With papermill you can parametrize a notebook and run it on different inputs to check that it is not raising uncaught exceptions. This can be wrapped to be part of a pytest test suite, possibly via a some ad-hoc pytest fixture or plugin.

If the notebooks themselves contain assertions to check that expectations on the outputs are met, then you have an automated way to check that the notebooks behave the way you want on some test inputs. For long notebooks, this is more like integration/functional tests rather than unit tests, but I think this is already an improvement over manually run notebooks.

Note sure about strict types: you mean running mypy on a notebook? Maybe this can be helpful:

- https://pypi.org/project/nb-mypy/

About linters, you can install `jupyterlab-lsp` and `python-lsp-ruff` together for instance.

vscode jupyter uses the same extensions as vscode, so you can get a linter and scrict type checking. Not sure about tests though