Embedded / IoT Mentor — a Claude Skill

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A skill for Claude that acts as an experienced embedded-systems mentor: it picks the microcontroller, board, and toolchain for your project, estimates what it will cost and how long it will take, and hands you a build plan that stops at a working breadboard instead of a production line you didn't ask for.

Most embedded advice fails in one of two directions — a parts list with no plan, or a production roadmap for someone who hasn't blinked an LED yet. This skill asks what you've actually built before, then answers at that level.

Try it

Where	One click
Your browser	iotmentor.dev — a web app by the same author, built on the same principles but with its own code. Nothing to install
Claude	Download embedded-iot-mentor.skill and open it
VS Code (Copilot Chat)	Copy vscode-copilot/'s prompt in — nothing to install
Your own code	then call the REST API — routes
Any other AI chat	Copy universal-prompt.md into the first message — no install, works in ChatGPT, Gemini, Copilot, and others

Prefer a page over a chat? docs/index.html is a static overview with the same "Try it" links and demo — live at mh-mansouri.github.io/embedded-iot-mentor, or open the file locally, no server needed.

Everything below is the longer way round: build it yourself, change it, or read why it answers the way it does.

Demo

A farmer asks for soil moisture and nitrogen across a meadow; the skill declines the nitrogen half, asks two questions, and returns a six-node LoRa plan with no code in it

A sheep farmer in Devon, with no coding experience, requests six sensing points, and the furthest sensing point is around 400 m away from the house. All of those are below the minimum cost for such a project. Worth watching for what the skill doesn't suggest: It opens by refusing half the request - no cheap probe measures soil nitrogen honestly - then lets three constraints do the choosing. The 400 meters away from home picks radio over Wi-Fi, "I don't write code" picks ready-made firmware over a toolchain, and a wet meadow picks the enclosure. The board is the last thing decided, not the first. The full transcript is Scenario D.

What it does

• Picks a platform — ESP32, Pico, STM32, nRF52 — and says plainly why that one, plus one or two alternatives and when each would win instead.
• Separates the hardware path from the firmware path, so you know what to buy and what to install without conflating them.
• Checks whether you need to write firmware at all. If ESPHome, Meshtastic or Tasmota already does the job, that's the answer — writing code is a cost, not a deliverable.
• Takes the reading all the way to a person — Home Assistant, a page the device serves itself, a hosted dashboard, or just an alert. "On my phone" in the kitchen and "on my phone" from work are two different builds, and it says so before you pick one.
• Estimates time and cost as ranges, and flags what actually drives them — including what the thing costs to run, once it's six nodes eating batteries in a field.
• Says what a sensor really measures. Cheap "NPK" probes read conductivity and guess; you get told that before you buy six of them, not after.
• Plans to MVP and stops there. Engineering prototype, pre-production, and production phases exist, but you only get them when you ask.
• Names the risks — power budget, part availability, no debug path, certification, the learning curve on whatever it just recommended.
• Rejects its own suggestions against a fixed bar: no maintained library, single-supplier part, a package you can't solder, no serial console — it drops the candidate and picks again.

Why it exists

The failure modes it's built to catch:

• A beginner pointed at an STM32 with an ST-Link because a forum said it was "more professional" — three evenings lost to toolchain setup before the first LED.
• A battery project designed around a dev board whose regulator idles at 20 mA, so the "two month" runtime is really four days. The board was never the problem; nobody costed the sleep current.
• A first PCB ordered with 0402 passives and a QFN, hand-assembled with a soldering iron, and dead on arrival with no test points to find out why.
• Six sensors deployed in a field in indoor boxes, sealed with tape instead of cable glands, condensing on their own PCBs by the second week.

Install

Option A — one file. Download embedded-iot-mentor.skill from the latest release (or straight from the repository), then in claude.ai:

1. Click your name in the bottom-left corner, choose Settings, then open the Skills page under Customize:

   

2. Click Add, then Upload a skill:

   

3. Drag the downloaded embedded-iot-mentor.skill file onto the upload box (or click it to browse):

   

(Skill saving must be enabled for your account or organization.)

Option B — Claude Code. Unpack it into your skills directory:

python package_skill.py --install                                     # for your user
python package_skill.py --install --skills-dir <repo>/.claude/skills  # for one project

Or install a bundle you already have, with no copy of this repo:

python package_skill.py --install-from embedded-iot-mentor.skill

Or by hand — a .skill is just a zip:

mkdir -p ~/.claude/skills && unzip embedded-iot-mentor.skill -d ~/.claude/skills/

# Windows: Expand-Archive refuses any extension but .zip, so rename a copy first
New-Item -ItemType Directory -Force "$HOME\.claude\skills" | Out-Null
Copy-Item embedded-iot-mentor.skill "$env:TEMP\embedded-iot-mentor.zip"
Expand-Archive "$env:TEMP\embedded-iot-mentor.zip" -DestinationPath "$HOME\.claude\skills" -Force

Claude Code picks it up on the next session — /skills lists it, and Claude also loads it on its own when a conversation matches the description.

Use it

Just describe the project. For example:

I want to log soil moisture in a greenhouse and see it on my phone. I've done a couple of Arduino sketches. Budget maybe €100, and I'd like it running in a month.

or

Which board for a battery sensor that has to last a year on a coin cell? I've shipped firmware before, so don't dumb it down.

or

I have an ESP32 and a BME280 sitting in a drawer. What's worth building with them?

or, the one in the demo above:

I am a farmer and want to measure soil moisture and nitrogen in different parts of my meadow to make sure my sheep are well fed.

It will ask a couple of short questions if the goal, experience level, power source, environment, or timeline are still unclear — then answer in tables rather than essays. A whole project plan is meant to fit on one screen; if you want the reasoning behind a pick, ask for it.

Elsewhere: VS Code and the REST API

The mentor is judgement written down, not a Claude feature, so it ports. Every port keeps the behaviour that matters — MVP first, hardware and firmware kept apart, ready-made firmware ahead of code to be written, the reject bar, and the hand-off on safety-critical, vehicle, and privacy questions.

What the two carry differs. The Copilot port is judgement only — no reference files, no scripts, so a real battery runtime or a BOM total is still the skill's job. The REST API reads both straight out of the skill folder, so it can never fall behind a change made here.

Good to know

• Prices and stock go stale. Estimates are ranges, not quotes. Check LCSC, Digi-Key, or your local supplier before ordering.
• It cannot verify part availability in your country, and that is the most common reason a good plan stalls.
• It stops at MVP by design. Ask explicitly for the later phases.
• Not for safety-critical work. It will help you to a prototype for medical, automotive, or safety systems, then tell you plainly where hobbyist advice ends.

Layout

The skill itself lives in embedded-iot-mentor/. Everything at the repository root is packaging and project metadata that the skill never reads.

Keeping the skill in its own folder matters: the spec requires a skill's name to match its folder name, so building it straight from the repository root would break the moment someone downloaded the repo as a ZIP and got embedded-iot-mentor-main/.

Build

python package_skill.py          # -> ./embedded-iot-mentor.skill
python package_skill.py --check  # validate source + bundle, build nothing

A .skill file is a zip archive holding the skill folder — the format is defined by the Agent Skills specification. The packer bundles everything under embedded-iot-mentor/, so a new reference file is picked up automatically with no build-script edit. Text files are stored with LF and zip timestamps are pinned, so the bundle is byte-identical whoever builds it.

--check is the gate, and CI runs it on every push and pull request. It fails when:

• the frontmatter breaks a spec constraint (name pattern/length, folder match, description length);
• SKILL.md points at a references/… or scripts/… file that doesn't exist;
• the committed .skill doesn't match the source folder.

That last one matters because the bundle is committed: edit the skill, forget to rebuild, and the download would ship a different version than the source folder.

Scripts

python embedded-iot-mentor/scripts/cost_estimator.py 1 4.50 "ESP32 DevKit" 10 0.12 "10k resistor"
python embedded-iot-mentor/scripts/footprint_hint.py 0603
python embedded-iot-mentor/scripts/sleep_budget.py --capacity 2000 --active-ma 80 \
    --active-ms 250 --sleep-ua 15 --interval-s 600

sleep_budget.py takes duty-cycle inputs rather than an average current, because the average is the number nobody knows up front. Same firmware, same battery, sleep current changed from 15 µA to a dev board's 8 mA regulator: 3.8 years becomes 8.3 days.

More skills like this

• Project Planning & Journaling — scopes a project before any code is written, then keeps a living, resumable documentation journal with a weekly routine review.
• Business Name Fit — picks or checks a business/brand name that's authentic to your origin and lands well in the markets you're selling into.

Contributing

Improvements are welcome — especially hands-on knowledge about parts, suppliers, and what actually goes wrong on a bench. See CONTRIBUTING.md.

License

Released under the MIT License — free to use, share, and build on.