# regression-modeler

Fits OLS or logistic regression on your table and explains the coefficients in plain language. Handy when you need significance checks without writing the stats code yourself.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · ★ 4676
- Author: zebbern
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/zebbern/claude-code-guide/tree/HEAD/skills/regression-modeler/SKILL.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/skills/regression-modeler

## What it does

The skill runs `scripts/regression_analyzer.py` against a CSV, TSV, Excel or JSON file. Point `--target` at the dependent column and pass predictors with `--features` as a comma-separated list. Leave features out and every numeric column gets used.

Linear output covers coefficients, R² and adjusted R², the F-test, AIC/BIC and the Durbin — Watson statistic. Logistic output gives coefficients, odds ratios, pseudo-R² and a likelihood ratio test. VIF is computed per predictor with warning levels, so multicollinearity shows up right away. The default `--type auto` flips to logistic when the target holds only zeros and ones.

You need Python 3.8+ plus pandas, numpy, statsmodels and scipy. Rows with missing values are dropped unless you pass `--keep-na`, which is meant for debugging. Results go to stdout or to a JSON file via `--output`.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add zebbern/claude-code-guide --skill regression-modeler -a claude-code
```

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

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add zebbern/claude-code-guide --skill regression-modeler -a codex
```

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

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add zebbern/claude-code-guide --skill regression-modeler -a cursor
```

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

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install zebbern/claude-code-guide regression-modeler
```

2. **Manually: put the files in place**
- File: `.github/skills/regression-modeler/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add zebbern/claude-code-guide --skill regression-modeler -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/zebbern/claude-code-guide --path skills/regression-modeler
```

### Windsurf (Devin Desktop)

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

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add zebbern/claude-code-guide --skill regression-modeler -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/zebbern/claude-code-guide/tree/HEAD/skills/regression-modeler/SKILL.md

## When to use it
- fit a linear regression on a spreadsheet
- test predictor significance with p-values
- spot multicollinearity through VIF
- build a logistic model for a binary target
- get metric explanations in plain language

## Example prompts
- run a regression on data.csv with price as the target
- fit a logistic regression for churn using age, income, tenure
- save the regression results to result.json

## Source text · SKILL.md · MIT

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: a4bc6dacf9873f63.

BEGIN THIRD-PARTY FILE a4bc6dacf9873f63
````text
---
name: regression-modeler
description: "Run regression analysis (OLS or logistic) on uploaded CSV/Excel data, generating coefficients, R², p-values, VIF, and plain-language interpretation. Triggered by requests for regression modeling, fitting data, testing significance, checking multicollinearity, or keywords like OLS, logit, coefficient, p-value, or R-squared."
license: MIT
---

# regression-modeler

Automated regression modeling tool — performs linear regression (OLS) or logistic regression (Logit) on tabular data, producing comprehensive statistical results with plain-language interpretation.

## Capabilities

| Feature | Description |
|---------|-------------|
| Linear Regression | OLS with coefficients, R², adjusted R², F-test, AIC/BIC, Durbin-Watson |
| Logistic Regression | Logit with coefficients, Odds Ratio, Pseudo R², likelihood ratio test |
| Multicollinearity Detection | VIF values for each predictor with warning levels |
| Plain-Language Interpretation | Clear explanations of what each metric and coefficient means |
| Auto Detection | Automatically switches to logistic regression when the target is binary (0/1) |

## Quick Start

```bash
# Linear regression: predict price using all numeric columns as predictors
python3 scripts/regression_analyzer.py data.csv --target price

# Logistic regression: predict churn (0/1) with specified features
python3 scripts/regression_analyzer.py users.csv --target churn --features "age,income,tenure"

# Save results to JSON
python3 scripts/regression_analyzer.py data.csv --target sales --output result.json
```

## Detailed Usage

### Basic Invocation

```bash
python3 scripts/regression_analyzer.py <data_file> --target <target_column> [options]
```

### Specifying Regression Type

```bash
# Force linear regression
python3 scripts/regression_analyzer.py data.csv -t y --type linear

# Force logistic regression
python3 scripts/regression_analyzer.py data.csv -t label --type logistic

# Auto-detect (default)
python3 scripts/regression_analyzer.py data.csv -t y --type auto
```

### Selecting Feature Columns

```bash
# Manually specify (comma-separated)
python3 scripts/regression_analyzer.py data.csv -t price -f "sqft,bedrooms,bathrooms"

# Omit to automatically use all numeric columns
python3 scripts/regression_analyzer.py data.csv -t price
```

## Parameters

| Parameter | Short | Required | Default | Description |
|-----------|-------|----------|---------|-------------|
| `input` | — | Yes | — | Input file path (CSV/TSV/Excel/JSON) |
| `--target` | `-t` | Yes | — | Target variable (dependent variable) column name |
| `--features` | `-f` | No | All numeric columns | Predictor column names, comma-separated |
| `--type` | `-T` | No | `auto` | Regression type: `linear` / `logistic` / `auto` |
| `--output` | `-o` | No | stdout | Output JSON file path |
````
END THIRD-PARTY FILE a4bc6dacf9873f63

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