afni

Plan AFNI-oriented MRI and fMRI command-line workflows with QC checks.

1|Updated May 16, 2026

Search Tags:#neuroimaging #qc #fmri #dataset inspection #afni #mri #command planning

One-click install

npx skills add https://github.com/MarvinCui/NeuroForge --skill afni

Or copy as Structured Prompt for Agent▼

Please help me install this Agent Skill.
Skill: afni
Source: https://github.com/MarvinCui/NeuroForge/tree/main/NeuroForge/skills/afni
Command: npx skills add https://github.com/MarvinCui/NeuroForge --skill afni

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

AFNI-oriented MRI and fMRI command planning becomes error-prone because users need correct tool usage, modality/file assumptions, and QC expectations before running heavy processing. This skill helps you translate a research question into an AFNI-focused, documentation-grounded plan and sanity checks tailored to the workflow stage you’re in.

Core Features & Use Cases

Command & workflow planning (no heavy execution): Draft safe, tool-aligned command ideas for inspection and downstream routing rather than running full pipelines automatically.
QC and dataset inspection guidance: Identify motion/mask/registration assumptions and common AFNI QC checkpoints to review before deeper analysis.
Documentation-first reference surfacing: Point you to high-value AFNI references relevant to your specific question (e.g., dataset inspection, ROI/atlas concepts, and AFNI real-time/usage docs).
Use case: You’re preparing to analyze an fMRI dataset and want to confirm assumptions (space, metadata, file organization, and QC outputs) before you propose modeling steps.

Quick Start

Ask: "Using the afni skill, help me plan an AFNI command-line approach to inspect my fMRI NIfTI dataset and list the key QC checks I should review first."

Frequently Asked Questions about afni

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I plan AFNI commands for fMRI dataset inspection?▼

To plan AFNI commands for fMRI dataset inspection, you can use this skill to draft safe, tool-aligned command ideas and identify relevant documentation references without executing heavy pipelines automatically.

What QC checks should I run on an fMRI dataset before preprocessing?▼

Before preprocessing an fMRI dataset, you should run QC checks to review motion correction assumptions, mask integrity, and registration expectations to ensure data sanity.

Can I automatically run full MRI processing pipelines with AFNI?▼

You cannot automatically run full MRI processing pipelines because this skill enforces safety constraints that avoid heavy automatic execution, focusing instead on planning and routing guidance.

What is the best way to route AFNI tools for specific neuroimaging tasks?▼

The best way to route AFNI tools for specific neuroimaging tasks is to use documentation-grounded planning that maps your research question to tool-specific boundaries and expected outputs.