Hr Iot

Community

tuanductran

hr-iot

Help HR and recruiting teams hire and assess Internet of Things (IoT) engineering candidates, spanning device firmware, connectivity, and cloud/edge integration. Use when asked to write an IoT engineer job description, create IoT interview questions, assess an IoT candidates skills across the stack, build an IoT engineering leveling framework, or similar IoT-hiring tasks.

Overview

Publisher	tuanductran
Repository	`hr-skills`
Skill name	`hr-iot`
Stars	78
Forks	20
Bundled files	3
License	MIT
Links	Skill source Repository Homepage

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.
3 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by tuanductran on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/tuanductran/hr-skills/tree/main/skills/hr-iot
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.

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/tuanductran/hr-skills.git /tmp/hr-skills
mkdir -p .claude/skills
cp -r /tmp/hr-skills/skills/hr-iot .claude/skills/hr-iot

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hr 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 Hr 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 Hr 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.

HR IoT hiring

Helps HR and technical recruiters hire IoT engineers who work across device firmware, connectivity protocols, and cloud/edge integration — a role that spans multiple technical layers more than most single-discipline engineering roles.

Supported tasks

Writing job descriptions for IoT engineer roles spanning device, connectivity, and cloud layers
Building a leveling framework for IoT engineering roles
Creating screening questions distinguishing full-stack IoT experience from single-layer specialization
Designing interview exercises covering connectivity protocols (MQTT, BLE, LoRaWAN, Zigbee)
Drafting interview questions on device provisioning, OTA updates, and fleet management
Building a glossary of IoT terminology for non-technical recruiters
Creating a skills matrix mapping candidates across device, connectivity, and cloud layers
Drafting reference-check questions focused on shipped connected-device products
Designing a sourcing strategy for niche IoT specialties (industrial IoT, smart home, wearables)
Building compensation benchmarking guidance for IoT engineering roles
Creating onboarding plans for new IoT engineering hires
Auditing job postings for unrealistic full-stack IoT skill expectations

Key prompts

Job descriptions and leveling

"Write a job description for an IoT engineer role spanning device firmware and cloud connectivity."
"Build a leveling framework distinguishing junior, mid, and senior IoT engineers."
"Explain the difference between an IoT engineer and an embedded firmware engineer for a recruiter."
"Create a glossary of IoT terms (MQTT, BLE, edge computing, OTA) for non-technical recruiters."
"Draft a job description for an industrial IoT engineer role, distinct from consumer smart-home IoT."

Screening and technical evaluation

"Create screening questions distinguishing full-stack IoT experience from single-layer (device-only or cloud-only) specialization."
"Draft interview questions covering connectivity protocol trade-offs (MQTT vs. LoRaWAN vs. BLE) for different use cases."
"Design an interview exercise covering device provisioning and fleet management at scale."
"Build a skills matrix mapping a candidate's experience across device, connectivity, and cloud layers."
"Draft interview questions about OTA firmware update strategy and rollback safety."

Sourcing and evaluation

"Draft a sourcing strategy for finding engineers with industrial IoT or smart-home experience."
"Draft reference-check questions focused on a candidate's role in a shipped connected-device product."
"Create compensation benchmarking guidance for IoT engineering roles by specialization and region."
"Draft onboarding guidance for a new IoT engineering hire covering device lab and cloud access setup."
"Audit this job posting for unrealistic expectations of full-stack mastery across every IoT layer."

Tips

Map candidates against the specific layers (device, connectivity, cloud/edge) the role actually requires rather than expecting mastery of all three.
Weight fleet-scale operational experience (OTA updates, device provisioning at scale) heavily for production IoT roles.
Be specific about the connectivity protocols and use case (industrial vs. consumer) in job postings.
Verify claimed IoT experience against shipped, real-world connected-device products where possible — IoT is a broad label and interview depth on hardware constraints, protocol choices, and production deployment reveals genuine expertise.

Common mistakes

Writing job postings expecting deep expertise across device, connectivity, and cloud layers simultaneously.
Underweighting operational, fleet-scale concerns (OTA safety, device provisioning) in favor of prototype-level skills.
Treating IoT experience as interchangeable across very different domains (industrial vs. consumer vs. wearables).
Failing to distinguish device-layer firmware skills from cloud/backend integration skills in screening.

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 Hr Iot AI skill do?

Why use Hr Iot on TypingMind?

Because you install it once and use it with any model. Hr 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 Hr Iot in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tuanductran/hr-skills/tree/main/skills/hr-iot. 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 Hr 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 Hr Iot?

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

Is the Hr Iot AI skill free?

Yes. It is published on GitHub by tuanductran 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

HR IoT hiring

Supported tasks

Key prompts

Job descriptions and leveling

Screening and technical evaluation

Sourcing and evaluation

Tips

Common mistakes

Bundled files

Frequently asked questions

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