claude-data-skills 0.1.0

A comprehensive collection of data science, analysis, and engineering skills and scripts.

Claude Data Skills 🐍📊

A professional-grade collection of data science, analysis, and engineering skills and scripts for AI-assisted development.

Overview

claude-data-skills is a comprehensive library designed to enhance AI-assisted data workflows. It provides a structured collection of "skills"—reusable, idiomatic patterns and scripts for everything from advanced standard library usage to complex machine learning pipelines.

Key Features

• 🚀 Professional Stdlib Usage: High-performance patterns for pathlib, itertools, collections, and contextlib.
• 🛡️ Data Safety First: Built-in guardrails to prevent accidental data loss or corruption during autonomous execution.
• 📊 Modern Visualization: First-class support for Plotly and Dash, ensuring interactive and high-quality data stories.
• 🧠 ML-Ready: Pre-configured patterns for PyTorch, Scikit-Learn, and Transformers.
• 📁 Unstructured Data Support: Advanced parsing for PDF, DOCX, XLSX, and binary formats.

Installation

Install the package directly from PyPI:

pip install claude-data-skills

Quick Start

Using the CLI

The package includes several built-in commands. For example, to run the standard library demonstration:

stdlib-demo

Importing Skills

You can import advanced utility patterns directly into your own scripts:

from skills.python_dev.python_stdlib_pro.scripts.stdlib_demo import test_pathlib

# Run a verified pathlib pattern
test_pathlib()

Core Principles

• Resource Aware: Every intensive task starts with hardware resource validation.
• LLM Optimized: Scripts are dense, idiomatic, and contain strict guardrails for local/open-source LLMs.
• Atomic Operations: Prevents file corruption by using temp-and-replace patterns for all writes.

Project Structure

skills/
├── data-analysis/          # Dask, Polars, Pandas, Geopandas
├── machine-learning/       # PyMC, PyTorch-Lightning, Scikit-Learn
├── python-dev/             # Stdlib Pro, Debugging, Refactoring
├── scientific-workflow/    # Scholar Evaluation, Visualization
└── unstructured-data/      # PDF, DOCX, XLSX, Binary

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Created and maintained by Yoni Kremer.