regression-modeler

regression-modeler is a skill for Claude Code, Codex from zebbern/claude-code-guide. It costs 76 tokens per session (951 once invoked), scanned A, original, MIT.

A statistics tool that fits linear or logistic regression models to CSV or Excel data and explains the results in plain language. Regression estimates how one or more input factors relate to an outcome.

In plain words
What is it for?
Use it to model numeric outcomes such as sales or prices, predict binary outcomes such as churn, and review coefficients, goodness of fit, p-values, and multicollinearity.
Why use it?
It avoids doing the calculations and statistical checks by hand, including significance tests and detecting inputs that overlap too much.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/zebbern/claude-code-guide/regression-modeler
Any agent
npx skills add zebbern/claude-code-guide --skill regression-modeler
Clone the repo
git clone --depth 1 https://github.com/zebbern/claude-code-guide

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for regression-modeler

README.md
[![agentmods](https://agentmods.dev/badge/skills/zebbern/claude-code-guide/regression-modeler.svg)](https://agentmods.dev/skills/zebbern/claude-code-guide/regression-modeler)
Your own site
<a href="https://agentmods.dev/skills/zebbern/claude-code-guide/regression-modeler"><img src="https://agentmods.dev/badge/skills/zebbern/claude-code-guide/regression-modeler.svg" alt="Measured on agentmods" height="20"></a>
Per session 76 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 951 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00076 $0.00951
Opus 5 $0.00038 $0.00476
Sonnet 5 $0.00015 $0.00190
Haiku 4.5 $0.00008 $0.00095

Measured yesterday against content hash 28e6d859f1c6, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

regression-modeler scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/regression_analyzer.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/regression-modeler/SKILL.md · 109 lines

How it starts

The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

# 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

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

Specifying Regression Type

# 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

# 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
--no-const No false Do not add an intercept term
--keep-na No false Keep rows with missing values (for debugging)

Read the full file on GitHub · 109 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 109 lines · 76 tokens per session scan A 28e6d859f1c6

Subscribe to this mod's changes

regression-modeler is a skill published in the GitHub repository zebbern/claude-code-guide (4,600 stars, last pushed yesterday), licensed MIT. It adds 76 tokens to every session and 951 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.