balintdecsi/skills

designing-analytics-projects

Suggestions for scoping and writing an Analytics Project Brief — the one-page artifact that defines problem, metrics, counter-metrics, stakeholders, methodology, success criteria, and pre-mortem before any analysis begins. Use when the task is to draft,…

原文语言：英语

ml-modeling

Best-practice suggestions for predictive ML modelling with scikit-learn — Pipelines, ColumnTransformer, cross-validation, hyperparameter search, honest train/test evaluation, model leaderboards, and threshold/loss-based decisions. Use when building, comparing…

原文语言：英语

statistical-modeling

Best-practice suggestions for statistical / inferential modelling in Python — OLS and logistic regression with statsmodels, robust standard errors, side-by-side regression tables with stargazer, confidence and prediction intervals, hypothesis tests, and…

原文语言：英语

time-series-forecasting

Best-practice suggestions for time series exploration and forecasting in Python — datetime indexing, resampling, temporal train/test splits, decomposition, ACF/PACF, stationarity checks, ARIMA/SARIMA/SARIMAX, AutoGluon TimeSeriesPredictor, backtesting,…

原文语言：英语

data-warehousing

Best practices for designing data warehouses and analytical pipelines using the bronze/silver/gold medallion architecture, validations-as-code, and idempotent transforms. Use when building or modifying data pipelines, ETL/ELT jobs, dbt models, SQL warehouses,…

原文语言：英语

analytics-project-setup

Technical setup skill for analytics and data science projects — repository scaffolding, folder structure (dev/prod split, data layers, numbered notebooks), environment management (uv, venv, dotenv), pre-commit hooks for notebook output clearing, branching and…

原文语言：英语

code-review

AI-powered code review using CodeRabbit. Default code-review skill. Trigger for any explicit review request AND autonomously when the agent thinks a review is needed (code/PR/quality/security).

原文语言：英语