micropython-repl

Interact with MicroPython boards via mpy-repl-tool to push files, execute code, and test MicroPython scripts. Use when working with MicroPython development, testing board functionality, or evaluating MicroPython code on hardware.

allowed_tools: BashExecution, Read, Glob, Grep, FileEditor

$ Installieren

git clone https://github.com/hoihu/py-framebuf /tmp/py-framebuf && cp -r /tmp/py-framebuf/.claude/skills/micropython-repl ~/.claude/skills/py-framebuf

// tip: Run this command in your terminal to install the skill


name: micropython-repl description: Interact with MicroPython boards via mpy-repl-tool to push files, execute code, and test MicroPython scripts. Use when working with MicroPython development, testing board functionality, or evaluating MicroPython code on hardware. allowed-tools: BashExecution, Read, Glob, Grep, FileEditor

MicroPython REPL Skill

This skill enables interaction with MicroPython boards using the mpy-repl-tool (via the there module). It connects to a board via RFC2217 serial-over-network protocol.

Connection Details

The board is accessible at: rfc2217://host.docker.internal:2217

All commands use this connection string via the -p parameter.

Core Operations

1. Push Python Files to Board

Push all Python files in the current directory to the board root:

python -m there -p rfc2217://host.docker.internal:2217 push *.py /

Push a specific file:

python -m there -p rfc2217://host.docker.internal:2217 push myfile.py /

Push to a specific directory on the board:

python -m there -p rfc2217://host.docker.internal:2217 push myfile.py /lib/

2. List Files on Board

List files in the root directory:

python -m there -p rfc2217://host.docker.internal:2217 ls

List files in a specific directory:

python -m there -p rfc2217://host.docker.internal:2217 ls /lib

List with details (sizes, etc):

python -m there -p rfc2217://host.docker.internal:2217 ls -l

3. Download Files from Board

Download a file from the board:

python -m there -p rfc2217://host.docker.internal:2217 pull /main.py .

View file contents directly:

python -m there -p rfc2217://host.docker.internal:2217 cat /boot.py

4. Execute Code on Board

Run a Python file that's already on the board:

python -m there -p rfc2217://host.docker.internal:2217 run /main.py

Execute a one-line Python expression:

python -m there -p rfc2217://host.docker.internal:2217 -c "print('Hello from MicroPython')"

Evaluate and capture output:

python -m there -p rfc2217://host.docker.internal:2217 -c "2 + 2"

5. Interactive REPL

Start an interactive REPL session:

python -m there -p rfc2217://host.docker.internal:2217 -i

_Note: After entering REPL, CTRL+C interrupts a running program, CTRL+D soft-resets the device

6. Remove Files from Board

Delete a file:

python -m there -p rfc2217://host.docker.internal:2217 rm /old_file.py

Delete multiple files:

python -m there -p rfc2217://host.docker.internal:2217 rm *.py

Delete all python files:

python -m there -p rfc2217://host.docker.internal:2217 rm -r *.py

7. Reset/Soft Reboot Board

Soft reset the board:

python -m there -p rfc2217://host.docker.internal:2217 --reset

Typical Workflow

  1. Develop: Edit Python files locally in the workspace
  2. Push: Upload files to the board using push *.py / and reset the board
  3. Execute: Run or evaluate the code using run or via -c
  4. Debug: Check output, modify code, and repeat
  5. Verify: Use ls to confirm files are on the board
  6. Download: Retrieve any generated files or logs using cat

Example: Complete Test Cycle

# 1. Push the framebuffer optimization script to the board
python -m there -p rfc2217://host.docker.internal:2217 push fb.py / --reset

# 2. Run the script on the board
python -m there -p rfc2217://host.docker.internal:2217 run /fb_opt.py

# 3. Download any output or log files
python -m there -p rfc2217://host.docker.internal:2217 cat /results.txt

Troubleshooting

  • Connection errors: Verify the RFC2217 server is running and accessible at host.docker.internal:2217
  • File not found: Use ls to check the exact path and filename on the board
  • Import errors: Ensure all dependent modules are pushed to the board
  • Memory errors: MicroPython boards have limited RAM; consider optimizing code or using smaller datasets

Additional Resources

For more advanced usage and commands, see reference.md.