Which libraries are supported by the Data Science Tools skill?

The skill provides documentation and best practices for the standard Python scientific stack, specifically NumPy, SciPy, Pandas, and Scikit-Learn.

Why does this skill enforce a specific script directory structure?

It mandates placing scripts in reports/{topic}/scripts/ to ensure reproducibility. This keeps the root directory clean and ensures that specific analysis code is permanently coupled with its generated report.

Can this skill help with non-linear regression?

Yes, it includes detailed patterns for using scipy.optimize.curve_fit, including advice on initial guesses (p0), parameter bounds, and convergence optimization.

How does this skill handle Python environments?

It provides standardized instructions for activating virtual environments and using shebangs to ensure scripts run correctly with the necessary data science dependencies.

Data Science Tools

Name: Data Science Tools
Author: mikkelkrogsholm

bymikkelkrogsholm

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Data Science & ML

Equips Claude with specialized knowledge for statistical analysis, regression modeling, and reproducible data science script organization.

This skill provides comprehensive guidance for performing advanced data analysis and machine learning tasks using the core Python scientific stack, including NumPy, SciPy, Pandas, and Scikit-Learn. It enforces strict best practices for script organization, ensuring that all analysis and modeling logic is contained within specific report directories to maintain project reproducibility and clean root structures. Whether you are fitting non-linear regression models using curve_fit, performing statistical hypothesis tests, or managing complex data transformations, this skill provides the implementation patterns and structural rules required for professional data science workflows.

Key Features

01Enforced script organization rules for reproducible reporting (reports/{topic}/scripts/)

02Automated model comparison and validation workflows

031 GitHub stars

04Non-linear regression modeling templates using scipy.optimize.curve_fit

05Guidance for virtual environment management and script execution in data pipelines

06Standardized implementation patterns for SciPy, NumPy, Pandas, and Scikit-Learn

Use Cases

01Performing statistical validation and hypothesis testing on processed datasets

02Organizing complex data analysis projects into isolated, modular reports

03Fitting logistic S-curves or exponential growth models to historical trend data

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add mikkelkrogsholm/dst-skills data-science-tools

For use in Claude.ai and ChatGPT

Download Skill