Provides modular protocol stacks for industrial communication like Modbus RTU/TCP and CANopen in Zephyr.

Install

mkdir -p .claude/skills/industrial && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/10994" && unzip -o skill.zip -d .claude/skills/industrial && rm skill.zip

Installs to .claude/skills/industrial

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Industrial communication protocols for Zephyr RTOS. Covers Modbus RTU (serial), Modbus TCP (Ethernet/Wi-Fi), and CANopen basics. Trigger when building factory automation controllers, industrial sensors, or medical equipment interfaces.
235 chars✓ has a “when” trigger
Intermediate

Key capabilities

  • Implement Modbus RTU serial communication
  • Bridge industrial data via Modbus TCP
  • Integrate CANopen automation networks
  • Validate register maps with linting scripts

How it works

It use Zephyr's modular protocol stacks to handle serial and network-based industrial communication.

Inputs & outputs

You give it
Register map CSV
You get back
Initialized industrial protocol interface

When to use industrial

  • Implementing Modbus RTU for sensors
  • Bridging industrial data via Modbus TCP
  • Integrating CANopen automation networks

About this skill

Zephyr Industrial Protocols

Build robust, industry-standard communication systems using Zephyr's modular industrial protocol stacks.

Core Workflows

1. Modbus RTU (Serial)

Implement serial-based industrial communication for meters, PLCs, and sensors.

  • Reference: modbus_rtu.md
  • Key Tools: CONFIG_MODBUS, RS-485 DE/RE handling, Register Mapping.

2. Modbus TCP

Bridge industrial data over standard Ethernet or Wi-Fi networks.

  • Reference: modbus_tcp.md
  • Key Tools: Port 502, TCP/IP networking, Client/Server patterns.

3. CANopen Basics

Integrate with complex automation networks using the CANopenNode stack.

  • Reference: canopen_basics.md
  • Key Tools: Object Dictionary (OD), PDO/SDO, Network Management (NMT).

Quick Start (Modbus RTU Server)

# prj.conf
CONFIG_MODBUS=y
CONFIG_MODBUS_SERIAL=y
// Initialize a server on a serial device
const struct device *dev = DEVICE_DT_GET(DT_CHOSEN(zephyr_modbus_serial));
struct modbus_iface_param param = {
    .mode = MODBUS_MODE_RTU,
    .server = { .node_addr = 1, .cb = &my_callbacks },
    .serial = { .baud = 115200, .parity = UART_CFG_PARITY_NONE },
};
modbus_init_server(dev, param);

Professional Patterns (Reliability & Safety)

  • RS-485 Hardware Handling: Always use the devicetree uart-rs485 property to handle transceiver direction signals automatically at the driver level.
  • Isolated Communication: Use galvanically isolated transceivers for both serial and CAN lines in factory environments to prevent damage from ground loops.
  • Watchdog Integration: In industrial control, always pair your communication loops with the watchdog_reliability pattern (see specialized skill) to ensure the system enters a fail-safe state on protocol lockup.

Automation Tools

Examples & Templates

Validation Checklist

  • Modbus RTU request/response exchange succeeds against a known test slave/master.
  • Modbus TCP endpoint responds on port 502 with correct register mappings.
  • CANopen node transitions through expected NMT states during startup.
  • Communication faults trigger safe retry or watchdog-protected recovery behavior.

Resources

  • References:
    • modbus_rtu.md: Serial Modbus master/slave setup.
    • modbus_tcp.md: Ethernet Modbus client/server patterns.
    • canopen_basics.md: Object Dictionary and PDO mapping.
  • Scripts:
    • modbus_register_lint.py: Register-map consistency checker.
  • Assets:
    • modbus_register_map_template.csv: Register planning template.

When not to use it

  • Non-industrial communication tasks

Prerequisites

Zephyr RTOSRS-485 hardwareCANopenNode stack

Limitations

  • Requires specific hardware support for RS-485
  • Limited to supported industrial protocols

How it compares

Unlike generic serial libraries, this provides specific industrial protocol implementations and hardware-level transceiver handling.

Compared to similar skills

industrial side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
industrial (this skill)02moReviewIntermediate
telegram-bot-builder1066moReviewIntermediate
workflow-orchestration-patterns102moNo flagsAdvanced
bullmq-specialist256moNo flagsIntermediate

Try saying

Example prompts that trigger this skill in your AI assistant.

You might also like

telegram-bot-builder

davila7

Expert in building Telegram bots that solve real problems - from simple automation to complex AI-powered bots. Covers bot architecture, the Telegram Bot API, user experience, monetization strategies, and scaling bots to thousands of users. Use when: telegram bot, bot api, telegram automation, chat bot telegram, tg bot.

106130

workflow-orchestration-patterns

wshobson

Design durable workflows with Temporal for distributed systems. Covers workflow vs activity separation, saga patterns, state management, and determinism constraints. Use when building long-running processes, distributed transactions, or microservice orchestration.

10117

bullmq-specialist

davila7

BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.

2595

unity-mcp-orchestrator

CoplayDev

Orchestrate Unity Editor via MCP (Model Context Protocol) tools and resources. Use when working with Unity projects through MCP for Unity - creating/modifying GameObjects, editing scripts, managing scenes, running tests, or any Unity Editor automation. Provides best practices, tool schemas, and workflow patterns for effective Unity-MCP integration.

1795

async-python-patterns

wshobson

Master Python asyncio, concurrent programming, and async/await patterns for high-performance applications. Use when building async APIs, concurrent systems, or I/O-bound applications requiring non-blocking operations.

1299

modal

davila7

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

587

Search skills

Search the agent skills registry