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Kernel Services

Community
beriberikix
kernel-services

Advanced Zephyr RTOS kernel services. Covers inter-thread communication using Zbus (pub/sub), behavioral management with the State Machine Framework (SMF), background processing via work queues, and persistent configuration with the Settings subsystem. Trigger when building modular application architectures, complex state-driven logic, or requiring persistent data storage.

Overview

Publisherberiberikix
Repositoryzephyr-agent-skills
Skill namekernel-services
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 Kernel Services 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/kernel-services
  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/kernel-services .claude/skills/kernel-services
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kernel Services 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 Kernel Services 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 Kernel Services 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 Kernel Services

Move beyond basic threading and logging to build modular, event-driven, and robust Zephyr applications.

Core Workflows

1. Event-Driven Communication (Zbus)

Decouple your modules using a lightweight publish-and-subscribe bus.

  • Reference: zbus.md
  • Key Tools: ZBUS_CHAN_DEFINE, ZBUS_SUBSCRIBER_DEFINE, zbus_chan_pub.

2. Behavioral Logic (SMF)

Manage complex system states and transitions using the State Machine Framework.

  • Reference: smf.md
  • Key Tools: smf_set_state, SMF_CREATE_STATE, Hierarchical states.

3. Background Processing (Work Queues)

Defer long-running or non-critical tasks to prevent blocking interrupts or high-priority threads.

4. Persistence (Settings)

Save and restore configuration data and state across reboots.

Quick Start (Zbus)

c
// Define a channel for sensor data
ZBUS_CHAN_DEFINE(sensor_data_chan, struct sensor_msg, NULL, NULL, ZBUS_OBSERVERS_EMPTY, ZBUS_CHAN_DEFAULTS);

// Publish from a thread
zbus_chan_pub(&sensor_data_chan, &msg, K_NO_WAIT);

Professional Patterns (Asset Tracker Style)

  • Modularity: Use Zbus as the backbone for inter-module communication.
  • Predictability: Use SMF to define clear lifecycle states for each module (e.g., Uninitialized -> Ready -> Active -> Error).
  • Responsiveness: Use custom work queues for sensor data ingestion to keep the main thread responsive for cloud communication.
  • Sensor Integration: For sensor data ingestion patterns, see the hardware-io skill.

Automation Tools

Examples & Templates

Validation Checklist

  • Zbus publishers and subscribers exchange messages without deadlock or missed updates.
  • SMF transitions follow expected state graph under normal and error conditions.
  • Deferred work executes on intended queue context with bounded execution time.
  • Settings values persist across reboot and reload successfully at startup.

Resources

  • References:
    • zbus.md: Publish/Subscribe patterns and subscriber types.
    • smf.md: Finite and Hierarchical state machine implementation.
    • settings_workqueue.md: Background work and persistent storage.
  • Scripts:
    • zbus_channel_lint.py: Zbus channel name collision checker.
  • Assets:
    • smf_state_table_template.c: State-machine 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 Kernel Services AI skill do?

Advanced Zephyr RTOS kernel services. Covers inter-thread communication using Zbus (pub/sub), behavioral management with the State Machine Framework (SMF), background processing via work queues, and persistent configuration with the Settings subsystem. Trigger when building modular application architectures, complex state-driven logic, or requiring persistent data storage.

Why use Kernel Services on TypingMind?

Because you install it once and use it with any model. Kernel Services 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 Kernel Services in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/beriberikix/zephyr-agent-skills/tree/main/skills/kernel-services. 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 Kernel Services?

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 Kernel Services?

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

Is the Kernel Services 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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