python_data_stack
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
npx skills add majiayu000/claude-skill-registry --skill python-data-stack-vuralserhat86-antigravity-agentic-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Provides a suite of statistical modeling capabilities for regression (OLS, WLS, GLS, GLSAR, Quantile Regression, Mixed Effects), generalized linear models (Binomial, Poisson, Negative Binomial, Gamma, Tweedie, etc.), discrete choice models (Logit, Probit, MNLogit, etc.), time series analysis (ARIMA, SARIMAX, VAR, etc.), and extensive tests/diagnostics for model validation.
How it works
- It demonstrates usage patterns for common models, including data preparation steps (e.g., adding a constant), fitting models, and extracting results like summaries, coefficients, and predictions.
- It shows computing odds ratios, predicted probabilities, marginal effects, and various goodness-of-fit and diagnostic metrics.
- It includes example code blocks for OLS, Logistic Regression, ARIMA, and GLM workflows, plus a detailed Core Statistical Modeling section outlining model types, features, and when to use them.
- It provides a Formula API section for R-style specifications and illustrates model selection via AIC/BIC and Likelihood Ratio Tests, along with cross-validation snippets.
- It lists best practices for data preparation, model building, inference, evaluation, and reporting, plus common workflows for linear, binary, count, and time-series analyses.
When to use it
Use this skill when you need to fit regression models (OLS, WLS, GLS, quantile, mixed effects), run generalized linear models for non-normal outcomes, analyze discrete outcomes with Logit/Probit, perform time-series modeling (ARIMA/SARIMAX/VAR), conduct extensive tests and diagnostics, and produce publication-ready statistical tables.
What it can touch
- Code blocks import and utilize: "statsmodels.api as sm", "from statsmodels.discrete.discrete_model import Logit", "from statsmodels.tsa.arima.model import ARIMA", "statsmodels.genmod.generalized_linear_model as sm" and related APIs inside the examples.
- No external tool calls beyond Python statistical modeling libraries shown in examples.
Caveats
- The material is example-driven and references external modules and references (e.g., AIC/BIC, LR tests) without asserting fixed outcomes beyond the shown results.
- No licensing text beyond general usage; the snippet sections imply usage with common Python statistical libraries.
# Statsmodels: Statistical Modeling and Econometrics ## Overview Statsmodels is Python's premier library for statistical modeling, providing tools for estimation, inference, and diagnostics across a wide range of statistical methods. Apply this skill for rigorous statistical analysis, from simple linear regression to complex time series models and econometric analyses. ## When to Use This Skill This skill should be used when: - Fitting regression models (OLS, WLS, GLS, quantile regression) - Performing generalized linear modeling (logistic, Poisson, Gamma, etc.) - Analyzing discrete outcomes (binary, multinomial, count, ordinal) - Conducting time series analysis (ARIMA, SARIMAX, VAR, forecasting) - Running statistical tests and diagnostics - Testing model assumptions (heteroskedasticity, autocorrelation, normality) - Detecting outliers and influential observations - Comparing models (AIC/BIC, likelihood ratio tests) - Estimating causal effects - Producing publication-ready statistical tables and inference ## Quick Start Guide ### Linear Regression (OLS) ```python import statsmodels.api as sm import numpy as np import pandas as pd # Prepare data - ALWAYS add constant for intercept X
- Overview
- When to Use This Skill
- Quick Start Guide
- Linear Regression (OLS)
- Logistic Regression (Binary Outcomes)
- Time Series (ARIMA)
- Generalized Linear Models (GLM)
- Core Statistical Modeling Capabilities
- 1. Linear Regression Models
- 2. Generalized Linear Models (GLM)
- 3. Discrete Choice Models
- 4. Time Series Analysis
- 5. Statistical Tests and Diagnostics
- Formula API (R-style)
Find information about specific models grep -r "Quantile Regression" references/ Find diagnostic tests grep -r "Breusch-Pagan" references/stats_diagnostics.md Find time series guidance grep -r "SARIMAX" references/time_series.md
What does the python_data_stack skill do?
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill python-data-stack-vuralserhat86-antigravity-agentic-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.