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Multimodal Pet Health Engine
by
MilesXiang
· GitHub ↗
· v1.1.0
· MIT-0
149
Downloads
0
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0
Active Installs
3
Versions
Install in OpenClaw
/install s2-pet-mmwave-analyzer
Description
Transforms mmWave radar data into pet health metrics by detecting micro-movements, fusing environmental data, and enabling automatic spatial adjustments.
Usage Guidance
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.
Capability Analysis
Package: s2-pet-mmwave-analyzer (xpi)
Version: 1.1.0
Description:
The package is a simulation tool for processing mmWave radar signals to monitor pet health. It uses standard scientific libraries (NumPy, SciPy, Matplotlib) to synthesize radar data, apply digital signal processing (Butterworth filtering and FFT), and visualize the results. The code does not perform any network operations, execute external binaries, or access sensitive system information. It strictly operates within its own directory to save generated reports.
Capability Assessment
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.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install s2-pet-mmwave-analyzer - After installation, invoke the skill by name or use
/s2-pet-mmwave-analyzer - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.1.0
S2-Pet-mmWave-Analyzer v1.1.0
- Added a requirements.txt file to specify and simplify installation of needed dependencies.
- Updated documentation to include explicit installation steps and stress the need for installing scientific computing libraries (numpy, scipy, matplotlib).
- Clarified the architecture, highlighting the deliberate use of data simulation for cloud/sandbox environments.
- Shifted technical focus in documentation to installation and deployment, while retaining concise pipeline and architecture summaries.
v1.0.1
v2.0.0 is a major upgrade to an enterprise-grade DSP engine for FMCW radar-based pet health monitoring.
- Implements an industrial-grade digital signal processing (DSP) pipeline, replicating hardware manufacturer logic (phase extraction, noise simulation, bandpass filtering, and FFT) for precise pet respiration and heart rate extraction from raw radar data.
- Introduces a sophisticated multi-modal fusion engine, cross-referencing vital signs with S2 environmental context to deliver accurate, clinically-relevant diagnoses (e.g., "cold stress").
- Enhances closed-loop automation: semantic health insights directly control smart home HVAC, adjusting airflow and temperature to relieve pet discomfort based on radar coordinates.
- Adds medical-grade visualization—diagnostic charts of raw, filtered, and frequency-domain signals are generated and saved as images for professional analysis.
- Moves beyond API-wrappers and simulation to full software-based radar DSP, enabling privacy-preserving, actionable pet health and spatial adjustment.
v1.0.0
- Initial release of S2-Pet-mmWave-Analyzer, transforming mainstream mmWave radar into a medical-grade pet health monitoring and space adjustment engine.
- Introduces a privacy-friendly, multimodal architecture with real-time fusion of radar physiological data and environmental context.
- Enables automated space adjustment by integrating with the S2 Timeline Orchestrator to dynamically control home environments based on pet health signals.
- Bilingual documentation (English/中文) included for broader accessibility.
Metadata
Frequently Asked Questions
What is Multimodal Pet Health Engine?
Transforms mmWave radar data into pet health metrics by detecting micro-movements, fusing environmental data, and enabling automatic spatial adjustments. It is an AI Agent Skill for Claude Code / OpenClaw, with 149 downloads so far.
How do I install Multimodal Pet Health Engine?
Run "/install s2-pet-mmwave-analyzer" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Multimodal Pet Health Engine free?
Yes, Multimodal Pet Health Engine is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Multimodal Pet Health Engine support?
Multimodal Pet Health Engine is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Multimodal Pet Health Engine?
It is built and maintained by MilesXiang (@spacesq); the current version is v1.1.0.
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