harmonization-tool
Use this skill whenever the user wants to remove site/scanner/batch effects from neuroimaging features before running downstream models, run mega-analysis across multiple datasets, or evaluate models with leave-site-out / site-stratified protocols. Triggers include: 'harmonize', 'ComBat', 'CovBat', 'site effect', 'scanner effect', 'batch effect', 'leave-site-out', 'mega-analysis', 'multi-site', 'cross-site', 'neuroHarmonize'. This is a horizontal cross-cutting layer between dataset skills and model skills.
npx skills add BioTender-max/awesome-bio-agent-skills --skill harmonization-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.
# Harmonization Tool Skill (Cross-Site Feature Alignment Layer) ## Overview `harmonization-tool` is the NeuroClaw cross-cutting layer that sits **between** dataset skills (ABIDE, ADHD-200, ABCD, HCP, UKB, ...) and model skills (BrainGNN, BNT, IBGNN, LGGNN, BrainNetCNN, FM-APP, SVM, SpaceNet, ...). Its job: take subject-level features extracted by dataset skills and remove **technical / batch variance** introduced by site, scanner, field strength, sequence, or dataset, while **preserving biological variance** (age, sex, diagnosis, ...). This is the prerequisite for any honest **mega-analysis** that pools individual-participant data (IPD) across sites or datasets. This skill follows NeuroClaw hierarchy: - Defines **WHAT to do**, not low-level implementation details. - Does **not** execute direct shell commands itself. - Delegates all execution via `claw-shell`. **Research use only.** --- ## When to Use This Skill Trigger this skill when the user asks for any of: - "harmonize features across sites / scanners / datasets" - "ComBat / ComBat-GAM / CovBat / neuroHarmonize / neuroCombat" - "remove site effect / scanner effect / batch effect" - "mega-analysis on ABIDE / ADHD-200 / ABCD / mu
- Overview
- When to Use This Skill
- Position in the NeuroClaw Pipeline
- Core Workflow (Never Bypassed)
- IO Contract (Standard Across All Dataset Skills)
- Methods Catalog
- Splitters
- Outputs
- KG Integration (Provenance, not Pollution)
- Pilot: ABIDE × BrainGNN (Phase 1)
- Real-data result: ABIDE I, aal116, N=639, 10 sites, 3 seeds, plain ComBat
- Real-data result: ADHD-200, aal116, N=669, 6 sites, 3 seeds, plain ComBat
- Two cohorts, one lesson
- Phase 2 Rollout
python skills/harmonization-tool/scripts/pilot_abide_style.py --source abide --abide-atlas rois_aal --method combat --seeds 42 7 123 python skills/harmonization-tool/scripts/pilot_abide_style.py --source adhd200 --method combat --seeds 42 7 123
What does the harmonization-tool skill do?
Use this skill whenever the user wants to remove site/scanner/batch effects from neuroimaging features before running downstream models, run mega-analysis across multiple datasets, or evaluate models with leave-site-out / site-stratified protocols. Triggers include: 'harmonize', 'ComBat', 'CovBat', 'site effect', 'scanner effect', 'batch effect', 'leave-site-out', 'mega-analysis', 'multi-site', 'cross-site', 'neuroHarmonize'. This is a horizontal cross-cutting layer between dataset skills and model skills.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill harmonization-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.