statsmodels

Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.

1.09x

Quality

75%

Does it follow best practices?

Impact

93%

1.09x

Average score across 6 eval scenarios

Securityby

Passed

No known issues

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npx tessl skill review --optimize ./scientific-skills/statsmodels/SKILL.md

Evaluation results

99%

Employee Compensation Analysis

OLS diagnostics and robust inference

Criteria

Without context

With context

Constant added

100%

Breusch-Pagan test

100%

Robust SEs applied

100%

VIF computed

100%

VIF threshold applied

87%

Cook's distance computed

100%

Cook's threshold used

100%

Confidence intervals reported

100%

Summary output captured

100%

Model comparison AIC/BIC

100%

96%

Software Defect Rate Analysis

Count model selection and overdispersion handling

Criteria

Without context

With context

Poisson model first

100%

60%

Constant added

50%

100%

Overdispersion computed

100%

Overdispersion threshold applied

30%

100%

Negative Binomial fitted

100%

Rate ratios reported

100%

AIC/BIC comparison

100%

Model selection justified

100%

Zero-inflation considered

100%

Results summary written

100%

Retail Store Monthly Sales Forecasting

ARIMA time series modeling and forecasting

Criteria

Without context

With context

ADF stationarity test

100%

Non-stationarity handled

100%

ACF/PACF for order selection

100%

ARIMA or SARIMAX fitted

100%

Seasonal model used

100%

Forecast with confidence intervals

91%

100%

Forecast CSV contains CI columns

100%

Ljung-Box residual test

100%

Residual adequacy stated

100%

Model summary written

100%

80%

38%

Predicting Customer Loan Default

Binary logistic regression workflow

Criteria

Without context

With context

Logit model used

100%

Constant added

100%

Odds ratios computed

58%

100%

Odds ratio CIs reported

62%

Marginal effects computed

28%

35%

McFadden Pseudo R-squared

100%

AUC-ROC reported

100%

results.summary() used

Significance noted

100%

Predicted probabilities

100%

96%

Modeling Healthcare Claim Costs

Gamma GLM for positive skewed outcomes

Criteria

Without context

With context

Gamma family used

100%

Log link specified

100%

Constant added

75%

Multiplicative effects computed

100%

Multiplicative CIs reported

100%

Pseudo R-squared reported

100%

AIC/BIC model comparison

100%

80%

Distribution explored

100%

Significance noted

100%

results.summary() used

100%

92%

Macroeconomic Interdependencies Analysis

VAR model and Granger causality

Criteria

Without context

With context

VAR class used

100%

DataFrame input

100%

Stationarity tested

100%

Lag order via select_order

100%

Lag order reported

100%

Granger causality tested

100%

IRF computed

100%

Forecasts generated

100%

results.summary() used

Repository: K-Dense-AI/claude-scientific-skills
Commit: 899a51b

Evaluated: about 1 month ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Employee Compensation Analysis Software Defect Rate Analysis Retail Store Monthly Sales Forecasting Predicting Customer Loan Default Modeling Healthcare Claim Costs Macroeconomic Interdependencies Analysis

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