Regression modeler
Полная коллекция скиллов Kimi (267 built-in + 7 plugin skills), выгруженная из сандбокса агента
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What its author says it does
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Run regression analysis (OLS or logistic) on uploaded CSV/Excel data, generating coefficients, R², p-values, VIF, and plain-language interpretation. Triggered by requests for regression modeling, fitting data, testing significance, checking multicollinearity, or keywords like OLS, logit, coefficient, p-value, or R-squared.
The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
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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) |
Output Structure (JSON)
{
"type": "linear",
"r_squared": 0.8523,
"r_squared_adj": 0.8471,
"f_statistic": 162.34,
"f_p_value": 0.0,
"coefficients": {
"sqft": {"coefficient": 135.42, "p_value": 0.0001, ...},
"bedrooms": {"coefficient": 8021.5, "p_value": 0.032, ...}
},
"vif": {"sqft": 2.31, "bedrooms": 1.87},
"interpretation": {
"model_summary": ["R² = 0.8523 (good model fit...)"],
"variable_analysis": ["sqft: coefficient = 135.42... positive effect..."]
}
}
Dependencies
- Python 3.8+
- pandas
- numpy
- statsmodels
- scipy
pip install pandas numpy statsmodels scipy
Gives 0 of the 12 instructions most data analysis skills give
Counted across 286 of the 286 authors here whose files we hold, read 2026-08-06
- use excel formulas instead of hardcoded calculated valuesin 35 of 286, across 7 files
- match existing template conventions when modifying filesin 35 of 286, across 7 files
- document sources for all hardcoded valuesin 35 of 286, across 7 files
- write minimal concise python codein 35 of 286, across 7 files
- place all assumptions in separate assumption cellsin 32 of 286, across 5 files
- apply industry-standard color coding to financial modelsin 31 of 286, across 5 files
- format years as text stringsin 30 of 286, across 3 files
- recalculate formulas using recalc.py after modificationsin 30 of 286, across 3 files
- format negative numbers using parenthesesin 30 of 286, across 3 files
- fix all identified formula errors before finishingin 27 of 286, across 1 file
- use colorblind-safe palettesin 19 of 286, across 12 files
- Name tests after the prevented bugin 13 of 286, across 8 files
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.