Data Science Expert

This is a solid foundation for data science work in Claude, covering EDA, feature engineering, and time series analysis with practical Python classes you can actually use. The DataCleaner and FeatureEngineer implementations handle the tedious parts of preprocessing with sensible defaults for missing values, outliers, and encoding. You get correlation heatmaps, distribution plots, and polynomial feature generation out of the box. It's built around pandas, numpy, scikit-learn, and statsmodels, so if you're already working in that stack, this integrates naturally. The code is more educational than production-hardened, but it's a good starting point when you need to spin up an analysis quickly without writing boilerplate from scratch.

skills.sh installs: 583
GitHub stars: 44
last updated: Aug 21, 2026

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Install

$ npx -y skills add personamanagmentlayer/pcl --skill data-science-expert --agent claude-code

Installs into .claude/skills of the current project.

Files

Data Science Expert

Expert guidance for data science, analytics, statistical modeling, and data visualization.

Core Concepts

Data Analysis

Exploratory Data Analysis (EDA)
Data cleaning and preprocessing
Feature engineering
Statistical inference
Time series analysis
A/B testing

Machine Learning

Supervised learning (classification, regression)
Unsupervised learning (clustering, PCA)
Model selection and validation
Feature importance
Hyperparameter tuning
Ensemble methods

Data Visualization

Matplotlib, Seaborn, Plotly
Statistical plots
Interactive dashboards
Storytelling with data
Best practices for visualization
Color theory and accessibility

Data Cleaning and EDA

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from typing import Dict, List

class DataCleaner:
    """Clean and preprocess data"""

    def __init__(self, df: pd.DataFrame):
        self.df = df.copy()
        self.cleaning_log = []

SKILL.md on GitHub