nipoppy-cli

Manage Nipoppy neuroimaging dataset workflows from initialization to extraction.

1|Updated Feb 25, 2026

Search Tags:#workflow #cli #container #data-management #neuroimaging #nipoppy

Ready to run

npx skills add https://github.com/bcmcpher/my-skills --skill nipoppy-cli

💬 Prompt for ChatGPT / Claude Chat▼

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.