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script-organization

Organize data science projects with numbered scripts and BUILD_INFO.txt provenance.

2|Updated Feb 18, 2026
Search Tags:#organize#reproducibility#data-science#project-structure#script-numbering
One-click install
npx skills add https://github.com/MusserLab/lab-claude-skills --skill script-organization
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: script-organization
Source: https://github.com/MusserLab/lab-claude-skills/tree/main/skills/script-organization
Command: npx skills add https://github.com/MusserLab/lab-claude-skills --skill script-organization

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Script organization in data science projects is often inconsistent, leading to onboarding friction, chaos in outputs, and fragile reproducibility.

Core Features & Use Cases

  • Numbered script prefixes (01_, 02_, ...) reveal logical workflow order.
  • data/ and outs/ directory conventions separate inputs from outputs for traceability.
  • Build provenance via BUILD_INFO.txt in outs/ to capture commit and date for reproducibility.
  • Use Case: Starting a new project, apply these conventions to ensure predictable execution and easy sharing across teammates.

Quick Start

Create a project skeleton with a scripts/ directory containing numbered scripts (01_...) and corresponding data/ and outs/ directories, plus BUILD_INFO.txt artifacts.

Frequently Asked Questions about script-organization

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

FAQPage Schema
How do I structure data science scripts for reproducibility?▼

To structure data science scripts for reproducibility, enforce numbered prefixes like 01_ to reveal workflow order, and separate inputs in a data/ directory from outputs in an outs/ directory for full traceability.

What is the best way to organize data science project directories?▼

The best way to organize data science project directories is by creating a scripts/ folder for numbered analysis steps, a data/ folder for raw inputs, and an outs/ folder to isolate generated outputs.

How do I track build provenance in data science workflows?▼

You track build provenance in data science workflows by generating a BUILD_INFO.txt file inside each outs/ subfolder, which captures the commit hash and date to ensure trackable, reproducible executions.

Does script organization work for large data science codebases?▼

Yes, script organization works for large data science codebases because enforcing numbered scripts and directory conventions scales effectively, ensuring predictable execution and easy sharing across teams.

What do I need to set up before applying data science project conventions?▼

Before applying data science project conventions, you need to create a project skeleton with a scripts/ directory containing numbered scripts, alongside corresponding data/ and outs/ directories for inputs and outputs.

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