Pre analysis

Skill WindcleaverDev/regkit/.agents/skills/pre-analysis

Statistically rigorous regression skills for Claude — Python computes, the model narrates.

Install

npx -y skills add WindcleaverDev/regkit --skill pre-analysis

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One thing to look at

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Audit tabular data before fitting any regression — target distribution and skewness, feature types and missingness, multicollinearity (pairwise correlation + VIF), suspected nonlinearity, univariate outliers, and concrete modeling recommendations (which transforms to apply, which features to drop or combine, which estimator family to use). Produces a structured PreAnalysisReport and a standalone HTML report. Use this skill whenever the user is about to fit a model and hasn't done EDA yet, asks "what should I do with this data?", "should I transform anything?", "is this data ready to model?", or hands over a CSV with a target in mind but no clear modeling plan. Run this BEFORE linear-regression or logistic-regression to avoid refit cycles.

SKILL.md

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pre-analysis

Audits tabular data and recommends modeling choices before any fit. Produces a PreAnalysisReport JSON validating against the pack schema and a standalone HTML report.

When this skill fires

User has tabular data + identified target, but hasn't fitted yet
User asks for EDA, data audit, "what should I do with this data?"
User asks whether to transform variables before modeling
Before any fit skill — especially before linear-regression if heteroscedasticity or skew is plausible

Inputs

--data <path> — CSV or Parquet
--target <column> — target variable (continuous, binary, or count)
--features <a,b,c> — comma-separated predictor columns, or all to use every non-target column
--output <dir> — output directory

How to invoke

uv run python .agents/skills/pre-analysis/scripts/audit.py \
    --data path/to/data.csv \
    --target price \
    --features all \
    --output results/

Outputs results/pre_analysis.json (PreAnalysisReport) and results/pre_analysis.html.

Verbalising the output

Read the warning block first (flags field, severity HIGH first). Then walk through modeling_recommendations — these are the concrete actions the user should take before fitting. Do not skip the recommendations to dive into raw distributions; the recommendations are the deliverable.

If target.recommendations includes log_transform, suggest passing --log-target to the subsequent fit skill. If multicollinearity.flagged is non-empty, suggest either dropping features or using regularized-regression with ridge.

Chaining

The typical next step is one of:

linear-regression (continuous target, OK assumptions) — pass any recommended transforms
logistic-regression (binary target)
regularized-regression (multicollinearity flagged)

Reference files

references/transformations.md — when to log/sqrt/Box-Cox/Yeo-Johnson
references/encoding.md — categorical encoding choices
references/vif_remediation.md — what to do about multicollinearity