afni

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."