linear-regressionlisted

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/regkit · ★ 0 · Data & Documents · score 72

Install: claude install-skill WindcleaverDev/regkit

# 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 ```bash 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