Agent skill · Data & Analytics

fmriprep-tool

Use this skill whenever the user wants to perform standardized preprocessing of functional MRI (fMRI) and anatomical MRI data using fMRIPrep. Triggers include: 'fmriprep', 'fMRIPrep', 'fMRI preprocessing', 'BIDS fMRI', 'run fmriprep', 'preprocess bold', 'BOLD preprocessing', 'anatomical preprocessing', or any request involving BIDS-organized fMRI datasets.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill fmriprep-tool --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 9 KB
Bundled scripts: none
Requires: - claw-shell - bids-organizer
Path: skills/neuroclaw/fmriprep-tool/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# fMRIPrep Tool ## Overview fMRIPrep is a robust, standardized preprocessing pipeline for BIDS-compliant functional and anatomical MRI data. It performs best-practice steps including anatomical segmentation, functional motion correction, susceptibility distortion correction, coregistration, normalization to standard space, and generates comprehensive QC reports. This skill serves as the **NeuroClaw interface-layer wrapper** for fMRIPrep and strictly follows the hierarchical design: 1. Check whether fMRIPrep and its dependencies (Docker or Singularity) are installed. 2. If missing → invoke `dependency-planner` to generate a safe installation plan. 3. Detect input BIDS dataset structure and confirm output directory. 4. Generate a clear, numbered execution plan with exact command, flags, estimated runtime, and risks. 5. Present the plan and wait for explicit user confirmation (“YES” / “execute” / “proceed”). 6. On confirmation → delegate the entire pipeline execution to `claw-shell`. 7. After completion, summarize outputs, highlight QC reports, and suggest next steps (e.g., feeding results into analysis or paper-writing). **Research use only.** ## Quick Reference | Task | Recommended

What's inside
Steps it walks through
  1. Overview
  2. Quick Reference
  3. Common Shell Command Examples
  4. Installation (Handled by dependency-planner)
  5. NeuroClaw recommended wrapper script
  6. Important Notes & Limitations
  7. When to Call This Skill
  8. Post-Execution Verification (Harness Integration)
  9. Complementary / Related Skills
  10. More Advanced Features
Commands it runs
Standard full pipeline (most common)
fmriprep \
participant \
Anatomical preprocessing only
fmriprep /data/bids /data/fmriprep_output participant --anat-only
Use Singularity instead of Docker (common on clusters)
singularity run --cleanenv \
fmriprep --version
More from awesome-bio-agent-skills
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About this skill
What does the fmriprep-tool skill do?

Use this skill whenever the user wants to perform standardized preprocessing of functional MRI (fMRI) and anatomical MRI data using fMRIPrep. Triggers include: 'fmriprep', 'fMRIPrep', 'fMRI preprocessing', 'BIDS fMRI', 'run fmriprep', 'preprocess bold', 'BOLD preprocessing', 'anatomical preprocessing', or any request involving BIDS-organized fMRI datasets.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill fmriprep-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.

Keep going