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Hardware Io

Community
beriberikix
hardware-io

Hardware interfacing and peripheral management for Zephyr RTOS. Covers the sensor subsystem (channels, triggers, fetch/get), pin control (Pinctrl) and multiplexing, GPIO management using Devicetree specs, and SoC-level hardware configurations. Trigger when adding new hardware components, configuring pinmux, or developing sensor-based applications.

Overview

Publisherberiberikix
Repositoryzephyr-agent-skills
Skill namehardware-io
Stars
73
Forks
19
Bundled files
6
LicenseApache-2.0
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.

  • 6 bundled files

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

  • Open source

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

Installation

Install the Hardware Io 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.
    https://github.com/beriberikix/zephyr-agent-skills/tree/main/skills/hardware-io
  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/beriberikix/zephyr-agent-skills.git /tmp/zephyr-agent-skills
mkdir -p .claude/skills
cp -r /tmp/zephyr-agent-skills/skills/hardware-io .claude/skills/hardware-io
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hardware Io 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 Hardware Io 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 Hardware Io 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.

Zephyr Hardware I/O

Interface with the physical world using Zephyr's standardized driver models and hardware abstraction layers.

Core Workflows

1. Sensor Subsystem

Interact with various sensors using a uniform API for data fetching and decoding.

  • Reference: sensors.md
  • Key Tools: sensor_sample_fetch, sensor_channel_get, struct sensor_value.

2. Pinctrl & GPIO

Manage pin multiplexing, electrical configuration, and basic digital input/output.

  • Reference: pinctrl_gpio.md
  • Key Tools: pinctrl, gpio_dt_spec, GPIO_DT_SPEC_GET.

3. SoC Configuration

Tune chip-level parameters and manage hardware across multiple board variants.

  • Reference: soc_config.md
  • Key Tools: Kconfig, soc_common.dtsi, SoC-level overlays.

Quick Start (Sensor Polling)

c
#include <zephyr/drivers/sensor.h>

const struct device *temp_sensor = DEVICE_DT_GET(DT_ALIAS(ambient_temp0));
struct sensor_value val;

void poll_sensor(void) {
    if (sensor_sample_fetch(temp_sensor) == 0) {
        sensor_channel_get(temp_sensor, SENSOR_CHAN_AMBIENT_TEMP, &val);
    }
}

Professional Patterns (Hardware Engineering)

  • Safe GPIO: Always use gpio_dt_spec to ensure polarity and pin number are automatically handled by the driver.
  • Background Sampling: Never poll sensors in high-priority threads; use a background work queue. See kernel-services for work-queue patterns.
  • Deferred Pinctrl: Define pin states in a shared .dtsi to simplify multi-revision hardware support.

Automation Tools

Examples & Templates

Validation Checklist

  • Sensor device nodes resolve correctly and DEVICE_DT_GET(...) targets are ready at runtime.
  • GPIO and pinctrl states match the intended board schematic and polarity rules.
  • Sensor sampling path returns stable values with expected units/scaling.
  • Background acquisition does not block high-priority or interrupt-critical code paths.

Resources

  • References:
    • sensors.md: Reading data, channels, and triggers.
    • pinctrl_gpio.md: Pin multiplexing and GPIO specs.
    • soc_config.md: Multi-variant SoC configuration.
  • Scripts:
    • gpio_alias_check.py: Alias duplication checker for DTS/overlay sets.
  • Assets:
    • sensor_poll_template.c: Polling loop starter template.

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 Hardware Io AI skill do?

Hardware interfacing and peripheral management for Zephyr RTOS. Covers the sensor subsystem (channels, triggers, fetch/get), pin control (Pinctrl) and multiplexing, GPIO management using Devicetree specs, and SoC-level hardware configurations. Trigger when adding new hardware components, configuring pinmux, or developing sensor-based applications.

Why use Hardware Io on TypingMind?

Because you install it once and use it with any model. Hardware Io 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 Hardware Io in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/beriberikix/zephyr-agent-skills/tree/main/skills/hardware-io. 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 Hardware Io?

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 Hardware Io?

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

Is the Hardware Io AI skill free?

Yes. It is published on GitHub by beriberikix under the Apache-2.0 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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