Nilearn fmri denoising starter
Skill ma-compbio-lab/SkillFoundry/skills/neuroscience-and-neuroimaging/nilearn-fmri-denoising-starter
A framework for discovering, compiling, and validating reusable skills for scientific agents.
npx -y skills add ma-compbio-lab/SkillFoundry --skill nilearn-fmri-denoising-starterAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
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Nilearn fMRI Denoising Starter
Use this skill to build a tiny toy fMRI-like timeseries matrix, regress out confounds with nilearn.signal.clean, and summarize the denoising effect.
What it does
- Creates deterministic toy voxel signals with known nuisance-confound structure.
- Uses
nilearn.signal.cleanto detrend, regress confounds, and standardize the cleaned output. - Returns pre/post confound-correlation summaries and cleaned-signal statistics in JSON.
When to use it
- You need a runnable starter for
fMRI preprocessing and denoising. - You want a verified local denoising example without requiring a full BIDS or fMRIPrep runtime.
Example
slurm/envs/neuro/bin/python skills/neuroscience-and-neuroimaging/nilearn-fmri-denoising-starter/scripts/run_nilearn_fmri_denoising.py \
--out scratch/neuro/nilearn_denoising_summary.json
Verification
- Skill-local tests:
python3 -m unittest discover -s skills/neuroscience-and-neuroimaging/nilearn-fmri-denoising-starter/tests -p 'test_*.py' - Repository smoke:
python3 -m unittest tests.smoke.test_phase31_frontier_leaf_conversion_skills -v