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name: nilearn description: "Use and maintain Nilearn, the Python neuroimaging package for Niimg/surface data, maskers, GLM, decoding, connectivity, plotting, datasets, and repository development." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Nilearn Repo Skill

Use this skill when a task involves the nilearn Python package or this Nilearn source checkout. Nilearn supports statistical learning for neuroimaging data: Niimg and surface manipulation, signal extraction, GLM, decoding, connectomes, decomposition, datasets, plotting, reports, and scikit-learn-style estimators.

Quick Start

• Install the package with python -m pip install nilearn for core APIs.
• Install plotting/reporting extras with python -m pip install "nilearn[plotting]" when a task needs Matplotlib, Plotly, or Kaleido-backed outputs.
• Check imports with: python -c "import nilearn; import nilearn.image; import nilearn.maskers; print(nilearn.__version__)".
• Nilearn does not expose package console scripts; use Python APIs and bundled smoke helpers in the relevant sub-skill.
• Read repo provenance before deciding whether this generated skill is stale for a changed checkout.
• Use root troubleshooting for install/import, optional dependency, data/cache, plotting, and development failure triage.

Route By Task

• Images, masks, and signals: Use data-io-signal for nilearn.image, nilearn.masking, nilearn.signal, Niimg validation, resampling, smoothing, thresholding, direct apply_mask/unmask, and no-network image smoke checks.
• Maskers and regions: Use maskers-regions for NiftiMasker, label/map/sphere maskers, multi-subject maskers, RegionExtractor, Parcellations, ReNA, inverse transforms, and masker reports.
• Surface data: Use surface-workflows for SurfaceImage, PolyMesh, PolyData, vol_to_surf, fsaverage helpers, and surface masker data contracts.
• GLM analysis: Use glm-analysis for first-level and second-level models, events, design matrices, HRFs, BIDS GLM helpers, contrasts, thresholding, cluster inference, and GLM reports.
• Decoding and connectomes: Use ml-decoding-connectivity for Decoder, SearchLight, SpaceNet/FREM, ConnectivityMeasure, group sparse covariance, CanICA/DictLearning, and mass-univariate permutation workflows.
• Datasets and interfaces: Use datasets-interfaces for dataset fetchers/loaders, local templates, atlases, cache/data_dir choices, OpenNeuro/NeuroVault, BIDS helpers, fMRIPrep confounds, and FSL design files.
• Plotting and reports: Use plotting-reporting for static brain plots, surface plots, interactive HTML views, connectome/matrix/event visualizations, cluster tables, HTML reports, and headless rendering issues.
• Repository changes: Use development-maintenance when editing this checkout, adding tests/docs/changelog entries, or debugging import-linter, pre-commit, estimator checks, or CI-style validation.

Common Workflow Chains

• Preprocess then model: Start with data-io-signal for Niimg/mask/signal validation, use maskers-regions for estimator-style extraction when needed, then use glm-analysis or ml-decoding-connectivity.
• Dataset-backed analysis: Start with datasets-interfaces to choose safe loaders or bounded fetchers, then route to maskers, GLM, decoding, connectivity, or plotting based on the analysis goal.
• Surface visualization: Use surface-workflows to validate mesh/data shapes and plotting-reporting to choose static or interactive rendering.
• GLM reporting: Use glm-analysis for model, contrast, and threshold decisions; use plotting-reporting for cluster tables, HTML report output, and headless/optional dependency failures.
• Coding-agent maintenance: Use development-maintenance first, then route to the user-facing sub-skill that owns the affected behavior for examples and API expectations.

Data And Safety Notes

• Treat most fetch_* dataset functions as network/cache operations unless the datasets sub-skill marks them as no-network local loaders.
• Prefer tiny synthetic NIfTI/surface fixtures for smoke checks and tests.
• Do not assume plotting extras are installed unless the environment was installed with nilearn[plotting] or the task confirms Matplotlib/Plotly.
• Use scikit-learn-style expectations for estimators: fit returns self, fitted attributes end in _, and transform or predict consumes data with shapes described by the owning sub-skill.
• When editing Nilearn itself, obey the repository’s import-layer architecture, test-marker requirements, changelog policy, and formatting conventions in development-maintenance.

Bundled Root Helper

• Run python scripts/inspect_nilearn_environment.py --help from this skill directory to inspect an environment for Nilearn imports, version, optional plotting packages, and public module availability without downloading data.