kmeans
Use this model doc whenever the user wants to perform brain parcellation using K-means. This is a non-deep-learning unsupervised route focused on parcel discovery, voxel or vertex grouping, and atlas-like region generation from neuroimaging features.
npx skills add BioTender-max/awesome-bio-agent-skills --skill kmeans --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.
# K-means Model Doc ## Overview K-means is a classical non-deep-learning method for data-driven brain parcellation. - Model family: non-deep-learning unsupervised clustering method - Typical objectives: - partition voxels, vertices, or ROI features into data-driven brain parcels - build subject-level or group-level parcellations from functional or structural similarity - export parcel labels and cluster summaries - Primary input: preprocessed neuroimaging features, optional mask - Primary output: parcel label map, cluster summaries, optional centroid outputs In NeuroClaw, this document is model-level guidance for K-means-based brain parcellation workflows rather than supervised prediction. Upstream preparation should usually be delegated to: - `fmri-skill` for rs-fMRI or task-fMRI feature preparation when parcellation is function-driven - `smri-skill` for structural feature preparation when parcellation is anatomy-driven - `nilearn-tool` for concrete masking, feature matrix preparation, and K-means-based parcel export **Research use only.** --- ## Quick Start ### 1) Prepare parcellation inputs Expected inputs: - preprocessed feature matrix or image list - optional brain mask - opti
- Overview
- Quick Start
- 1) Prepare parcellation inputs
- 2) K-means route
- Input / Output Contract
- Required inputs
- Optional inputs
- Produced outputs
- Recommended Delegation
- When to Use K-means
- Limitations and Notes
- Reference
delegated through claw-shell after features are prepared python skills/nilearn-tool/scripts/kmeans_parcellation_reference.py \
What does the kmeans skill do?
Use this model doc whenever the user wants to perform brain parcellation using K-means. This is a non-deep-learning unsupervised route focused on parcel discovery, voxel or vertex grouping, and atlas-like region generation from neuroimaging features.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill kmeans --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.