data-science-setup

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.

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.

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.

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.

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.

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.