nilearn-tool
Use this skill whenever any NeuroClaw fMRI modality skill needs to execute concrete Nilearn operations: ROI/atlas time-series extraction, confounds handling (fMRIPrep), seed-based connectivity maps, ROI-to-ROI connectivity matrices, and optional GLM/decoding utilities. This is the dedicated base/tool skill that contains Nilearn usage patterns and lightweight wrappers. Never called directly by the user.
npx skills add BioTender-max/awesome-bio-agent-skills --skill nilearn-tool --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Nilearn Tool (Base/Tool Layer) ## Overview `nilearn-tool` is the **NeuroClaw base/tool skill** that implements concrete **Nilearn** workflows for turning **preprocessed BOLD** into features (ROI time series, connectivity matrices, seed maps) and optional statistical modeling (GLM). It is **never called directly by the user**. It is delegated to by `fmri-skill` (or other interface/modality skills) and executed via `claw-shell`. **Research use only.** ## Agent Reference Rule When the agent needs Nilearn-based implementation code, it should first consult the curated snippets in `skills/nilearn-tool/scripts/` instead of copying directly from long tutorial scripts with hard-coded paths. Reference snippets available: - `scripts/preprocess_bold_reference.py` -> dummy removal, smoothing, band-pass filtering, MNI resampling - `scripts/connectome_reference.py` -> atlas ROI extraction and ROI-to-ROI connectivity export - `scripts/zalff_summary_reference.py` -> MNI resampling, zALFF summary, atlas-level regional export - `scripts/task_glm_reference.py` -> first-level task GLM with design matrix and contrast maps - `scripts/second_level_glm_reference.py` -> group-level GLM from subject contra
- Overview
- Agent Reference Rule
- Scope (What this tool does / does not do)
- ✅ This tool does
- ❌ This tool does NOT do
- Core Outputs (Typical)
- Minimal Nilearn Usage Patterns (Short Snippets)
- 1) fMRIPrep confounds (recommended)
- 2) ROI time series (atlas/parcellation)
- 3) ROI-to-ROI connectivity
- 4) Seed-to-voxel connectivity (concept)
- Curated Reference Snippets
- scripts/preprocessboldreference.py
- scripts/connectomereference.py
python skills/nilearn-tool/scripts/preprocess_bold_reference.py \ python skills/nilearn-tool/scripts/connectome_reference.py \ python skills/nilearn-tool/scripts/zalff_summary_reference.py \ conda run -n neuroclaw-nilearn python skills/nilearn-tool/nilearn_pipeline.py roi-ts \ conda run -n neuroclaw-nilearn python skills/nilearn-tool/nilearn_pipeline.py connectome \ conda create -n neuroclaw-nilearn python=3.11 -y conda install -n neuroclaw-nilearn -c conda-forge nilearn nibabel numpy scipy pandas scikit-learn matplotlib -y
What does the nilearn-tool skill do?
Use this skill whenever any NeuroClaw fMRI modality skill needs to execute concrete Nilearn operations: ROI/atlas time-series extraction, confounds handling (fMRIPrep), seed-based connectivity maps, ROI-to-ROI connectivity matrices, and optional GLM/decoding utilities. This is the dedicated base/tool skill that contains Nilearn usage patterns and lightweight wrappers. Never called directly by the user.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill nilearn-tool --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.