nipoppy-cli

Manage Nipoppy neuroimaging dataset workflows from initialization to extraction.

1|Updated Feb 25, 2026
One-click install
npx skills add https://github.com/bcmcpher/my-skills --skill nipoppy-cli
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: nipoppy-cli
Source: https://github.com/bcmcpher/my-skills/tree/main/plugins/nipoppy-cli/skills/nipoppy-cli
Command: npx skills add https://github.com/bcmcpher/my-skills --skill nipoppy-cli

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Nipoppy CLI helps researchers manage and automate complex neuroimaging data workflows in a single, consistent interface, reducing manual setup and error-prone steps.

Core Features & Use Cases

  • Centralized control over dataset initialization, curation tracking, reorganization, BIDS conversion, processing, and IDP extraction.
  • Workflow-agnostic guidance with dataset-state checks and reference materials to reduce misconfigurations.
  • Use case: Initialize a new dataset, register participants, track; reorganization; run bidsify and processing pipelines; extract IDPs.

Quick Start

Run nipoppy init /data/my-study to scaffold a new Nipoppy dataset, then follow with manifest edits and subsequent commands in the typical linear workflow.

Frequently Asked Questions about nipoppy-cli

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

FAQPage Schema
How do I manage neuroimaging dataset workflows from initialization to extraction?

To manage neuroimaging workflows, initialize a new dataset with a scaffold command, edit the manifest, register participants, and execute subsequent commands linearly to handle curation, BIDS conversion, processing, and extraction.

What do I need to run a neuroimaging workflow in Linux with Apptainer?

Running neuroimaging workflows in Linux with Apptainer requires valid dataset states, specifically a config.json file and manifest.tsv, alongside Boutiques descriptors to validate inputs and execute pipeline commands safely.

How does BIDS conversion work for neuroimaging datasets?

BIDS conversion for neuroimaging datasets works by running a bidsify command after dataset reorganization, utilizing dataset-state checks to ensure proper configuration before executing the conversion pipeline safely.

What is the best way to track neuroimaging data curation status?

The best way to track neuroimaging data curation status is by using a centralized CLI workflow that applies dataset-state checks against a manifest, ensuring consistent validation and reducing configuration errors.

Can I extract imaging-derived phenotypes after processing neuroimaging pipelines?

Yes, you can extract imaging-derived phenotypes immediately after processing neuroimaging pipelines, as the workflow supports end-to-end extraction as the final step in the standard linear dataset pipeline.