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Embedded Iot

Community
travisjneuman
embedded-iot

Embedded systems firmware, microcontrollers (ESP32, STM32, Arduino, Raspberry Pi), RTOS (FreeRTOS, Zephyr), IoT protocols (MQTT, CoAP, BLE), bare-metal C/C++, and hardware peripheral interfaces (I2C, SPI, UART, GPIO). Use when developing firmware, working with microcontrollers, or building IoT devices.

Overview

Publishertravisjneuman
Repository.claude
Skill nameembedded-iot
Stars
98
Forks
22
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by travisjneuman on GitHub. Read the source before you install it.

Installation

Install the Embedded Iot AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/travisjneuman/.claude.git /tmp/.claude
mkdir -p .claude/skills
cp -r /tmp/.claude/skills/embedded-iot .claude/skills/embedded-iot
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Embedded Iot in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Embedded Iot on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Embedded Iot is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Embedded Systems & IoT

Microcontroller Platforms

PlatformBest ForLanguageIDE
ESP32WiFi/BLE IoT devicesC/C++, MicroPythonPlatformIO, Arduino IDE
STM32Industrial, real-timeC/C++STM32CubeIDE, PlatformIO
ArduinoPrototyping, learningC++ (Arduino)Arduino IDE, PlatformIO
Raspberry Pi PicoRP2040, dual-coreC/C++, MicroPythonThonny, VS Code
nRF52BLE-focused IoTC, ZephyrnRF Connect SDK

Firmware Patterns

GPIO & Peripherals

c
// ESP-IDF GPIO example
gpio_config_t io_conf = {
    .pin_bit_mask = (1ULL << GPIO_NUM_2),
    .mode = GPIO_MODE_OUTPUT,
    .pull_up_en = GPIO_PULLUP_DISABLE,
    .pull_down_en = GPIO_PULLDOWN_DISABLE,
    .intr_type = GPIO_INTR_DISABLE,
};
gpio_config(&io_conf);
gpio_set_level(GPIO_NUM_2, 1);

I2C Communication

c
// Read sensor via I2C
i2c_cmd_handle_t cmd = i2c_cmd_link_create();
i2c_master_start(cmd);
i2c_master_write_byte(cmd, (SENSOR_ADDR << 1) | I2C_MASTER_WRITE, true);
i2c_master_write_byte(cmd, REG_TEMP, true);
i2c_master_start(cmd);
i2c_master_write_byte(cmd, (SENSOR_ADDR << 1) | I2C_MASTER_READ, true);
i2c_master_read(cmd, data, 2, I2C_MASTER_LAST_NACK);
i2c_master_stop(cmd);
i2c_master_cmd_begin(I2C_NUM_0, cmd, pdMS_TO_TICKS(1000));
i2c_cmd_link_delete(cmd);

RTOS (FreeRTOS)

c
// Task creation
void sensor_task(void *pvParameters) {
    while (1) {
        float temp = read_temperature();
        xQueueSend(data_queue, &temp, portMAX_DELAY);
        vTaskDelay(pdMS_TO_TICKS(1000));
    }
}
xTaskCreate(sensor_task, "sensor", 4096, NULL, 5, NULL);

// Mutex for shared resources
SemaphoreHandle_t spi_mutex = xSemaphoreCreateMutex();
if (xSemaphoreTake(spi_mutex, pdMS_TO_TICKS(100)) == pdTRUE) {
    spi_transfer(data);
    xSemaphoreGive(spi_mutex);
}

IoT Protocols

MQTT

c
// ESP-IDF MQTT client
esp_mqtt_client_config_t mqtt_cfg = {
    .broker.address.uri = "mqtt://broker.hivemq.com",
};
esp_mqtt_client_handle_t client = esp_mqtt_client_init(&mqtt_cfg);
esp_mqtt_client_start(client);
esp_mqtt_client_publish(client, "/sensors/temp", "23.5", 0, 1, 0);

Key IoT Protocols

ProtocolTransportUse Case
MQTTTCP/TLSPub/sub messaging, telemetry
CoAPUDP/DTLSConstrained devices, REST-like
BLERadioShort-range, low power
LoRaWANRadioLong-range, low data rate
MatterIPSmart home interoperability

Best Practices

  • Power management: Deep sleep modes, wake-on-interrupt, duty cycling
  • Watchdog timers: Always enable, reset periodically, catch firmware hangs
  • OTA updates: Dual partition scheme, rollback on boot failure, signature verification
  • Memory: Static allocation preferred, avoid heap fragmentation, use memory pools
  • Testing: Hardware-in-the-loop (HIL), mock hardware interfaces for unit tests

Frequently asked questions

What does the Embedded Iot AI skill do?

Embedded systems firmware, microcontrollers (ESP32, STM32, Arduino, Raspberry Pi), RTOS (FreeRTOS, Zephyr), IoT protocols (MQTT, CoAP, BLE), bare-metal C/C++, and hardware peripheral interfaces (I2C, SPI, UART, GPIO). Use when developing firmware, working with microcontrollers, or building IoT devices.

Why use Embedded Iot on TypingMind?

Because you install it once and use it with any model. Embedded Iot is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Embedded Iot in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/travisjneuman/.claude/tree/master/skills/embedded-iot. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Embedded Iot?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Embedded Iot?

As many as you like. As long as a model supports skills, you can use Embedded Iot with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Embedded Iot AI skill free?

Yes. It is published on GitHub by travisjneuman under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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