dipy

What problem does it solve?

Diffusion MRI questions often require careful planning across reconstruction, registration, denoising, and tractography steps without accidentally assuming incorrect data formats or settings. This Skill helps you structure safe, tool-specific reasoning and documentation for DIPY workflows rather than jumping straight into heavy processing.

Core Features & Use Cases

• Diffusion workflow guidance: plan reconstruction, registration, denoising, tracking, and validation steps for diffusion MRI in Python.
• Routing to high-value references: surface what to read first (documentation, tutorials, and conceptual materials) before drafting commands.
• Quality control planning: propose checks and cautions (data space, modality assumptions, metadata, expected outputs) to reduce errors.

Quick Start

Ask your AI assistant: “Using the dipy skill, help me plan a diffusion MRI reconstruction and tractography workflow for my dataset and list the key QC checks and assumptions I should verify first.”