data-science-setup

Name: data-science-setup
Availability: InStock
Author: a2ngerer

Configure Claude for data science and ML projects with standardized notebooks and reproducibility practices.

5|Updated Apr 16, 2026

Search Tags:#workflow #onboarding #reproducibility #notebooks #data-science #experiment-tracking

Ready to run

npx skills add https://github.com/a2ngerer/claude_onboarding_agent --skill data-science-setup

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-science-setup
Source: https://github.com/a2ngerer/claude_onboarding_agent/tree/main/skills/data-science-setup
Command: npx skills add https://github.com/a2ngerer/claude_onboarding_agent --skill data-science-setup

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Configure Claude for data science and ML projects by standardizing notebooks, experiments, and reproducibility practices.

Core Features & Use Cases

Notebook & experiment workflow setup: Create a consistent workspace from exploration to production.
Data layout & reproducibility conventions: Enforce standard folders and experiment-tracking for repeatable results.
Handoff & subagent integration: Provide guided handoffs and optional subagents for reproducibility checks.

Quick Start

Run the onboarding flow for a new project and answer the prompts to tailor Claude setup for your data-science workflow.

Frequently Asked Questions about data-science-setup

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

FAQPage Schema

How do I set up a reproducible data science workflow in Claude?▼

To set up a reproducible data science workflow, this skill configures your project with standardized notebooks, experiment tracking, and enforced environment scaffolding. It runs an onboarding flow that standardizes data layout and automation practices.

What's the best way to standardize notebooks and experiment tracking for ML projects?▼

Standardizing notebooks and experiment tracking is handled by enforcing notebook hygiene and reproducibility conventions. The setup guides you through standard folder structures and tracking practices to ensure repeatable results from exploration to production.

Can I use this data science setup for exploratory analysis and model development?▼

Yes, this data science setup is applicable to projects focused on exploratory analysis and model development. It provides a consistent workspace that guides you from initial exploration through to production-level experiment tracking.

How do I create a consistent workspace from data exploration to production?▼

Creating a consistent workspace from exploration to production involves running the onboarding flow for your project. You answer prompts that tailor the environment scaffolding, notebook hygiene rules, and workflow automation to your specific data science needs.

Does the data science setup support subagents for reproducibility checks?▼

Yes, the data science setup supports optional subagents for reproducibility checks. It includes guided handoffs and optional subagent integration, along with graphify integration, to deliver an end-to-end reproducible environment.

What data layout conventions are enforced for reproducible ML experiments?▼

Reproducible ML experiments require enforced standard folders and experiment-tracking conventions. This setup dictates specific data layout structures to guarantee repeatable results and maintain workflow automation across your project.