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docs: collapse secondary README details

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@@ -235,6 +235,11 @@ The separate **17-keypoint pose-estimation model** is now published at [`ruvnet/
### Results & proof
See the measured benchmarks, witness records, and one-command reproducibility check.
<details>
<summary><strong>View benchmark and proof details</strong></summary>
| What | Where | Numbers |
|------|-------|---------|
| **MM-Fi pose model (SOTA)** | [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) | 82.69% torso-PCK@20 (single) · 83.59% (ensemble+TTA) · 75K-param micro variant 74.30% |
@@ -253,8 +258,15 @@ python archive/v1/data/proof/verify.py
Tracked in [#509](https://github.com/ruvnet/RuView/issues/509); see [ADR-079](docs/adr/ADR-079-camera-ground-truth-training.md) phases P7–P9 for the camera-supervised fine-tune path.
</details>
### Model weights: what's real, what's not
See which checkpoints are validated, experimental, or architecture-only.
<details>
<summary><strong>View model maturity details</strong></summary>
"WiFi → pose" means three different things in this repo, at three different maturity
levels. Read the label, not the headline ([ADR-187](docs/adr/ADR-187-archive-v1-deprecation-honest-labeling.md)):
@@ -272,13 +284,17 @@ project can stand behind today is the **MM-Fi benchmark number**, not a live sin
number. The path to a first *reproducible* on-device baseline (PCK@20 ≥ 35%) is tracked in
[ADR-079](docs/adr/ADR-079-camera-ground-truth-training.md) / [#645](https://github.com/ruvnet/RuView/issues/645) — do not advertise the live single-ESP32 17-keypoint feature without the "first-cut, below-target, runtime-stub" caveat until that baseline is measured.
</details>
## 🧩 Edge Module Catalog
<details>
<summary><b>🧩 105 edge modules ready to install on a Cognitum appliance</b> &mdash; live catalog from <code>app-registry.json</code> v2.1.0 (updated 2026-05-13). Browse + install at <a href="https://seed.cognitum.one/store">seed.cognitum.one/store</a> or your local appliance <code>http://&lt;appliance&gt;:9000/cogs</code>.</summary>
Add signed modules for health, security, buildings, industry, research, AI, and more.
Each module is a small signed binary (~400 KB) that runs alongside the WiFi-DensePose sensing stack on a Cognitum-V0 appliance. The catalog updates over the air &mdash; your appliance fetches it via <code>GET /api/v1/edge/registry</code> ([ADR-102](docs/adr/ADR-102-edge-module-registry.md)) and verifies each binary against an Ed25519 signature ([ADR-100](docs/adr/ADR-100-cog-packaging-specification.md)) before install.
<details>
<summary><strong>Browse the full edge module catalog</strong></summary>
Browse and install modules at [seed.cognitum.one/store](https://seed.cognitum.one/store) or on your appliance at `http://<appliance>:9000/cogs`. Each module is a small signed binary that runs beside the sensing stack. The appliance updates the catalog over the air and verifies every module before installation ([ADR-100](docs/adr/ADR-100-cog-packaging-specification.md), [ADR-102](docs/adr/ADR-102-edge-module-registry.md)).
### 🫀 Health &mdash; <sub>14 modules</sub>
@@ -561,8 +577,12 @@ These scenarios exploit WiFi's ability to penetrate solid materials — concrete
---
## 🧠 Self-Learning WiFi AI
Learn compact room fingerprints from raw CSI and adapt the model to each environment.
<details>
<summary><strong>🧠 Self-Learning WiFi AI (ADR-024)</strong> — Adaptive recognition, self-optimization, and intelligent anomaly detection</summary>
<summary><strong>View self-learning architecture and commands</strong></summary>
Every WiFi signal that passes through a room creates a unique fingerprint of that space. WiFi-DensePose already reads these fingerprints to track people, but until now it threw away the internal "understanding" after each reading. The Self-Learning WiFi AI captures and preserves that understanding as compact, reusable vectors — and continuously optimizes itself for each new environment.
@@ -645,7 +665,12 @@ See [`docs/adr/ADR-024-contrastive-csi-embedding-model.md`](docs/adr/ADR-024-con
## 🧩 Claude Code & Codex Plugin
RuView ships a [Claude Code](https://docs.anthropic.com/en/docs/claude-code) plugin (and Codex prompt mirror) that wraps the whole workflow — onboarding, ESP32 setup, configuration, sensing apps, model training, advanced multistatic sensing, CLI/API/WASM, mmWave radar, and witness verification — as 9 skills, 7 `/ruview-*` commands, and 3 agents. It lives in [`plugins/ruview/`](plugins/ruview/README.md); the marketplace manifest is [`.claude-plugin/marketplace.json`](.claude-plugin/marketplace.json) at the repo root.
Use the in-repo plugin for guided setup, sensing, training, and verification in Claude Code or Codex.
<details>
<summary><strong>View plugin installation and commands</strong></summary>
RuView's [Claude Code](https://docs.anthropic.com/en/docs/claude-code) plugin and Codex prompt mirror cover onboarding, ESP32 setup, sensing apps, model training, advanced sensing, CLI/API/WASM, mmWave radar, and witness verification. The source lives in [`plugins/ruview/`](plugins/ruview/README.md); the marketplace manifest is [`.claude-plugin/marketplace.json`](.claude-plugin/marketplace.json).
```bash
# In Claude Code — add this repo as a plugin marketplace, then install:
@@ -669,12 +694,19 @@ claude --plugin-dir ./plugins/ruview
Verify the plugin structure: `bash plugins/ruview/scripts/smoke.sh`. Full details: [`plugins/ruview/README.md`](plugins/ruview/README.md).
**Portable harness — `npx @ruvnet/ruview`:** a lighter, host-portable companion to the in-repo plugin, minted via [MetaHarness](https://www.npmjs.com/package/metaharness) and hardened per [ADR-182](docs/adr/ADR-182-npx-ruview-harness-via-metaharness.md). It runs **without cloning this repo** and on more hosts (Claude Code, Codex, Copilot, opencode, …), exposing the RuView operator tools (`onboard`, `verify`, `node_monitor`, `calibrate`, `node_flash`) over an MCP server — plus the project's **MEASURED-vs-CLAIMED honesty guardrail enforced in code** (`ruview.claim_check` flags untagged or retracted-"100%" accuracy claims). v0.1: the onboarding/verify/claim-check paths are tested (17/17, `verify.py` → PASS); the hardware tools are fail-closed wrappers. Try `npx @ruvnet/ruview` to onboard, or `npx @ruvnet/ruview claim-check --text "…"`. Source: [`harness/ruview/`](harness/ruview/README.md).
For the portable RuView MetaHarness, use `npx @ruvnet/ruview@0.3.1`; the quick commands and fuller explanation are in the collapsed MetaHarness section near the top of this README and in [`harness/ruview/`](harness/ruview/README.md).
</details>
---
## 📖 Documentation
Start with the user, build, and calibration guides; expand for the full reference map.
<details>
<summary><strong>Browse all documentation</strong></summary>
| Document | Description |
|----------|-------------|
| [User Guide](docs/user-guide.md) | Step-by-step guide: installation, first run, API usage, hardware setup, training |
@@ -696,6 +728,8 @@ Verify the plugin structure: `bash plugins/ruview/scripts/smoke.sh`. Full detail
| [Medical Examples](examples/medical/README.md) | Contactless blood pressure, heart rate, breathing rate via 60 GHz mmWave radar — $15 hardware, no wearable |
| [Extended Documentation](docs/readme-details.md) | Latest additions, key features, installation, quick start, signal processing, training, CLI, testing, deployment, and changelog |
</details>
---
## 🚧 Beta software