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project-data-separation

Separate repository code from user data and notebooks in scientific pipelines.

3|Updated Dec 10, 2025
Search Tags:#project structure#data organization#scientific workflow#notebook management#data separation
One-click install
npx skills add https://github.com/smith6jt-cop/Skills_Registry --skill project-data-separation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: project-data-separation
Source: https://github.com/smith6jt-cop/Skills_Registry/tree/main/plugins/scientific/project-data-separation/skills/project-data-separation
Command: npx skills add https://github.com/smith6jt-cop/Skills_Registry --skill project-data-separation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill automates the separation of repository code from user data and notebooks, preventing Git conflicts and data loss.

Core Features & Use Cases

  • Structured Project Layout: Creates standardized folder hierarchies for raw data, processed outputs, and analysis results.

Core Features & Use Cases

  • Backward Compatibility: Maps new structured paths to legacy variable names used in existing notebooks.
  • Use Case: When starting a new imaging experiment, use this Skill to generate a complete project structure with all necessary directories.

Quick Start

Create a new project structure for the current experiment data.

Frequently Asked Questions about project-data-separation

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

FAQPage Schema
How do I organize code, data, and notebooks in a scientific project without Git conflicts?▼

Data separation prevents Git conflicts by organizing repository code, user data, and notebooks into structured paths. The Skill creates standardized directories for raw data, processed outputs, and analysis results, keeping code versioned separately from experiment data.

What's the best way to structure a scientific workflow with mixed code and data?▼

Structured project layout creates standardized folder hierarchies for raw data, processed outputs, notebooks, configurations, and logs. This prevents data leakage, enables template updates without losing experiments, and maintains consistency across diverse projects.

Can I use existing notebooks with a new project structure?▼

Yes. Backward compatibility maps new structured paths to legacy variable names, so existing notebooks work with the new organization without modification. This enables gradual migration while maintaining code stability.

How do I initialize a project structure for a new imaging experiment?▼

Project initialization creates all necessary directories in a single operation: raw data, processed outputs, notebooks, configs, metadata, and logs folders. Use the Skill to generate a complete standardized structure tailored to your experiment immediately.

Why separate data from code in scientific pipelines?▼

Data separation enables sharing code while protecting data privacy, updating templates without data loss, organizing experiments systematically, and preventing accidental data commits to version control—critical for reproducible research and team collaboration.

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