How do I install the Linear Regression skill?

Run npx skillmds@latest add windcleaverdev/linear-regression in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Linear Regression safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Linear Regression?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Linear Regression free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Linear Regression?

WindcleaverDev (@windcleaverdev) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Linear Regression by windcleaverdev

Name: Linear Regression
Author: WindcleaverDev

Linear Regression

Fit a linear regression (OLS) on tabular data and produce a rigorous report — coefficient table with confidence intervals, standardized betas, plain-English interpretation of each coefficient (transform-aware), fit statistics, and a standalone HTML report. Use this skill whenever the user wants to model a continuous outcome from one or more predictors, asks to "fit a regression" or "run OLS" or "model Y from X", or hands over tabular data with a continuous target. Outputs are both a structured JSON (LinearRegressionReport) and a self-contained HTML deliverable. Pair this skill with the diagnostics skill to check assumptions and identify influential observations.

WindcleaverDev 1ffca1e 7 files · 29.1 KB Updated Sep 17, 2026

File contents

linear-regression

Fits an OLS linear regression with statsmodels, produces a LinearRegressionReport JSON validating against the pack schema, and renders a standalone HTML report.

When this skill fires

User wants to fit a linear regression on tabular data
User has identified a continuous target and one or more predictors
User asks for OLS, multiple regression, or "model Y from X"
A previous skill (e.g. pre-analysis) recommended linear regression

Inputs

A CSV or Parquet file with the data
The target column name (must be numeric)
A list of predictor column names (numeric or categorical — categoricals are one-hot encoded with the first level dropped)

Optional:

--log-target to fit on log(target) — useful for skewed positive targets
--robust-se {HC0|HC1|HC2|HC3} for heteroscedasticity-robust standard errors
--standardize to also report standardized β coefficients

How to invoke

uv run python linear-regression/scripts/fit.py \
    --data path/to/data.csv \
    --target price \
    --features sqft,bedrooms,bathrooms,neighborhood \
    --output results/

Outputs results/report.json (LinearRegressionReport) and results/report.html.

Verbalising the output

The interpretations field contains a list of InterpretationFact objects. Each has:

fact — the canonical claim
confidence — high/medium/low based on p-value and CI width
caveats — list of qualifiers ("ceteris paribus", "not causal", scale notes)

Read the headline field aloud first, then walk through the top 2-3 interpretation facts ordered by absolute coefficient size, attaching caveats appropriate to the user's apparent sophistication. Do not invent statistics; only verbalise what's in the report.

Diagnostics

This skill performs the fit — it does not run the full assumption battery. After fitting, recommend the user run the diagnostics skill on the fitted model:

uv run python diagnostics/scripts/diagnose.py --fit-report results/report.json --data path/to/data.csv --output results/

Reference files

references/interpretation.md — when each interpretation_type applies and how to phrase it
references/robust_se.md — when to use each HC variant

---name: linear-regressiondescription: Fit a linear regression (OLS) on tabular data and produce a rigorous report — coefficient table with confidence intervals, standardized betas, plain-English interpretation of each coefficient (transform-aware), fit statistics, and a standalone HTML report. Use this skill whenever the user wants to model a continuous outcome from one or more predictors, asks to "fit a regression" or "run OLS" or "model Y from X", or hands over tabular data with a continuous target. Outputs are both a structured JSON (LinearRegressionReport) and a self-contained HTML deliverable. Pair this skill with the diagnostics skill to check assumptions and identify influential observations.---# linear-regressionFits an OLS linear regression with `statsmodels`, produces a `LinearRegressionReport` JSON validating against the pack schema, and renders a standalone HTML report.## When this skill fires- User wants to fit a linear regression on tabular data- User has identified a continuous target and one or more predictors- User asks for OLS, multiple regression, or "model Y from X"- A previous skill (e.g. pre-analysis) recommended linear regression## Inputs- A CSV or Parquet file with the data- The target column name (must be numeric)- A list of predictor column names (numeric or categorical — categoricals are one-hot encoded with the first level dropped)Optional:- `--log-target` to fit on log(target) — useful for skewed positive targets- `--robust-se {HC0|HC1|HC2|HC3}` for heteroscedasticity-robust standard errors- `--standardize` to also report standardized β coefficients## How to invoke```bashuv run python linear-regression/scripts/fit.py \ --data path/to/data.csv \ --target price \ --features sqft,bedrooms,bathrooms,neighborhood \ --output results/```Outputs `results/report.json` (LinearRegressionReport) and `results/report.html`.## Verbalising the outputThe `interpretations` field contains a list of InterpretationFact objects. Each has:- `fact` — the canonical claim- `confidence` — high/medium/low based on p-value and CI width- `caveats` — list of qualifiers ("ceteris paribus", "not causal", scale notes)Read the `headline` field aloud first, then walk through the top 2-3 interpretation facts ordered by absolute coefficient size, attaching caveats appropriate to the user's apparent sophistication. Do not invent statistics; only verbalise what's in the report.## DiagnosticsThis skill performs the *fit* — it does not run the full assumption battery. After fitting, recommend the user run the `diagnostics` skill on the fitted model:```bashuv run python diagnostics/scripts/diagnose.py --fit-report results/report.json --data path/to/data.csv --output results/```## Reference files- `references/interpretation.md` — when each interpretation_type applies and how to phrase it- `references/robust_se.md` — when to use each HC variant

Frequently asked questions