resend-email
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ibis-data
Use Ibis for database-agnostic data access in Python. Use when writing data queries, connecting to databases (DuckDB, PostgreSQL, SQLite), or building portable data pipelines that should work across backends.
13temporal-go
Build Temporal workflow applications in Go. Use when creating or modifying Temporal workflows, activities, workers, clients, signals, queries, updates, retry policies, saga patterns, or writing Temporal tests.
13binary-classification
Build a production-ready binary classifier on tabular data using XGBoost. Use when the user needs to predict a binary outcome from tabular features (churn, fraud, conversion, default, click). Covers class imbalance, threshold tuning, calibration verification, and SHAP feature importance. Default to this for any binary classification task on tabular data.
6bayesian-bandits
Implement Thompson sampling for multi-armed and contextual bandits. Use when the user wants to adaptively allocate traffic across variants (ads, recommendations, content, pricing) to minimize regret instead of running a fixed-allocation A/B test. Covers Bernoulli bandits, contextual bandits, regret analysis, and comparison with epsilon-greedy and UCB.
5bayesian-ab-testing
Run a Bayesian A/B test on conversion data using PyMC. Use when the user wants to compare two variants (landing pages, emails, pricing, UI changes) and decide which to ship using posterior probabilities and expected loss instead of p-values. Covers Beta-Binomial model, ROPE, expected loss, sample-size guidance, and ArviZ diagnostics.
5regression
Build a production-ready regression model on tabular data using XGBoost with conformalized quantile regression for prediction intervals. Use when the user needs to predict a continuous target from tabular features (price, sales, demand, time-to-event, score) and report uncertainty alongside the point estimate. Default to this for any tabular regression task.
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