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Esp32 Test Plan

Community
agodianel
esp32-test-plan

Generates a layered test plan for an ESP32 firmware change — static checks, host-side pytest/C logic, ESP-IDF Unity tests on target, pytest-embedded integration — with positive, negative, boundary, concurrency and error cases and explicit pass/fail evidence. Use when the user asks for a "test plan", "how do I test this", "what tests do I need", "unit tests for this component", "pytest-embedded", "Unity test", "hardware-in-the-loop", "/test-plan", or after an implementation contract is accepted for ESP32, ESP32-S3, ESP32-C3, ESP32-C6 or other ESP-IDF projects.

Overview

Publisheragodianel
Repositoryesp32-claude-workbench
Skill nameesp32-test-plan
Stars
81
Forks
7
Bundled files
2
LicenseMIT
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by agodianel on GitHub. Read the source before you install it.

Installation

Install the Esp32 Test Plan 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/agodianel/esp32-claude-workbench/tree/main/skills/esp32-test-plan
  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/agodianel/esp32-claude-workbench.git /tmp/esp32-claude-workbench
mkdir -p .claude/skills
cp -r /tmp/esp32-claude-workbench/skills/esp32-test-plan .claude/skills/esp32-test-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Esp32 Test Plan 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 Esp32 Test Plan 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 Esp32 Test Plan 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.

ESP32 Test Plan

Every implementation contract needs a test plan; maximise tests that run without hardware, and make hardware tests explicit.

Tools

If the esp32-workbench MCP server is connected, call its tools; otherwise run the CLI. If esp32wb is not on PATH, use uvx --from git+https://github.com/agodianel/esp32-claude-workbench esp32wb <args>. Add --json for machine-readable output (useful as CI evidence).

Static layer (no hardware)MCP toolCLI
All static checksproject_doctoresp32wb doctor . (non-zero exit on errors)
Pin audit for the targetpin_auditesp32wb pins .
Stack sizesstack_checkesp32wb stacks .
sdkconfig reviewsdkconfig_reviewesp32wb sdkconfig sdkconfig
Partition fit vs built apppartition_checkesp32wb partitions partitions.csv --bin build/<app>.bin
Mission completenessvalidate_missionesp32wb mission validate missions/<file>.md

Use the test plan template.

Steps

1. Classify the change → layers

LayerApplies whenTooling
0. StaticAlwaysesp32wb doctor, compiler warnings (-Werror where possible), idf.py clang-check / cppcheck if available
1. Repository logicPython tooling, scripts, templatespytest
2. Host-side C logicPure logic: parsers, state machines, protocol encoders, mathNative build (CMake + Unity/CMock, or ESP-IDF linux target where the component supports it)
3. On-target unitComponent behaviour needing the real chip or driversESP-IDF Unity test app per component (test_apps/), run via pytest-embedded
4. Target integrationEnd-to-end behaviour: boot, Wi-Fi, peripherals, OTA, powerpytest-embedded with pytest-embedded-idf + pytest-embedded-serial-esp

Record the target chip for layers 3–4; a test passing on classic ESP32 says nothing about ESP32-S3 or a RISC-V chip.

2. Enumerate cases per layer

  • Positive: valid inputs, expected behaviour.
  • Negative: invalid input, NULL handles, driver returning errors, missing NVS keys.
  • Boundary: buffer sizes, max lengths, timeouts, ADC/PWM range ends, queue full.
  • Concurrency: two tasks on shared state, ISR + task, dual-core contention (if the chip is dual-core).
  • Error / recovery: bus NACK/timeout, Wi-Fi drop and reconnect, brownout, OTA failure + rollback.
  • Resource: heap and stack high-water marks before/after; no leak over N iterations.

3. Pass/fail criteria and evidence

For each case: expected result, tolerance (timing/analog), and evidence — test output, assertion count, serial log excerpt, measured value, or tool JSON.

4. Infrastructure

New test files, fixtures/mocks, test data, sdkconfig for tests (sdkconfig.ci.*), and hardware setup (board, wiring, instruments).

5. Write the plan

Fill the template; store it with the mission (or in the mission's Test Plan section for small changes).

pytest conventions

  • Mark hardware tests so CI can skip them:
    python
    @pytest.mark.hardware
    @pytest.mark.esp32s3        # pytest-embedded target marker, if used
    def test_wifi_reconnect(dut) -> None:
        dut.expect("Got IP", timeout=30)
  • Keep host tests free of serial ports and network.
  • Type hints, pathlib.Path, fixtures for shared setup (project standard).

Rules

  1. No contract without a test plan; tiny changes may use a 2–3 line plan.
  2. Prefer extracting pure logic so it can be tested on the host.
  3. Every hardware test is explicitly marked and lists required wiring.
  4. Include negative tests — happy-path-only is insufficient for firmware.
  5. "What was not tested and why" is a mandatory section.

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 Esp32 Test Plan AI skill do?

Generates a layered test plan for an ESP32 firmware change — static checks, host-side pytest/C logic, ESP-IDF Unity tests on target, pytest-embedded integration — with positive, negative, boundary, concurrency and error cases and explicit pass/fail evidence. Use when the user asks for a "test plan", "how do I test this", "what tests do I need", "unit tests for this component", "pytest-embedded", "Unity test", "hardware-in-the-loop", "/test-plan", or after an implementation contract is accepted for ESP32, ESP32-S3, ESP32-C3, ESP32-C6 or other ESP-IDF projects.

Why use Esp32 Test Plan on TypingMind?

Because you install it once and use it with any model. Esp32 Test Plan 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 Esp32 Test Plan in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agodianel/esp32-claude-workbench/tree/main/skills/esp32-test-plan. 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 Esp32 Test Plan?

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 Esp32 Test Plan?

As many as you like. As long as a model supports skills, you can use Esp32 Test Plan with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Esp32 Test Plan AI skill free?

Yes. It is published on GitHub by agodianel 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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