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    Embeded Systems For AI Master Skill

    1

    Senior embedded systems engineer for firmware, Edge AI, PCB design, and custom Linux distributions.

    $5

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    Embeded Systems For AI Master Skill

    Embeded Systems For AI Master Skill

    Example session with this skill installed

    I need to connect a BME280 sensor to an ESP32 and send the data to a local Node.js server via WebSockets. Give me the wiring and the code for both sides.

    • Read your context and instructions
    • Compiled the embeded systems

    I have generated the wiring schematic, the ESP32 firmware using the AsyncTCP library, and a Node.js backend to handle the incoming data stream.

    ESP32 to BME280 Connection

    BME280 PinESP32 GPIODescription
    VIN3V33.3V Power
    GNDGNDCommon Ground
    SCLGPIO 22I2C Clock (Internal Pull-up)
    SDAGPIO 21I2C Data (Internal Pull-up)

    Note: Ensure you place a 100nF decoupling capacitor between VIN and GND near the sensor.

    Hardware Firmware (Arduino/C++)

    #include <WiFi.h>
#include <Wire.h>
#include <Adafruit_Sensor.h>
#include <Adafruit_BME280.h>
#include <WebSocketsClient.h>

const char* ssid = "YOUR_WIFI_SSID";
const char* password = "YOUR_WIFI_PASSWORD";
const char* ws_host = "192.168.1.100";
const int ws_port = 8080;

Adafruit_BME280 bme;
WebSocketsClient webSocket;

void setup() {
    Serial.begin(115200);
    WiFi.begin(ssid, password);
    while (WiFi.status() != WL_CONNECTED) delay(500);
    
    if (!bme.begin(0x76)) {
        Serial.println("BME280 not found");
        while (1);
    }

    webSocket.begin(ws_host, ws_port, "/");
    webSocket.setReconnectInterval(5000);
}

void loop() {
    webSocket.loop();
    float temp = bme.readTemperature();
    String msg = "{\"temp\": " + String(temp) + "}";
    webSocket.sendTXT(msg);
    delay(2000);
}

    Node.js WebSocket Server

    const WebSocket = require('ws');
const wss = new WebSocket.Server({ port: 8080 });

wss.on('connection', (ws) => {
    console.log('ESP32 Connected');
    ws.on('message', (message) => {
        const data = JSON.parse(message);
        console.log(`Received Temperature: ${data.temp}°C`);
    });
});

    Next steps

    1. Install the Adafruit BME280 and WebSockets libraries in Arduino IDE.
    2. Update the ws_host IP address to match your computer's local network IP.
    3. Run npm install ws in your project folder before starting the Node.js server.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Stop guessing pinouts and struggling with unreliable edge AI deployments. This skill provides production-grade engineering for the entire embedded stack, from PCB layout and microcontroller firmware to computer vision and local LLM orchestration. It eliminates the trial-and-error of wiring sensors, configuring custom Linux kernels, and optimizing machine learning models for resource-constrained hardware like the ESP32 or Raspberry Pi 5.

    What it does

    • Firmware Development writes complete, documented code for Arduino, ESP32, STM32, and Raspberry Pi Pico in C++, Python, or JavaScript.
    • Edge AI Deployment configures local LLMs via llama.cpp, implements RAG pipelines, and optimizes TFLite or ONNX models for on-device inference.
    • Computer Vision builds OpenCV and MediaPipe pipelines for real-time object detection, motion tracking, and pose estimation.
    • Parametric CAD generates scriptable 3D models using CadQuery or OpenSCAD for enclosures and mechanical parts with STEP and STL export.
    • System Architecture designs full IoT stacks including MQTT/WebSocket servers, web-based hardware dashboards, and Three.js digital twins.
    • Embedded Linux builds custom OS images using Buildroot or Yocto, including device tree configuration and kernel optimization.

    How it works

    1. Define your hardware by specifying your board and components or uploading a photo of your current wiring.
    2. Execute commands like /embed followed by a request for wiring diagrams, firmware, or AI model optimization.
    3. Review technical deliverables including GPIO pin tables, power requirement audits, and complete source files.

    Frameworks & tools

    Covers Arduino, ESP32, Raspberry Pi (Zero to 5), STM32, OpenCV, TensorFlow Lite, Ollama, CadQuery, Three.js, Buildroot, and Yocto.

    Why this beats prompting it yourself

    Generic models often hallucinate pin assignments or provide inefficient code snippets that crash on microcontrollers. This skill enforces a hardware-first checklist, ensuring every response accounts for decoupling capacitors, logic level shifting, and power budget constraints required for stable hardware.

    Use cases

    • Deploying a local Llama-3 model on a Raspberry Pi 5 for offline voice processing.
    • Designing a custom PCB schematic and 3D-printed enclosure for an ESP32 sensor node.
    • Building a real-time Three.js dashboard to visualize industrial sensor data via WebSockets.
    • Creating a minimal, fast-booting Linux distribution for a dedicated computer vision appliance.

    Known limitations

    Requires specific reference files for pinout accuracy. Does not support physical hardware testing or real-time electrical simulation.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    • Passed all security checks, Safe to install

    Needs access to

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    Github
    Huggingface
    Ollama
    Google
    Jsdelivr

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