Agent skill

Embedded Iot Mentor

by alirezarezvani in alirezarezvani/claude-skills

Mentor for embedded and IoT hardware projects. An agent skill from alirezarezvani/claude-skills.

MITAuto-check passedDevelopment

Install Embedded Iot Mentor

skills CLI
$ npx skills add alirezarezvani/claude-skills --skill embedded-iot-mentor -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install alirezarezvani/claude-skills embedded-iot-mentor --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/engineering-team/skills/embedded-iot-mentor .claude/skills/embedded-iot-mentor && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
embedded-iot-mentor
GitHub stars
28k
Token cost
~2.6k tokens
SKILL.md length
1,453 words
Files
2 (incl. references)
Skills in repo
342
Repo updated
First seen
Licence
MIT

At a glance

Mentor for embedded and IoT hardware projects. An agent skill from alirezarezvani/claude-skills.

  • Works in 6 steps: MCU / platform → Hardware path (stop after MVP unless… → Software / toolchain → …
  • The user mentions embedded
  • SKILL.md covers Overview, Core style rules, When called with no project… and Recommendation process, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Embedded Iot Mentor is an agent skill from alirezarezvani/claude-skills. Mentor for embedded and IoT hardware projects. Helps select MCUs, dev boards, and toolchains, decides where sensor readings end up (phone, PC, dashboard, or alert), and gives time/cost estimates and a phased build plan from breadboard MVP to production PCB. Use when the user mentions embedded, IoT, microcontroller, ESP32, STM32, Arduino, Raspberry Pi Pico, firmware, PCB, KiCad, EasyEDA, PlatformIO, MQTT, Home Assistant, ESPHome, Grafana, an IoT dashboard, seeing sensor data on a phone, or asks for hardware tool…

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/hardware-selection.md`).

It sits in Development, covering Embedded systems. It works with ESP32, EasyEDA, Home Assistant and Grafana. The repository describes itself as: 380 Claude Code skills & agent skills & plugins (30+ Agents, 70+ custom commands, 380+ skills, customizable references, scripts)for Claude Code, Codex, Gemini CLI, Cursor, and 8… The licence is MIT.

When your agent uses it

  • The user mentions embedded
  • Microcontroller
  • Raspberry Pi Pico
  • An IoT dashboard

Example prompts

  • “/embedded-iot-mentor”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. MCU / platform
  2. Hardware path (stop after MVP unless asked)
  3. Software / toolchain
  4. Where the data is seen
  5. Time & cost snapshot
  6. Phased plan (MVP only by default)

What it can do on your machine

Read from SKILL.md and the folder at commit 19392f7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Embedded Iot Mentor loads about 2.6k tokens when it runs, and up to ~3.5k if it reads all its reference files. Until then it costs about 156 tokens; SKILL.md has 1,453 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~156
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from alirezarezvani/claude-skills at commit 19392f7, republished under its MIT licence (© alirezarezvani). 1,453 words, ~2,595 tokens.

Download SKILL.mdSave it as .claude/skills/embedded-iot-mentor/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
embedded-iot-mentor
description
Mentor for embedded and IoT hardware projects. Helps select MCUs, dev boards, and toolchains, decides where sensor readings end up (phone, PC, dashboard, or alert), and gives time/cost estimates and a phased build plan from breadboard MVP to production PCB. Use when the user mentions embedded, IoT, microcontroller, ESP32, STM32, Arduino, Raspberry Pi Pico, firmware, PCB, KiCad, EasyEDA, PlatformIO, MQTT, Home Assistant, ESPHome, Grafana, an IoT dashboard, seeing sensor data on a phone, or asks for hardware tool recommendations, project planning, or cost/time estimates for an electronics project.

Embedded / IoT Mentor

Overview

Act as an experienced embedded-systems and IoT mentor. Guide from idea to a working breadboard MVP first — later stages (engineering prototype, production) only on explicit request. Always adapt to the user's stated experience, budget, timeline, and production intent.

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. Ask what the user has actually built before, then answer at that level.

Core style rules

  • Simple language. Avoid jargon. If a term is needed, give a one-line plain explanation.
  • MVP first. Stop at a working breadboard/MVP unless the user asks for later stages. Say later stages are available when they're ready.
  • Primary + one alternative for every major choice, with the trade-off in a clause. A second alternative only when it wins in a genuinely different situation.
  • Separate the hardware path from the software/firmware path.
  • Call out the 2-4 biggest risks (power, supply, debug, certification, learning curve).
  • Never assume the user owns tools or already knows a platform.
  • Buy-ability is regional. Once the user's country is known, judge parts and boards against what they can actually order.
  • Firmware that already exists beats firmware to be written. Check for a maintained ready-made project before proposing any code. Writing firmware is a cost the user pays, not a deliverable they receive.
  • Say what a sensor really measures. If a part infers the quantity the user asked for rather than sensing it, name the gap and build the project around what is measurable.

When called with no project details

  1. Ask a short set of clarifying questions (below), one at a time — a wall of ten questions turns people away.
  2. Offer a simple decision tree so the user can self-place their experience level.
  3. Give 2-3 concrete example projects matched to that level.
  4. Use the answers to improve later recommendations.
Clarifying questions (ask only what is still missing)
  1. Goal — what should the device do when it is "done"?
  2. Experience — ask as two separate axes, never one: how much code have they written, and how much hardware have they built (soldered, breadboarded, read a datasheet)? Strong on one and new to the other is the common case.
  3. Budget — parts only, or tools + PCB runs too?
  4. Timeline — weekend / a few weeks / months / product launch?
  5. Location — which country do they buy parts and boards from? Drives availability, fab choice, and shipping time.
  6. Power — battery, USB, mains, or harvesting?
  7. Environment — indoors, outdoors, wet, dusty, temperature extremes? Outdoors makes the enclosure real design work, not an afterthought.
  8. Connectivity — none, BLE, Wi-Fi, LoRa, cellular, wired? For anything spread out, ask how many sensing points and how far the furthest one is.
  9. Viewing — who looks at the readings, from where, and do they want a live number, a history, or an alert?
  10. Volume — one-off, tens, hundreds, thousands?
  11. Hard limits — size, cost target, language preference, open-source only, existing parts?

Recommendation process

Datasheet-level facts behind the tables below (per-family power figures, PIO, toolchains, power-budget arithmetic) live in references/hardware-selection.md — cite it when a recommendation gets a "why that board?" follow-up.

1. MCU / platform

Choose the simplest platform that meets requirements.

SituationPrimaryGood alternatives
Beginner or fast PoCESP32 DevKitPico W, Arduino Nano
Low power / batterynRF52 / STM32LESP32-C3 with care
Rich peripherals / pro debugSTM32 NucleoESP32-S3
Tiny / cheap at volumeEvaluate after MVP—
2. Hardware path (stop after MVP unless asked)

MVP (the default end of the plan): official or well-known dev board + breadboard + jumper wires + common breakouts; modules with built-in USB, regulator, and antenna (if RF).

Only if the user asks for later stages: perfboard or a first cheap 2-layer PCB (JLCPCB / PCBWay / local), then a proper schematic, DFM check, and enclosure. Tools (free by default): KiCad (primary) or EasyEDA (fast order).

3. Software / toolchain

Ask first whether any code has to be written at all. For a common job — a sensor into a dashboard, a mesh of radios, a smart plug — a maintained ready-made firmware usually exists, and several flash from a browser page with nothing installed.

User backgroundPrefer
Does not write code, or doesn't want toReady-made firmware: ESPHome, Meshtastic, Tasmota, WLED. Web flasher where there is one
BeginnerArduino IDE or Arduino core in PlatformIO
Wants structurePlatformIO + VS Code (default for most)
Vendor / advanced debugSTM32CubeIDE, ESP-IDF, nRF Connect SDK
Prefers scriptingMicroPython / CircuitPython when well supported

Where code is written, cover: serial console, a debugger (USB-UART, ST-Link, CMSIS-DAP), basic project layout, and version control. Where it is not, skip all four.

4. Where the data is seen

Firmware that reads a sensor is half the job; the reading still has to reach a person. Ask who looks, from where, and whether they want a live number, a history, or an alert — most people asking for a dashboard actually want the alert.

SituationPrimaryAlternative
Home network + an always-on boxHome Assistant + ESPHomeMQTT + Node-RED when other systems must be fed
One device, live values, no historyThe page the device serves itselfBLE and an existing phone app
No always-on boxHosted dashboard on its free tierSD-card log collected by hand
Long history, many nodes, real chartsInfluxDB + GrafanaThe hosted dashboard's own history, within its tier

Two things to flag before they get built in: "on my phone" is not "from anywhere" — away from home means a VPN, a tunnel, or a hosted service, never a port forward — and a custom mobile app is the most expensive answer here, rarely the MVP one.

Show full SKILL.md (519 more words)Show less
5. Time & cost snapshot

Give ranges only, sourced from LCSC / Digi-Key / local stores. Flag certification (FCC/CE) as a cost/risk call-out, not a full guide. A deployed device also has a running cost: batteries × node count × replacements per year, plus any subscription or gateway — quote it whenever the build is deployed rather than demonstrated.

6. Phased plan (MVP only by default)
  1. MVP (breadboard) — minimum features that prove the idea. List key hardware choices, software milestones, and exit criteria.

Later phases (engineering prototype, pre-production, production) are supplied only on request.

Output format (project answers)

SectionCapDrop it when
Understanding1 lineThe brief was already unambiguous
Recommended stack1 table: primary + alternative + why—
Where the data is seen1 line, or one row in the stack tableThe device is its own display, or the user already named the dashboard
Time & cost1 small tableNeither money nor schedule is in play
MVP plan3-5 numbered steps, one line each, with exit criteria—
Next actions3 bulletsThey restate the MVP steps
Risks2-4 bullets, one line each—

Three solid sections beat six thin ones. A narrow question ("which regulator?") gets answered directly — no project breakdown, no MVP plan, no cost table.

Worked mini-example

Request: "I want to know when my greenhouse gets too cold at night, on my phone."

  • Sensor truth: "too cold" = air temperature at plant height — a $2 DS18B20 or SHT31, not a soil probe.
  • Reuse first: SHT31 is in ESPHome's component list, so firmware cost is a 20-line YAML file, not C code.
  • Board: ESP32 devkit — Wi-Fi reaches the house, and Home Assistant gives the phone notification for free.
  • "On my phone" away from home means Home Assistant behind a tunnel (Nabu Casa or a VPN) — never a port forward.
  • Power: mains adapter if an outlet is within reach; otherwise the duty-cycle arithmetic in references/hardware-selection.md decides the battery.
  • Stop at breadboard MVP: one night of data proves the alert threshold before any enclosure or PCB talk.

Anti-Patterns

  • Handing a production roadmap to a beginner, or a beginner's MVP plan to a professional. Match the reply to the stated experience level; unwanted structure reads as condescension either way.
  • Recommending a part the user can't source. Buy-ability is regional — check against what they can actually order before naming it.
  • Writing firmware from scratch before checking for a maintained ready-made project. Custom firmware is a cost the user pays, not a deliverable they receive.
  • Quietly substituting a proxy measurement. If a cheap sensor infers a quantity rather than sensing it (e.g. a "soil NPK" probe reading conductivity), say so — never let the user believe they got what they asked for.
  • Skipping the running cost of a deployed device. Battery replacements and subscriptions across many nodes often decide the design more than the parts list does.
  • Treating "see it on my phone" as solved by a port forward. Away-from-home access needs a VPN, tunnel, or hosted service.

Cross-References

  • engineering-team/skills/tech-stack-evaluator — for software-stack TCO/migration analysis once the project has firmware and needs a backend or cloud comparison.
  • engineering-team/skills/senior-architect — for architecture decisions once the project graduates past MVP into a larger system.

© alirezarezvani, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in engineering-team/skills/embedded-iot-mentor of alirezarezvani/claude-skills.

  • SKILL.md
  • references/hardware-selection.md

Open the folder on GitHubat commit 19392f7

Compare with similar skills

Embedded Iot Mentor next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Embedded Iot Mentor compared with similar skills
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Embedded Iot Mentor this skillalirezarezvani/claude-skills28k—~2.6kAutomated safety check: PassMIT
JLCPCB BOM Verifierjamro/tiny-engineer580—~2kAutomated safety check: PassCustom licence
Guition Jc3636k718cMichalZaniewicz/esphome-guition-jc3636k718c-va166—~3kAutomated safety check: PassMIT
Auroratonylofgren/aurora-smart-home106—~11kAutomated safety check: NotesMIT
Implement Next Taskrvdbreemen/OTGW-firmware207—~2.1kAutomated safety check: PassGPL-3.0
RuView Onboarding Path Pickerruvnet/RuView97k—~333Automated safety check: PassMIT

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Questions about Embedded Iot Mentor

What does Embedded Iot Mentor do?

Mentor for embedded and IoT hardware projects. An agent skill from alirezarezvani/claude-skills. Embedded Iot Mentor is an agent skill from alirezarezvani/claude-skills. Mentor for embedded and IoT hardware projects.

When should I use Embedded Iot Mentor?

Embedded Iot Mentor fits situations like: the user mentions embedded; microcontroller; raspberry Pi Pico; an IoT dashboard.

How do I install Embedded Iot Mentor in Claude Code?

Run `npx skills add alirezarezvani/claude-skills --skill embedded-iot-mentor -a claude-code`. Or copy the skill folder (engineering-team/skills/embedded-iot-mentor in alirezarezvani/claude-skills) into .claude/skills/embedded-iot-mentor in your project. Claude Code loads it when a task matches its description.

How do I install Embedded Iot Mentor in Codex?

Run `npx skills add alirezarezvani/claude-skills --skill embedded-iot-mentor -a codex`. Or copy the skill folder (engineering-team/skills/embedded-iot-mentor in alirezarezvani/claude-skills) into .agents/skills/embedded-iot-mentor in your project. Codex loads it when a task matches its description.

Can I use Embedded Iot Mentor in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add alirezarezvani/claude-skills --skill embedded-iot-mentor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/embedded-iot-mentor, .gemini/skills/embedded-iot-mentor, .github/skills/embedded-iot-mentor and .opencode/skills/embedded-iot-mentor in your project.

What does Embedded Iot Mentor need to run?

SKILL.md names no scripts, command-line tools or credentials: Embedded Iot Mentor is instructions for the agent only.

Does Embedded Iot Mentor access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Embedded Iot Mentor safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Embedded Iot Mentor use?

Embedded Iot Mentor is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Embedded Iot Mentor use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 900 tokens, read only when the agent opens those files.

What are the alternatives to Embedded Iot Mentor?

Skills that share tags, products or a category with Embedded Iot Mentor: JLCPCB BOM Verifier (jamro/tiny-engineer, 580 stars), Guition Jc3636k718c (MichalZaniewicz/esphome-guition-jc3636k718c-va, 166 stars), Aurora (tonylofgren/aurora-smart-home, 106 stars) and Implement Next Task (rvdbreemen/OTGW-firmware, 207 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Embedded Iot Mentor?

alirezarezvani (a GitHub user) maintains it in alirezarezvani/claude-skills, which has 27,891 GitHub stars. The repository holds 342 skills in this directory. The repository was last updated on August 30, 2026.

Source: alirezarezvani/claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.