Esp32 Repo Scout

Community

agodianel

esp32-repo-scout

Scans an ESP-IDF or PlatformIO firmware repository and produces a scout report of target chip, components, dependencies, pin usage, FreeRTOS tasks and risks before any change is planned. Use when the user says "scout the repo", "explain this firmware project", "what does this ESP32 project do", "map the components", "onboard me", "where is app_main", or before writing a mission or implementation contract on an unfamiliar ESP32, ESP32-S3, ESP32-C3, ESP32-C6 or other Espressif codebase.

Overview

Publisher	agodianel
Repository	`esp32-claude-workbench`
Skill name	`esp32-repo-scout`
Stars	81
Forks	7
Bundled files	1
License	MIT
Links	Skill source Repository

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.
1 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 Repo Scout 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/agodianel/esp32-claude-workbench/tree/main/skills/esp32-repo-scout
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/agodianel/esp32-claude-workbench.git /tmp/esp32-claude-workbench
mkdir -p .claude/skills
cp -r /tmp/esp32-claude-workbench/skills/esp32-repo-scout .claude/skills/esp32-repo-scout

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

Use it in TypingMind

Enable Esp32 Repo Scout 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 Repo Scout 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 Repo Scout 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 Repo Scout

Build an accurate map of an ESP32 firmware project before proposing changes. Read-only: this skill never edits files.

Tools

Deterministic tools beat guessing. If the esp32-workbench MCP server is connected, call its tools; otherwise run the esp32wb CLI. If esp32wb is not on PATH, use uvx --from git+https://github.com/agodianel/esp32-claude-workbench esp32wb <args>. Analysis commands accept --json.

Need	MCP tool	CLI
All static checks at once	`project_doctor`	`esp32wb doctor . [--target T]`
GPIO usage for the detected chip	`pin_audit`	`esp32wb pins .`
Verified pin facts for the chip	`chip_info`	`esp32wb chip <target>`
Task stack sizes	`stack_check`	`esp32wb stacks .`

esp32wb doctor . is the fastest first step: it detects the target from CONFIG_IDF_TARGET and runs pins, stacks, sdkconfig and partition checks together.

Steps

1. Identify build system and target

CMakeLists.txt with include($ENV{IDF_PATH}/tools/cmake/project.cmake) → ESP-IDF CMake project.
platformio.ini → PlatformIO (check framework = espidf vs arduino, and board =).
Makefile with PROJECT_NAME + component.mk → legacy GNU Make ESP-IDF (pre-v4); flag as a risk.
Target chip: grep CONFIG_IDF_TARGET sdkconfig sdkconfig.defaults*. If absent, the default is classic esp32 — say so explicitly instead of assuming the board.
ESP-IDF version: idf.py --version, dependencies.idf in main/idf_component.yml, CI files, or .devcontainer. Record it; APIs differ a lot between v4.x and v5.x.

2. Map components

bash
find . -name CMakeLists.txt -not -path "./build/*" -not -path "./managed_components/*"
grep -rn "idf_component_register" --include=CMakeLists.txt . | grep -v build/

For each component record: name, public headers (include/), REQUIRES / PRIV_REQUIRES, and whether it has a test/ folder or host tests.

3. Hardware configuration

sdkconfig.defaults* (per-target variants like sdkconfig.defaults.esp32s3).
Pin definitions: GPIO_NUM_, #define .*_(PIN|GPIO|SDA|SCL|MOSI|MISO|CLK|CS), Kconfig CONFIG_*_GPIO.
Peripherals: grep for i2c_, spi_, uart_, ledc_, adc_, rmt_, i2s_, esp_wifi_, nimble/esp_bt.
Board: look for a pin_map.h, README, or schematic reference. Do not infer the board from pin numbers alone.
Run esp32wb pins . and esp32wb chip <target> rather than recalling pin limits from memory.

4. Task architecture

bash
grep -rnE "xTaskCreate(PinnedToCore|Static)?\(" --include=*.c --include=*.cpp .

Record task name, stack size (ESP-IDF stack depth is in bytes), priority, and core affinity. esp32wb stacks . flags undersized stacks for you.

5. External dependencies

idf_component.yml / dependencies.lock, managed_components/, .gitmodules, vendored third_party/ or lib/, PlatformIO lib_deps.

6. Existing workflow state

missions/ — is there an active mission? Read it first and resume from its Next Step.
tests/, pytest.ini, test/ inside components, CI workflows.

Output: Scout Report

Write to the active mission file if one exists, otherwise print:

markdown
# Scout Report — <project>
- **Build system / ESP-IDF version**:
- **Target chip** (source: sdkconfig line or "not set → default esp32"):
- **Board** (source):
## Components
| Component | Purpose | Requires | Tests |
## Hardware
| GPIO | Function | Defined in |
(Include `esp32wb pins` findings verbatim.)
## Tasks
| Task | Stack (bytes) | Prio | Core |
## Dependencies
## Test coverage
## Risks / unknowns

Rules

State the evidence for every claim (file + line or tool output).
Mark anything you could not confirm as unknown — never fill gaps with classic-ESP32 defaults.
Next step after scouting is usually the esp32-feature-contract workflow (mission → contract → test plan).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

agents/openai.yaml1 KB

Frequently asked questions

What does the Esp32 Repo Scout AI skill do?

Why use Esp32 Repo Scout on TypingMind?

Because you install it once and use it with any model. Esp32 Repo Scout 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 Repo Scout in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agodianel/esp32-claude-workbench/tree/main/skills/esp32-repo-scout. 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 Repo Scout?

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 Repo Scout?

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

Is the Esp32 Repo Scout 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

ESP32 Repo Scout

Tools

Steps

1. Identify build system and target

2. Map components

3. Hardware configuration

4. Task architecture

5. External dependencies

6. Existing workflow state

Output: Scout Report

Rules

Bundled files

Frequently asked questions

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