# data-science-expert

A skill for analytics, statistics and visualization that kicks in when a task mentions pandas, NumPy or exploratory data analysis.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 573 installs · ★ 43
- Author: personamanagmentlayer
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/personamanagmentlayer/pcl/tree/HEAD/stdlib/ai/data-science-expert/SKILL.md
- Updated: 2026-10-12
- HTML: https://affpapa.org/en/nevibecode/skills/data-science-expert

## What it does

The skill hands the agent ready-made patterns for data work: a `DataCleaner` class for missing values, duplicates and outliers, an `EDA` class for summary stats, correlations and distribution plots, and a `FeatureEngineer` for encoding, scaling and polynomial features. Under the hood it is pandas, NumPy, Matplotlib, Seaborn and scikit-learn code the agent reuses instead of writing from scratch.

It triggers on its own when a prompt mentions analytics, visualization, statistics, pandas or NumPy, or when the task touches machine learning and feature engineering. Allowed tools are `Read`, `Write`, `Edit` and `Bash(python:*)`, so the agent can run code and inspect the output.

One caveat: the z-score outlier example calls `stats.zscore`, yet `scipy.stats` is never imported in that snippet. That path breaks until you add the import yourself. The rest runs on pandas and NumPy alone.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add personamanagmentlayer/pcl --skill data-science-expert -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/data-science-expert/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/data-science-expert/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add personamanagmentlayer/pcl --skill data-science-expert -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/data-science-expert/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add personamanagmentlayer/pcl --skill data-science-expert -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/data-science-expert/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install personamanagmentlayer/pcl data-science-expert
```

2. **Manually: put the files in place**
- File: `.github/skills/data-science-expert/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add personamanagmentlayer/pcl --skill data-science-expert -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/personamanagmentlayer/pcl --path stdlib/ai/data-science-expert
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/data-science-expert/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add personamanagmentlayer/pcl --skill data-science-expert -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/personamanagmentlayer/pcl/tree/HEAD/stdlib/ai/data-science-expert/SKILL.md

## When to use it
- handle missing values and duplicates before training a model
- compute a correlation matrix and plot it as a heatmap
- encode categorical columns and scale numeric ones
- select features with SelectKBest and mutual information
- lay out a grid of distributions for numeric columns

## Example prompts
- clean the dataset of outliers with the IQR method and tell me how many rows are left
- run EDA on the sales table: summary stats and a correlation matrix
- one-hot encode the categorical features and scale the numeric ones with StandardScaler

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 38740395da8683f4.

BEGIN THIRD-PARTY FILE 38740395da8683f4
````text
---
name: data-science-expert
version: 1.1.0
description: >-
  Expert-level data science, analytics, visualization, and statistical modeling. Use when
  the user mentions analytics, visualization, statistics, pandas, or NumPy, or when the
  task involves Data Analysis, Machine Learning, Data Visualization, or Feature
  Engineering.
category: ai
tags: [data-science, analytics, visualization, statistics, pandas, numpy]
allowed-tools:
  - Read
  - Write
  - Edit
  - Bash(python:*)
---

# 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

```python
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 = []

    def handle_missing_values(self, strategy: str = 'drop',
                             fill_value=None) -> pd.DataFrame:
        """Handle missing values"""
        missing_before = self.df.isnull().sum().sum()

        if strategy == 'drop':
            self.df = self.df.dropna()
        elif strategy == 'fill':
            if fill_value is not None:
                self.df = self.df.fillna(fill_value)
            else:
                # Fill numeric with median, categorical with mode
                for col in self.df.columns:
                    if self.df[col].dtype in ['float64', 'int64']:
                        self.df[col].fillna(self.df[col].median(), inplace=True)
                    else:
                        self.df[col].fillna(self.df[col].mode()[0], inplace=True)

        missing_after = self.df.isnull().sum().sum()
        self.cleaning_log.append(f"Missing values: {missing_before} -> {missing_after}")

        return self.df

    def remove_duplicates(self) -> pd.DataFrame:
        """Remove duplicate rows"""
        before = len(self.df)
        self.df = self.df.drop_duplicates()
        after = len(self.df)
````
END THIRD-PARTY FILE 38740395da8683f4

The third-party file has ended. Everything between the 38740395da8683f4 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

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---
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