nilearn-fmri-denoising-starter — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited nilearn-fmri-denoising-starter (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Use this skill to build a tiny toy fMRI-like timeseries matrix, regress out confounds with nilearn.signal.clean, and summarize the denoising effect.
nilearn.signal.clean to detrend, regress confounds, and standardize the cleaned output.fMRI preprocessing and denoising.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.jsonpython3 -m unittest discover -s skills/neuroscience-and-neuroimaging/nilearn-fmri-denoising-starter/tests -p 'test_*.py'python3 -m unittest tests.smoke.test_phase31_frontier_leaf_conversion_skills -v~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.