harmonics), so this detector
+    // cannot separate a true period-2 sub-harmonic from a plain periodic signal.
+    // The construct it CAN discriminate with known ground truth is
+    // "periodic-with-coordination vs aperiodic". We validate that.
+    //
+    // Negative 1: incrementing-seed pseudo-noise (no periodicity).
+    let mut noise = Vec::with_capacity(n);
+    let mut s: u32 = 12345;
+    for _ in 0..n {
+        s = s.wrapping_mul(1664525).wrapping_add(1013904223);
+        noise.push(0.3 + 0.4 * ((s >> 8) & 0xffff) as f32 / 65535.0);
+    }
+    // Negative 2: near-constant motion (no oscillation at all).
+    let flat: Vec<f32> = (0..n).map(|t| 0.5 + 1e-4 * (t as f32 * 0.01).sin()).collect();
+
+    let mut c = Confusion::default();
+    c.observe(run_time_crystal(&pos), true); // planted period-2 -> detect
+    c.observe(run_time_crystal(&noise), false); // pseudo-noise -> reject
+    c.observe(run_time_crystal(&flat), false); // flat -> reject
+    c.report("exo_time_crystal (periodic-coordination vs aperiodic)");
+    assert!(
+        run_time_crystal(&pos),
+        "must detect planted period-2 coordinated motion"
+    );
+    assert!(
+        !run_time_crystal(&noise),
+        "must NOT fire on pseudo-noise"
+    );
+    assert!(!run_time_crystal(&flat), "must NOT fire on flat motion");
+}
+
+// ── 3. exo_ghost_hunter — hidden breathing (autocorr at breathing-range lag) ─
+// When presence==0, aggregate phase is autocorrelated at lags 5..=15; a peak
+// there above HIDDEN_PRESENCE_THRESHOLD(0.3) emits HIDDEN_PRESENCE(652).
+// PLANT positive: phase sinusoid at a lag in [5,15] across an empty room.
+// PLANT negative: flat phase (no periodic breathing signature).
+
+fn run_ghost_hidden_breathing(period: f32, amp: f32, frames: usize) -> f32 {
+    use wifi_densepose_wasm_edge::exo_ghost_hunter::GhostHunterDetector;
+    let mut det = GhostHunterDetector::new();
+    let n_sc = 32usize;
+    let mut max_hidden = 0.0f32;
+    for t in 0..frames {
+        let breath = if period > 0.0 {
+            amp * (t as f32 * 2.0 * PI / period).sin()
+        } else {
+            0.0
+        };
+        let mut phases = [0.0f32; 32];
+        let mut amps = [0.0f32; 32];
+        let mut vars = [0.0f32; 32];
+        for i in 0..n_sc {
+            // breathing modulates phase uniformly (chest motion -> common phase shift)
+            phases[i] = 0.1 * (i as f32 * 0.2).sin() + breath;
+            amps[i] = 1.0;
+            vars[i] = 0.01;
+        }
+        // presence = 0 (empty room) is required for the hidden-breathing path.
+        for &(id, v) in det.process_frame(&phases, &amps, &vars, 0, 0.0) {
+            if id == 652 {
+                if v > max_hidden {
+                    max_hidden = v;
+                }
+            }
+        }
+    }
+    max_hidden
+}
+
+#[test]
+fn exo_ghost_hunter_hidden_breathing() {
+    // Period 8 frames is within the breathing lag window [5,15].
+    let pos = run_ghost_hidden_breathing(8.0, 0.5, 200);
+    // Flat phase (no breathing) -> no hidden-presence event.
+    let neg = run_ghost_hidden_breathing(0.0, 0.0, 200);
+
+    let mut c = Confusion::default();
+    c.observe(pos > 0.0, true);
+    c.observe(neg > 0.0, false);
+    c.report("exo_ghost_hunter (hidden breathing, lag 8)");
+    println!(
+        "  detail: planted-breathing hidden-presence score={:.3}, flat-phase score={:.3}",
+        pos, neg
+    );
+    assert!(
+        pos > 0.3,
+        "planted breathing must score above HIDDEN_PRESENCE_THRESHOLD (0.3); got {}",
+        pos
+    );
+    assert!(
+        neg <= 0.0,
+        "flat phase must not emit hidden presence; got {}",
+        neg
+    );
+}
+
+// ── 4. occupancy — calibration + variance-driven zone occupancy ──────────────
+// BASELINE_FRAMES=200 of low-variance amplitudes establish baseline; then
+// high amplitude-variance per zone (score > ZONE_THRESHOLD=0.02) flips a zone
+// to occupied (EVENT_ZONE_OCCUPIED=300).
+
+#[test]
+fn occupancy_variance_detection() {
+    use wifi_densepose_wasm_edge::occupancy::OccupancyDetector;
+
+    fn run(occupied_signal: bool) -> bool {
+        let mut det = OccupancyDetector::new();
+        let n_sc = 32usize;
+        let mut phases = [0.0f32; 32];
+        // Calibration: 220 frames of near-flat amplitudes (low variance).
+        for t in 0..220 {
+            let mut amps = [1.0f32; 32];
+            for i in 0..n_sc {
+                amps[i] = 1.0 + 1e-3 * ((t + i) as f32 * 0.7).sin();
+                phases[i] = 0.01 * (i as f32).sin();
+            }
+            det.process_frame(&phases, &amps);
+        }
+        // Test phase: 60 frames. If occupied, inject strong per-zone amplitude
+        // variance; else keep flat.
+        let mut fired = false;
+        for t in 0..60 {
+            let mut amps = [1.0f32; 32];
+            for i in 0..n_sc {
+                amps[i] = if occupied_signal {
+                    // strong structured variance within each zone
+                    1.0 + 2.0 * (((i % 4) as f32) - 1.5) + 0.5 * (t as f32 * 0.3 + i as f32).sin()
+                } else {
+                    1.0 + 1e-3 * ((t + i) as f32 * 0.7).sin()
+                };
+            }
+            for &(id, _) in det.process_frame(&phases, &amps) {
+                if id == 300 {
+                    fired = true;
+                }
+            }
+        }
+        fired
+    }
+
+    let mut c = Confusion::default();
+    c.observe(run(true), true);
+    c.observe(run(false), false);
+    c.report("occupancy (zone variance vs flat baseline)");
+    assert!(run(true), "high zone variance after calibration must occupy a zone");
+    assert!(!run(false), "flat amplitude must stay unoccupied");
+}
+
+// ── 5. intrusion — calibrate, arm, then disturbance>=0.8 alerts ──────────────
+// disturbance = 0.6*frac(|Δphase|>1.5) + 0.4*frac(|Δamp|>3σ). Calibrate 200
+// quiet frames, monitor 100 quiet frames -> Armed, then 3 frames of large
+// phase+amp disturbance -> EVENT_INTRUSION_ALERT(200).
+
+#[test]
+fn intrusion_disturbance_alert() {
+    use wifi_densepose_wasm_edge::intrusion::IntrusionDetector;
+
+    fn run(intrude: bool) -> bool {
+        let mut det = IntrusionDetector::new();
+        let n_sc = 32usize;
+        // Calibration (200) + monitoring quiet (120) -> Armed. Quiet = constant.
+        for _ in 0..330 {
+            let phases = [0.5f32; 32];
+            let amps = [1.0f32; 32];
+            det.process_frame(&phases, &amps);
+        }
+        let mut alerted = false;
+        // 10 test frames.
+        for t in 0..10 {
+            let mut phases = [0.5f32; 32];
+            let mut amps = [1.0f32; 32];
+            if intrude {
+                for i in 0..n_sc {
+                    // alternate phase by 3.0 (>1.5) and amplitude far from baseline 1.0.
+                    phases[i] = if t % 2 == 0 { 0.5 } else { 4.0 };
+                    amps[i] = 1.0 + 8.0; // huge deviation vs ~0 baseline variance
+                }
+            }
+            for &(id, _) in det.process_frame(&phases, &amps) {
+                if id == 200 {
+                    alerted = true;
+                }
+            }
+        }
+        alerted
+    }
+
+    let mut c = Confusion::default();
+    c.observe(run(true), true);
+    c.observe(run(false), false);
+    c.report("intrusion (armed -> disturbance alert vs quiet)");
+    assert!(run(true), "large phase+amplitude disturbance must alert when armed");
+    assert!(!run(false), "quiet environment must not alert");
+}
+
+// ── 6. sig_sparse_recovery — ISTA recovery of planted null subcarriers ───────
+// Initialize correlation on clean frames, then null >10% of subcarriers and
+// MEASURE how well ISTA recovers them (rate-error style: recovery residual).
+
+#[test]
+fn sig_sparse_recovery_recovers_nulls() {
+    use wifi_densepose_wasm_edge::sig_sparse_recovery::SparseRecovery;
+
+    let mut det = SparseRecovery::new();
+    let n_sc = 32usize;
+    // Underlying smooth signal (neighbor-correlated) the model can learn.
+    let truth: Vec<f32> = (0..n_sc).map(|i| 1.0 + 0.5 * (i as f32 * 0.4).sin()).collect();
+
+    // Warm up correlation model with 30 clean frames.
+    for _ in 0..30 {
+        let mut amps: Vec<f32> = truth.clone();
+        det.process_frame(&mut amps);
+    }
+
+    // Null subcarriers 5..13 (8/32 = 25% > MIN_DROPOUT_RATE 0.10).
+    let mut amps: Vec<f32> = truth.clone();
+    let nulled: Vec<usize> = (5..13).collect();
+    for &i in &nulled {
+        amps[i] = 0.0;
+    }
+    // Baseline error if the nulls were left at 0.0 (unrecovered).
+    let mut sse0 = 0.0f32;
+    for &i in &nulled {
+        sse0 += truth[i] * truth[i];
+    }
+    let baseline_rmse = (sse0 / nulled.len() as f32).sqrt();
+
+    let mut recovery_seen = false;
+    for &(id, _) in det.process_frame(&mut amps) {
+        if id == 715 {
+            recovery_seen = true; // RECOVERY_COMPLETE
+        }
+    }
+    // Measure recovery error on the nulled positions (now written back in-place).
+    let mut sse = 0.0f32;
+    for &i in &nulled {
+        let d = amps[i] - truth[i];
+        sse += d * d;
+    }
+    let rmse = (sse / nulled.len() as f32).sqrt();
+    println!(
+        "MEASURED-on-synthetic | {:<34} | dropout-detect+recovery-trigger=PASS | recovered RMSE={:.4} vs unrecovered-null RMSE={:.4} ({:+.1}%) over {} nulled subcarriers",
+        "sig_sparse_recovery (ISTA)",
+        rmse,
+        baseline_rmse,
+        100.0 * (1.0 - rmse / baseline_rmse),
+        nulled.len()
+    );
+    // CONSTRUCTIBLE + MEASURED: the dropout detection and recovery-trigger
+    // pipeline fires correctly on >10% planted nulls. This is the validatable
+    // claim and we assert it.
+    assert!(recovery_seen, "dropout > 10% must trigger ISTA recovery (RECOVERY_COMPLETE)");
+    // HONEST MEASURED RESULT (reported, NOT asserted as a win): on this
+    // neighbor-correlated synthetic signal the tridiagonal-model ISTA recovery
+    // does NOT beat leaving the nulls at zero (RMSE ~1.00 vs ~0.98). The skill's
+    // *recovery accuracy* is therefore NOT validated as effective on synthetic
+    // data — only its dropout-detection/trigger path is. Reported in RESULTS.md.
+    assert!(
+        rmse.is_finite() && rmse < 5.0,
+        "recovered values must be finite and bounded; got {}",
+        rmse
+    );
+}
+
+// ── 7. exo_rain_detect — broadband variance onset (empty room) ───────────────
+// presence=0, MIN_EMPTY_FRAMES=40 baseline, then >=6/8 groups with variance
+// ratio > 2.5 for ONSET_FRAMES=10 -> EVENT_RAIN_ONSET(660).
+
+#[test]
+fn exo_rain_detect_broadband_onset() {
+    use wifi_densepose_wasm_edge::exo_rain_detect::RainDetector;
+
+    fn run(rain: bool) -> bool {
+        let mut det = RainDetector::new();
+        let n_sc = 32usize;
+        let phases = [0.1f32; 32];
+        let amps = [1.0f32; 32];
+        // 60 empty baseline frames with low variance.
+        for _ in 0..60 {
+            let vars = [0.001f32; 32];
+            det.process_frame(&phases, &vars, &amps, 0);
+        }
+        let mut onset = false;
+        // 40 frames: broadband-high variance if rain, else stay low.
+        for _ in 0..40 {
+            let vars = if rain { [0.5f32; 32] } else { [0.001f32; 32] };
+            for &(id, _) in det.process_frame(&phases, &vars, &amps, 0) {
+                if id == 660 {
+                    onset = true;
+                }
+            }
+        }
+        let _ = n_sc;
+        onset
+    }
+
+    let mut c = Confusion::default();
+    c.observe(run(true), true);
+    c.observe(run(false), false);
+    c.report("exo_rain_detect (broadband variance onset)");
+    assert!(run(true), "broadband variance elevation must trigger rain onset");
+    assert!(!run(false), "stable low variance must not trigger rain");
+}
+
+// ── 8. sig_flash_attention — peak-attention subcarrier localization ──────────
+// Q=mean(phase) per group, K=mean(prev_phase), score=Q*K/sqrt(8), softmax peak.
+// Plant a sustained large phase in a KNOWN group -> assert that group becomes
+// the reported attention peak (EVENT_ATTENTION_PEAK_SC=700).
+
+#[test]
+fn sig_flash_attention_peak_localization() {
+    use wifi_densepose_wasm_edge::sig_flash_attention::FlashAttention;
+
+    fn peak_for_group(target_group: usize) -> i32 {
+        let mut det = FlashAttention::new();
+        let n_sc = 32usize;
+        let subs_per = n_sc / 8;
+        let mut last_peak = -1;
+        // Sustain the spike so both Q (this frame) and K (prev frame) are large
+        // in the target group -> highest score there.
+        for _ in 0..20 {
+            let mut phases = [0.05f32; 32];
+            let mut amps = [1.0f32; 32];
+            for i in (target_group * subs_per)..((target_group + 1) * subs_per) {
+                phases[i] = 3.0;
+                amps[i] = 3.0;
+            }
+            for &(id, v) in det.process_frame(&phases, &amps) {
+                if id == 700 {
+                    last_peak = v as i32;
+                }
+            }
+        }
+        last_peak
+    }
+
+    let mut correct = 0u32;
+    let total = 8u32;
+    for g in 0..8usize {
+        let got = peak_for_group(g);
+        if got == g as i32 {
+            correct += 1;
+        }
+        println!("  flash_attention: planted group {} -> reported peak {}", g, got);
+    }
+    let acc = correct as f32 / total as f32;
+    println!(
+        "MEASURED-on-synthetic | {:<34} | peak-localization accuracy = {}/{} = {:.3}",
+        "sig_flash_attention", correct, total, acc
+    );
+    assert!(acc >= 0.75, "must localize the planted attention group in >=75% of cases; got {}", acc);
+}
+
+// ── 9. spt_spiking_tracker — phase-delta zone localization ───────────────────
+// LIF neurons fire on |phase - prev_phase|; zone with most spikes is tracked
+// (EVENT_TRACK_UPDATE=770 carries zone id). Plant motion in a KNOWN zone.
+
+#[test]
+fn spt_spiking_tracker_zone_localization() {
+    use wifi_densepose_wasm_edge::spt_spiking_tracker::SpikingTracker;
+
+    fn track_zone(target_zone: usize) -> i32 {
+        let mut det = SpikingTracker::new();
+        let n_sc = 32usize;
+        let per = n_sc / 4; // 4 zones of 8 subcarriers
+        let mut prev = [0.0f32; 32];
+        let mut last_zone = -1;
+        // SPARSE plant: each zone's output neuron sums home-weight 1.0 + cross
+        // 0.25. Firing all 8 inputs (8*0.25=2.0) overdrives EVERY zone, so the
+        // tracker collapses to zone 0. Firing only 2 inputs in the target zone
+        // gives potential 2.0 at home (fires) but 0.5 cross (silent) -> only the
+        // target zone fires. This is the genuinely-constructible localization.
+        let base = target_zone * per;
+        for t in 0..60 {
+            let mut phases = [0.0f32; 32];
+            // 2 subcarriers in the target zone get a large alternating delta.
+            for k in 0..2 {
+                phases[base + k] = if t % 2 == 0 { 0.0 } else { 3.0 };
+            }
+            for &(id, v) in det.process_frame(&phases, &prev) {
+                if id == 770 {
+                    last_zone = v as i32;
+                }
+            }
+            prev.copy_from_slice(&phases);
+        }
+        last_zone
+    }
+
+    let mut correct = 0u32;
+    for z in 0..4usize {
+        let got = track_zone(z);
+        if got == z as i32 {
+            correct += 1;
+        }
+        println!("  spiking_tracker: planted zone {} -> tracked zone {}", z, got);
+    }
+    let acc = correct as f32 / 4.0;
+    println!(
+        "MEASURED-on-synthetic | {:<34} | zone-localization accuracy = {}/4 = {:.3}",
+        "spt_spiking_tracker", correct, acc
+    );
+    assert!(acc >= 0.75, "must track the planted motion zone in >=75% of cases; got {}", acc);
+}
+
+// ── 10. sig_optimal_transport — distribution-shift detection ─────────────────
+// Sliced Wasserstein over amplitudes; sustained shift > WASS_SHIFT(0.25) for
+// SHIFT_DEB(3) -> EVENT_DISTRIBUTION_SHIFT(726). Plant a large vs no shift.
+
+#[test]
+fn sig_optimal_transport_distribution_shift() {
+    use wifi_densepose_wasm_edge::sig_optimal_transport::OptimalTransportDetector;
+
+    fn run(shift: bool) -> bool {
+        let mut det = OptimalTransportDetector::new();
+        let n_sc = 32usize;
+        // Establish a reference distribution.
+        let base: Vec<f32> = (0..n_sc).map(|i| i as f32 * 0.1).collect();
+        for _ in 0..10 {
+            let mut a = base.clone();
+            det.process_frame(&mut a);
+        }
+        let mut shifted = false;
+        // The detector compares each frame to the PREVIOUS frame (prev_amps is
+        // updated every frame), so a one-time jump decays. To exceed WASS_SHIFT
+        // (0.25) for SHIFT_DEB(3) consecutive frames we need a sustained large
+        // frame-to-frame change: alternate between two very different
+        // distributions each frame.
+        for t in 0..15 {
+            let mut a: Vec<f32> = if shift {
+                if t % 2 == 0 {
+                    base.clone()
+                } else {
+                    base.iter().map(|x| 10.0 - x).collect() // reversed + offset
+                }
+            } else {
+                base.clone()
+            };
+            for &(id, _) in det.process_frame(&mut a) {
+                if id == 726 {
+                    shifted = true;
+                }
+            }
+        }
+        shifted
+    }
+
+    let mut c = Confusion::default();
+    c.observe(run(true), true);
+    c.observe(run(false), false);
+    c.report("sig_optimal_transport (distribution shift)");
+    assert!(run(true), "large amplitude-distribution shift must be detected");
+    assert!(!run(false), "stationary distribution must not flag a shift");
+}
+
+// ── 11. lrn_dtw_gesture_learn — enroll a template, replay match vs reject ────
+// STILLNESS_FRAMES=60 stillness, then 3 rehearsals of the same gesture
+// (motion->stillness) -> EVENT_GESTURE_LEARNED(730). Replaying the learned
+// gesture later (in Idle) -> EVENT_GESTURE_MATCHED(731); replaying a different
+// gesture -> no match.
+
+#[test]
+fn lrn_dtw_gesture_learn_enroll_and_match() {
+    use wifi_densepose_wasm_edge::lrn_dtw_gesture_learn::GestureLearner;
+
+    // A gesture is a phase trajectory across frames; motion_energy gates the
+    // enroll state machine (still < 0.05, moving >= 0.05).
+    fn gesture_frame(kind: u8, step: usize) -> ([f32; 32], f32) {
+        let mut phases = [0.0f32; 32];
+        let s = step as f32;
+        for i in 0..32 {
+            phases[i] = match kind {
+                // distinct trajectories
+                0 => (s * 0.4 + i as f32 * 0.1).sin(),
+                _ => (s * 0.9 + i as f32 * 0.05).cos() * 1.5,
+            };
+        }
+        (phases, 0.5) // moving
+    }
+
+    let mut det = GestureLearner::new();
+    let still = ([0.0f32; 32], 0.0f32);
+
+    // helper to feed N still frames
+    let feed_still = |det: &mut GestureLearner, n: usize| {
+        for _ in 0..n {
+            det.process_frame(&still.0, still.1);
+        }
+    };
+    let feed_gesture = |det: &mut GestureLearner, kind: u8, len: usize| -> bool {
+        let mut learned = false;
+        for s in 0..len {
+            let (ph, me) = gesture_frame(kind, s);
+            for &(id, _) in det.process_frame(&ph, me) {
+                if id == 730 {
+                    learned = true;
+                }
+            }
+        }
+        learned
+    };
+
+    // Enroll gesture kind 0: stillness, then 3 identical rehearsals (each
+    // motion burst followed by stillness).
+    feed_still(&mut det, 70);
+    let mut any_learned = false;
+    for _ in 0..3 {
+        any_learned |= feed_gesture(&mut det, 0, 30);
+        feed_still(&mut det, 70);
+    }
+
+    // Replay the SAME gesture during Idle -> expect a match (731).
+    let mut matched_same = false;
+    for s in 0..30 {
+        let (ph, me) = gesture_frame(0, s);
+        for &(id, _) in det.process_frame(&ph, me) {
+            if id == 731 {
+                matched_same = true;
+            }
+        }
+    }
+    feed_still(&mut det, 70);
+    // Replay a DIFFERENT gesture -> ideally no match (731) to the learned one.
+    let mut matched_diff = false;
+    for s in 0..30 {
+        let (ph, me) = gesture_frame(1, s);
+        for &(id, _) in det.process_frame(&ph, me) {
+            if id == 731 {
+                matched_diff = true;
+            }
+        }
+    }
+
+    let tmpl_count = det.template_count();
+    println!(
+        "MEASURED-on-synthetic | {:<34} | learned_event={} templates={} match_same={} match_different={}",
+        "lrn_dtw_gesture_learn", any_learned, tmpl_count, matched_same, matched_diff
+    );
+    // The enroll path must complete (a template is learned from 3 identical
+    // rehearsals). Whether the precise replay matches is the DTW behavior we
+    // measure and report; we assert the deterministic enrollment.
+    assert!(
+        any_learned || tmpl_count > 0,
+        "3 identical rehearsals after stillness must enroll a template"
+    );
+}
+
+// ── 12. sig_mincut_person_match — stable id assignment for distinct signatures ─
+// Per-person feature = top-FEAT_DIM variances in that person's spatial region.
+// Two persons with DISTINCT, stable variance signatures should get stable ids
+// (EVENT_PERSON_ID_ASSIGNED=720) with zero swaps across frames.
+
+#[test]
+fn sig_mincut_person_stable_ids() {
+    use wifi_densepose_wasm_edge::sig_mincut_person_match::PersonMatcher;
+
+    let mut det = PersonMatcher::new();
+    let n_sc = 32usize;
+    let amplitudes = [1.0f32; 32];
+    let mut swaps = 0u32;
+    let mut assigned = false;
+
+    // 40 frames, 2 persons: person 0 region (0..16) high-variance signature,
+    // person 1 region (16..32) low-variance signature, both stable.
+    for _ in 0..40 {
+        let mut variances = [0.0f32; 32];
+        for i in 0..n_sc {
+            variances[i] = if i < 16 {
+                2.0 + 0.05 * (i as f32).sin()
+            } else {
+                0.2 + 0.01 * (i as f32).cos()
+            };
+        }
+        for &(id, _) in det.process_frame(&amplitudes, &variances, 2) {
+            if id == 720 {
+                assigned = true;
+            }
+            if id == 721 {
+                swaps += 1;
+            }
+        }
+    }
+    println!(
+        "MEASURED-on-synthetic | {:<34} | assigned={} id_swaps_over_40_frames={}",
+        "sig_mincut_person_match", assigned, swaps
+    );
+    assert!(assigned, "distinct stable signatures must assign person ids");
+    assert!(swaps == 0, "stable distinct signatures must not swap ids; got {} swaps", swaps);
+}