datalad-init

Initialize DataLad datasets with YODA layout and pre-flight validations.

1|Updated Feb 25, 2026

Search Tags:#git #analytics #provenance #reproducibility #dataset #datalad #yoda

Ready to run

npx skills add https://github.com/bcmcpher/my-skills --skill datalad-init

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: datalad-init
Source: https://github.com/bcmcpher/my-skills/tree/main/plugins/datalad-cli/skills/datalad-init
Command: npx skills add https://github.com/bcmcpher/my-skills --skill datalad-init

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Creates a reproducible DataLad dataset using YODA principles, applying a structured layout and provenance tracking to local analysis projects.

Core Features & Use Cases

Enforces a standard YODA directory layout (code/, outputs/, inputs/, README.md) for DataLad datasets.
Automatically applies -c yoda configuration to ensure consistent behavior and attributes.
Performs pre-flight checks to avoid overwriting existing datasets and warn about legacy annex backends.

Quick Start

Initialize a DataLad dataset at the target path using the YODA layout and provenance tracking.

Frequently Asked Questions about datalad-init

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

FAQPage Schema

How do I create a reproducible DataLad dataset with a standard directory layout?▼

Initialize a reproducible DataLad dataset using the YODA layout, which automatically creates standardized code/, outputs/, inputs/, and README.md directories with built-in provenance tracking for local analysis projects.

What is the YODA layout for DataLad datasets?▼

The YODA layout is a standardized directory structure for DataLad datasets that organizes analysis projects into code/, outputs/, inputs/, and README.md directories to enforce reproducible research principles.

Why does DataLad require the -c yoda configuration when initializing datasets?▼

The -c yoda configuration is required because it ensures consistent behavior and attributes across your DataLad dataset, automatically applying the standardized YODA directory layout and provenance tracking for reproducible analysis.

Can I initialize a DataLad dataset in a directory that already contains data?▼

Pre-flight checks warn you when a dataset already exists to prevent accidental overwriting, and also alert you if the annex backend is legacy, guiding you toward safe DataLad initialization.

How do I set up provenance tracking for local analysis projects?▼

Set up provenance tracking by initializing a DataLad dataset with the YODA layout, which applies structured directories and automatic provenance capture to track all changes and inputs throughout your local analysis workflow.

What's the best way to structure reproducible data analysis projects?▼

Use the YODA layout to structure reproducible data analysis projects, organizing files into dedicated code/, outputs/, and inputs/ directories with a README.md to maintain consistent provenance tracking across DataLad datasets.