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WindcleaverDevWindcleaverDev/regkit/.agents/skills/linear-regression/SKILL.md

linear-regression

Skill0 starsChanged 5 months agoMITView on GitHub

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.

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    1. muralidharsoth-tech/andrew-ng-ml-claude-skills★ 40same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

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The file

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  1. Very specific:

    Read the headline field aloud first, then walk through the top 2-3 interpretation facts ordered by absolute coefficient size, attaching …

    line 47

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Files
SKILL.md

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

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