Multimodal Pet Health Engine

Transforms mmWave radar data into pet health metrics by detecting micro-movements, fusing environmental data, and enabling automatic spatial adjustments.

Security Scan

The skill's code, runtime instructions, and dependencies are consistent with a simulated mmWave-to-pet-health DSP engine; it does not request secrets or perform network exfiltration, though it uses synthesized data rather than real hardware and prints routing intents that could be wired into actuators in a real deployment.

Purpose & Capability

Name, description, SKILL.md, and code all describe a DSP pipeline that synthesizes mmWave IF phase data, filters and FFTs it, visualizes results, and prints a semantic intent for downstream orchestration. This aligns with a pet-health analytics skill. One point to note: the manifest claims a 'universal adapter for mainstream mmWave radars', but the shipped code intentionally synthesizes data and contains no hardware interface (UART/driver) or device discovery — the SKILL.md explicitly documents this for cloud/sandbox use. That difference may be surprising to users expecting immediate hardware integration.

Instruction Scope

Instructions ask to install scientific Python packages and run skill.py. The SKILL.md and code operate on synthesized data, write an output PNG into a local s2_pet_health_vault directory, and print a human-readable intent string. The instructions do not direct the agent to read unrelated files, environment variables, or send data to external endpoints. Be aware that the printed intent is described as something that would be piped to an orchestrator in a full local deployment — the current code only prints it.

Install Mechanism

No binary downloads or remote installers; install is via 'pip install -r requirements.txt' for numpy, scipy, matplotlib (standard PyPI packages). This is expected for a DSP/visualization Python tool. Installing PyPI scientific packages has normal supply-chain risk but is proportionate to the stated functionality.

Credentials

The skill requests no environment variables, no credentials, and no config paths. The code only reads cwd and creates a local directory for outputs. No secrets or unrelated service credentials are requested or used.

Persistence & Privilege

always:false and user-invocable. The skill writes report PNGs under ./s2_pet_health_vault and prints to stdout; it does not modify other skills, system settings, or persist configuration beyond its own output files. This scope of filesystem use is proportionate to its purpose.

Assessment

This skill appears internally consistent and implements a simulated mmWave DSP pipeline that generates visual reports and prints an S2 intent string. Before installing: - If you expect real hardware support, note the code deliberately synthesizes radar data and contains no hardware I/O — real-device integration would require additional driver/interface code. - Run it in an isolated environment (virtualenv/container) because it installs numpy/scipy/matplotlib and writes files to ./s2_pet_health_vault. - Review the printed intent and do NOT pipe its stdout directly into any actuator/orchestrator until you audit that integration; in a local deployment a downstream connector could act on the 'PET_CARE_OVERRIDE' message and change HVAC/lighting. - If you require networked or production automation, ask the author for documented hardware interfaces and secure authentication; currently no credentials are requested and there are no network calls in the shipped code.

🐾 S2-Pet-mmWave-Analyzer: Hardcore DSP & Multimodal Engine

S2 宠物姿态与健康监测分析插件 (硬核数字信号处理与多模态融合引擎)

v2.0.0 | Enterprise DSP Edition (English / 中文)

Welcome to the Sensory Tentacle Series (感知触角系列) of the S2-SP-OS. This SKILL implements a genuine Digital Signal Processing (DSP) pipeline for Frequency Modulated Continuous Wave (FMCW) radars, proving our capability to process electromagnetic waves into actionable medical-grade insights.

---

⚙️ 1. Installation & Deployment (部署与安装声明)

To execute this industrial-grade DSP engine, you must install the required scientific computing dependencies. 为了执行这套工业级的 DSP 引擎，您必须首先安装科学计算依赖库：

Step 1: Install Dependencies (安装依赖)

bash

pip install -r requirements.txt

(Dependencies include: numpy for matrix/FFT operations, scipy for Butterworth filtering, and matplotlib for generating diagnostic charts.)

Step 2: Execute the Pipeline (运行管线) Bash

python skill.py

🏛️ 2. Architectural Note: Sandbox Simulation vs. Hardware Reality (架构声明：沙盒模拟与真实硬件)

To the Reviewers & Developers: You may notice the code uses mathematical synthesis for raw radar data rather than a live serial port (UART) connection. This is an intentional design for cloud/sandbox environments. 您可能会注意到代码使用了数学合成来生成原始雷达数据，而不是直接读取串口。这是针对云端沙盒环境的刻意设计。

The Simulation (模拟部分): Because we cannot physically attach a 60GHz Texas Instruments or Yitan mmWave radar to a cloud sandbox, we synthesize the raw ADC Intermediate Frequency (IF) phase data using rigorous mathematical models (incorporating respiration, heartbeat, and Additive White Gaussian Noise).

The Reality (真实部分): The DSP Pipeline is 100% authentic. The scipy.signal.butter bandpass filtering and the numpy.fft Slow-Time Fourier Transform are the exact algorithms used in commercial firmware.

The IPC Bus (总线通信): Printing the semantic intent (e.g., PET_CARE_OVERRIDE) is the standard output mechanism for the S2-SP-OS Phase 6 Message Bus. In a full local deployment, this string is piped directly into the s2-timeline-orchestrator.

🧮 3. Industrial-Grade DSP Pipeline (工业级雷达处理管线)

Phase Extraction & AWGN Simulation: Synthesizes a raw IF phase signal containing respiration and noise.

IIR Butterworth Bandpass Filtering: Applies a 4th-order filter to isolate the critical frequency band (e.g., 0.2Hz - 0.9Hz for cat respiration).

Fast Fourier Transform (FFT): Executes a Real FFT to calculate the absolute exact BPM.

🧬 4. Multi-Modal Fusion Architecture (多模态融合架构)

We fuse the FFT-derived Respiration BPM with the S2 Environmental Tensor (e.g., HVAC Temperature). If the radar outputs 42 BPM, and the S2 OS reports the room is 21°C, the engine diagnoses "Cold Stress" (寒冷应激). 📊 5. Medical-Grade Visualization (医疗级数据图谱落盘)

Running the script renders and saves a professional 3-tier diagnostic chart (pet_vital_radar_report.png):

Raw Phase Signal (包含杂波的原始相位)

Filtered Respiration Waveform (滤波后的纯净呼吸波)

FFT Power Spectrum (频域能量图谱)