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name: mne-python description: "Route MNE-Python neurophysiology I/O, preprocessing, visualization, source modeling, analysis, CLI, datasets, and repository-development workflows." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

MNE-Python repo skill

Use this skill when a task involves MNE-Python: MEG/EEG/iEEG/fNIRS/eye-tracking I/O, Raw/Epochs/Evoked objects, preprocessing, source localization, time-frequency analysis, statistics, decoding, visualization, reports, datasets, CLI commands, or safe repository maintenance.

Start here

1. Confirm MNE-Python is importable and record its version: python -c "import mne; print(mne.__version__)".
2. For install choices, optional extras, Python requirements, and environment checks, read references/installation-and-environment.md.
3. For a package map and high-level object relationships, read references/package-map.md.
4. If an error spans install, imports, optional dependencies, data/cache, plotting backends, or runtime state, read references/troubleshooting.md.
5. To smoke-test an environment without datasets or GUI, run scripts/mne_smoke_check.py. For CLI help and command discovery, run scripts/mne_cli_probe.py.
6. To check staleness against a checkout, read references/repo-provenance.md.

Route by task

• File I/O, Raw, Info, channels, reader selection, array-to-Raw, preload, and vendor formats → sub-skills/io-raw-data/SKILL.md.
• Events, annotations, filtering, bad channels, ICA/SSP, artifact correction, Epochs, Evoked, covariance, and rank → sub-skills/preprocessing-epochs-evoked/SKILL.md.
• Plots, topomaps, interactive/headless visualization, 3D backend choices, and mne.Report → sub-skills/visualization-reporting/SKILL.md.
• Source spaces, BEM, transforms, forward models, inverse operators, beamformers, dipoles, source estimates, labels, and morphing → sub-skills/source-modeling-inverse/SKILL.md.
• PSD, TFR/CSD, cluster statistics, regression, decoding, scikit-learn estimators, and simulation → sub-skills/timefreq-stats-decoding-simulation/SKILL.md.
• mne CLI commands, dataset helpers, config/cache/logging/sys_info, and install extras → sub-skills/cli-datasets-config/SKILL.md.
• Editing this repository, public API stubs, docs, tests, changelog fragments, import-location checks, deprecations, and AI-assistance policy → sub-skills/repo-development/SKILL.md.

Cross-cutting operating rules

• Prefer documented MNE containers and methods over ad-hoc arrays. Track object type, channel names/types, sampling frequency, event ids, bad channels, projectors, rank, and units at every stage.
• Many MNE object methods mutate in place and return self; make copies when preserving earlier states matters.
• Optional dependencies are workflow-specific. Base MNE covers many 2D/core workflows; HDF5/vendor readers, decoding, 3D/Qt/browser rendering, notebook interactivity, FreeSurfer/OpenMEEG, and large datasets require extra checks.
• Do not trigger dataset downloads, GUI windows, FreeSurfer/OpenMEEG commands, GPU/CUDA paths, or long examples unless the user explicitly approves those resources and runtime side effects.
• When creating code for headless or CI environments, use show=False, explicit output files, open_browser=False, deterministic random seeds, and small synthetic data.
• This generated skill is self-contained. Do not require the original MNE-Python checkout to be present except when the user explicitly asks for repository-development work in a checkout.

Minimal smoke command

python scripts/mne_smoke_check.py --include-decoding

The smoke helper constructs tiny synthetic data, exercises Raw/Epochs/Evoked, PSD, imports selected subpackages, optionally checks decoding imports, and never uses network, datasets, GUI, or source-repository files.