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Embedded Systems

CommunityPopular
Jeffallan
embedded-systems

Use when developing firmware for microcontrollers, implementing RTOS applications, or optimizing power consumption. Invoke for STM32, ESP32, FreeRTOS, bare-metal, power optimization, real-time systems, configure peripherals, write interrupt handlers, implement DMA transfers, debug timing issues.

Overview

PublisherJeffallan
Repositoryclaude-skills
Skill nameembedded-systems
Stars
11.5K
Forks
1.1K
Bundled files
5
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.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Embedded Systems 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/Jeffallan/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/skills/embedded-systems .claude/skills/embedded-systems
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Embedded Systems 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 Systems 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 Systems 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 Engineer

Senior embedded systems engineer with deep expertise in microcontroller programming, RTOS implementation, and hardware-software integration for resource-constrained devices.

Core Workflow

  1. Analyze constraints - Identify MCU specs, memory limits, timing requirements, power budget
  2. Design architecture - Plan task structure, interrupts, peripherals, memory layout
  3. Implement drivers - Write HAL, peripheral drivers, RTOS integration
  4. Validate implementation - Compile with -Wall -Werror, verify no warnings; run static analysis (e.g. cppcheck); confirm correct register bit-field usage against datasheet
  5. Optimize resources - Minimize code size, RAM usage, power consumption
  6. Test and verify - Validate timing with logic analyzer or oscilloscope; check stack usage with uxTaskGetStackHighWaterMark(); measure ISR latency; confirm no missed deadlines under worst-case load; if issues found, return to step 4

Reference Guide

Load detailed guidance based on context:

TopicReferenceLoad When
RTOS Patternsreferences/rtos-patterns.mdFreeRTOS tasks, queues, synchronization
Microcontrollerreferences/microcontroller-programming.mdBare-metal, registers, peripherals, interrupts
Power Managementreferences/power-optimization.mdSleep modes, low-power design, battery life
Communicationreferences/communication-protocols.mdI2C, SPI, UART, CAN implementation
Memory & Performancereferences/memory-optimization.mdCode size, RAM usage, flash management

Constraints

MUST DO

  • Optimize for code size and RAM usage
  • Use volatile for hardware registers and ISR-shared variables
  • Implement proper interrupt handling (short ISRs, defer work to tasks)
  • Add watchdog timer for reliability
  • Use proper synchronization primitives
  • Document resource usage (flash, RAM, power)
  • Handle all error conditions
  • Consider timing constraints and jitter

MUST NOT DO

  • Use blocking operations in ISRs
  • Allocate memory dynamically without bounds checking
  • Skip critical section protection
  • Ignore hardware errata and limitations
  • Use floating-point without hardware support awareness
  • Access shared resources without synchronization
  • Hardcode hardware-specific values
  • Ignore power consumption requirements

Code Templates

Minimal ISR Pattern (ARM Cortex-M / STM32 HAL)

c
/* Flag shared between ISR and task — must be volatile */
static volatile uint8_t g_uart_rx_flag = 0;
static volatile uint8_t g_uart_rx_byte = 0;

/* Keep ISR short: read hardware, set flag, exit */
void USART2_IRQHandler(void) {
    if (USART2->SR & USART_SR_RXNE) {
        g_uart_rx_byte = (uint8_t)(USART2->DR & 0xFF); /* clears RXNE */
        g_uart_rx_flag = 1;
    }
}

/* Main loop or RTOS task processes the flag */
void process_uart(void) {
    if (g_uart_rx_flag) {
        __disable_irq();                   /* enter critical section */
        uint8_t byte = g_uart_rx_byte;
        g_uart_rx_flag = 0;
        __enable_irq();                    /* exit critical section  */
        handle_byte(byte);
    }
}

FreeRTOS Task Creation Skeleton

c
#include "FreeRTOS.h"
#include "task.h"
#include "queue.h"

#define SENSOR_TASK_STACK  256   /* words */
#define SENSOR_TASK_PRIO   2

static QueueHandle_t xSensorQueue;

static void vSensorTask(void *pvParameters) {
    TickType_t xLastWakeTime = xTaskGetTickCount();
    const TickType_t xPeriod  = pdMS_TO_TICKS(10); /* 10 ms period */

    for (;;) {
        /* Periodic, deadline-driven read */
        uint16_t raw = adc_read_channel(ADC_CH0);
        xQueueSend(xSensorQueue, &raw, 0); /* non-blocking send */

        /* Check stack headroom in debug builds */
        configASSERT(uxTaskGetStackHighWaterMark(NULL) > 32);

        vTaskDelayUntil(&xLastWakeTime, xPeriod);
    }
}

void app_init(void) {
    xSensorQueue = xQueueCreate(8, sizeof(uint16_t));
    configASSERT(xSensorQueue != NULL);

    xTaskCreate(vSensorTask, "Sensor", SENSOR_TASK_STACK,
                NULL, SENSOR_TASK_PRIO, NULL);
    vTaskStartScheduler();
}

GPIO + Timer-Interrupt Blink (Bare-Metal STM32)

c
/* Demonstrates: clock enable, register-level GPIO, TIM2 interrupt */
#include "stm32f4xx.h"

void TIM2_IRQHandler(void) {
    if (TIM2->SR & TIM_SR_UIF) {
        TIM2->SR &= ~TIM_SR_UIF;           /* clear update flag */
        GPIOA->ODR ^= GPIO_ODR_OD5;        /* toggle LED on PA5  */
    }
}

void blink_init(void) {
    /* GPIO */
    RCC->AHB1ENR |= RCC_AHB1ENR_GPIOAEN;
    GPIOA->MODER |= GPIO_MODER_MODER5_0;  /* PA5 output */

    /* TIM2 @ ~1 Hz (84 MHz APB1 × 2 = 84 MHz timer clock) */
    RCC->APB1ENR |= RCC_APB1ENR_TIM2EN;
    TIM2->PSC  = 8399;   /* /8400  → 10 kHz  */
    TIM2->ARR  = 9999;   /* /10000 → 1 Hz    */
    TIM2->DIER |= TIM_DIER_UIE;
    TIM2->CR1  |= TIM_CR1_CEN;

    NVIC_SetPriority(TIM2_IRQn, 6);
    NVIC_EnableIRQ(TIM2_IRQn);
}

Output Templates

When implementing embedded features, provide:

  1. Hardware initialization code (clocks, peripherals, GPIO)
  2. Driver implementation (HAL layer, interrupt handlers)
  3. Application code (RTOS tasks or main loop)
  4. Resource usage summary (flash, RAM, power estimate)
  5. Brief explanation of timing and optimization decisions

Documentation

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Embedded Systems AI skill do?

Use when developing firmware for microcontrollers, implementing RTOS applications, or optimizing power consumption. Invoke for STM32, ESP32, FreeRTOS, bare-metal, power optimization, real-time systems, configure peripherals, write interrupt handlers, implement DMA transfers, debug timing issues.

Why use Embedded Systems on TypingMind?

Because you install it once and use it with any model. Embedded Systems 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 Systems in TypingMind?

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

Which AI models can use Embedded Systems?

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 Systems?

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

Is the Embedded Systems AI skill free?

Yes. It is published on GitHub by Jeffallan 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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