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data-science-development

Produce reproducible data analyses with pinned environments and statistical rigor.

1|Updated May 15, 2026
Search Tags:#data analysis#dbt#data hygiene#ab testing#dashboarding#statistical rigor#reproducible notebooks
One-click install
npx skills add https://github.com/Blaze-sports-Intel/uber-engineer --skill data-science-development
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-science-development
Source: https://github.com/Blaze-sports-Intel/uber-engineer/tree/main/plugins/uber-engineer/skills/data-science-development
Command: npx skills add https://github.com/Blaze-sports-Intel/uber-engineer --skill data-science-development

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Data science work often fails when analyses can’t be reproduced, datasets aren’t cleaned consistently, or statistical conclusions are presented without proper rigor, uncertainty, and decision-ready context.

Core Features & Use Cases

  • Reproducible analysis from raw inputs with pinned environments and rerunnable notebooks so results can be verified end-to-end.
  • Dataset hygiene and statistical rigor including correct assumptions, effect sizes, confidence ranges, and multiple-comparisons handling for A/B tests and hypothesis tests.
  • Decision-ready outputs such as dashboarding with clear owners and refresh cadence, plus a decision memo that recommends action with confidence.

Quick Start

Ask: “Analyze whether onboarding v2 lifted d7 retention for the defined cohort, run the appropriate hypothesis test with the right multiple-comparisons correction, and produce a decision memo plus a dashboard plan with an owner and refresh cadence.”

Frequently Asked Questions about data-science-development

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

FAQPage Schema
How do I run reproducible data analysis with pinned environments in pandas or Polars?▼

Reproducible data analysis requires pinned environments and rerunnable notebooks so outputs can be verified end-to-end. This Skill enforces notebook reruns with documented statistical assumptions to turn raw datasets into trustworthy insights.

What is the best way to structure A/B testing analysis with proper statistical rigor?▼

A/B testing with statistical rigor requires correct assumptions, effect sizes, confidence ranges, and multiple-comparisons handling. This Skill applies these corrections to hypothesis tests and outputs a decision memo recommending action with confidence.

Can I use dbt-style versioned transformations with DuckDB or Spark for dashboarding?▼

Yes, dbt-style versioned transformations apply to DuckDB and Spark alongside pandas and Polars. This Skill processes raw datasets into decision-ready artifacts and wires dashboard ownership with defined refresh cadences.

Does this Skill support building dashboards with Streamlit that have clear refresh cadences?▼

Yes, Streamlit is supported for dashboarding outputs. This Skill ensures dashboards have clear owners and refresh cadence wiring, producing decision-ready artifacts backed by evidence-backed verification before marking the analysis done.

Why does my data analysis lack statistical rigor and reproducibility across reruns?▼

Data analysis lacks reproducibility when environments are not pinned and statistical conclusions lack proper rigor. This Skill requires notebook reruns with pinned environments, documented assumptions, and multiple-comparisons corrections to ensure trustworthy insights.

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